SYSTEM AND METHOD FOR MEASURING AND ANALYZING SWING KINEMATICS AND MATCHING SWING KINEMATICS TO OPTIMIZED SWING EQUIPMENT - Patent application

Abstract

A system and method for analyzing a swing motion of a sports equipment, such as a golf club, including at least one or more inertial acceleration sensors, one or more gyrometric sensors, a data acquisition unit, a core microcontroller, and a Bluetooth radio. The motion detection unit detects at least one motion of the swing. In particular, the sensor and the data acquisition unit use the acquired detection data to calculate swing information to match the swing motion of a user with an optimized equipment. In particular, the data acquisition unit acquires the detection data from the sensor and transfers such data to a computing device or server that stores information matching the swing motion with an optimized swing device.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 306,309, filed February 3, 2022, the disclosure of which is incorporated by reference.

[0002] FIELD OF THEINVENTION

[0002] The present invention encompasses a system and method for analyzing an individual's motion in training with a sports equipment, e.g., a golf club, and / or matching a sports equipment, e.g., a golf club, to an individual. The system and method include one or more inertial acceleration sensors, one or more gyrometric sensors, a data acquisition unit, a core microcontroller, and a Bluetooth radio. The motion detection unit detects at least one motion, e.g., a swing, by measuring and collecting various data related to, e.g., acceleration, torque, and sonification. In particular, the sensor and the data acquisition unit calculate information related to the motion, and use the acquired data to match the user's swing motion to an optimized equipment or an optimized training paradigm. In particular, the data acquisition unit acquires the detection data from the sensor and transfers it to a computing device or a server that stores information matching the motion to the optimized equipment or training protocol. [Background technology]

[0003] 2. Background of the Invention

[0003] Methods for analyzing the motion of an object generally use one of two approaches: motion sensors are attached to the object and sensor data is collected and analyzed, or stationary devices such as video cameras or radars are configured to observe the moving object. For example, motion sensors may include inertial sensors that capture acceleration and gyroscope data, which are then integrated to measure the trajectory of the object. Video analysis of motion may include traditional motion capture systems using spectral reflective markers or more advanced methods of locating and tracking objects without markers using image processing.

[0004]

[0004] Motion analysis methods based on a single type of sensor (such as a motion sensor, video camera, or radar) have limitations. For example, current inertial sensor-based methods are typically not very accurate over long time frames. On the other hand, video-based methods can lose objects due to occlusion.

[0005]

[0005] Known motion capture systems typically utilize a number of passive or active markers or a number of sensors. The data is typically analyzed in a lab on a per user or per swing basis, is not used for any purpose other than motion analysis or representation of that particular user's motion, and is generally not subject to data mining.

[0006]

[0006] There are no known systems in which motion capture elements, such as wireless sensors, can be seamlessly integrated or otherwise coupled to optimized equipment, such as baseball bats, tennis rackets, golf clubs, or hockey sticks, in a compact format such that the user is unaware that sensors are located in or on the item. There are no known systems that provide a seamless mount configured to capture motion data and then wirelessly transfer such motion data to a server database that contains data matching such motion data to specific equipment or training protocols optimized for the performance of an individual, such as an athlete.

[0007]

[0007] Data derived from existing sensors for many events is not stored in a database and is not used for anything other than the performance for which the motion capture data was obtained.

[0008]

[0008] There are no known systems that allow for data mining of large numbers of individuals associated with athletic activities or associated equipment to find patterns in the data that allow business strategies to be determined based on previously undiscovered patterns associated with the activity. Existing systems further do not have the ability to provide real-time feedback and comparison with stored data sets to optimize equipment selection. Summary of the Invention [Problem to be solved by the invention]

[0009]

[0009] Due to at least the above limitations, there is a need for a sensor-enabled motion analysis system and method that matches the dynamic motion information detected using the sensor system of the present invention with a particular "idealized" motion information information, such as a database containing information stored regarding sports equipment, to match a user's swing with optimal sports equipment. [Means for solving the problem]

[0010] Summary of the Invention

[0010] In various embodiments, the present invention encompasses systems and methods that match data collected from a sensor device including one or more sensors with stored data to match a swing profile to a preferred or optimized swing equipment (e.g., a golf club or bat).

[0011]

[0011] In one embodiment, the present invention encompasses a system or method with a sensor device to measure and analyze a swing and obtain data about the swing, the system or method comprising:

[0012] a) a core microcontroller processor and radio;

[0013] b) one or more low inertial acceleration and / or gyrometric sensors;

[0014] c) one or more magnetometer sensors; and

[0015] d) Batteries and Includes.

[0012]

[0016] In certain embodiments, the sensors collect data including data on acceleration, swing motion, torque, speed, and impact. In certain embodiments, data collected using the sensors is transmitted (e.g., remotely) to a computing device or server, where such data is matched with stored data including information corresponding to an optimized swing device. For example, when a user swings an object that includes a sensor device attached to the object, using data obtained from the sensors, the computing device or server then matches the user's swing motion with stored information corresponding to a swing implement that best suits or is "ideal" for the user's optimal performance. This information is optionally graphed to show an idealized motion curve compared to an individual's actual motion (see, e.g., FIG. 7).

[0013]

[0017] In another embodiment, the invention encompasses a swing measurement and analysis system or method including a sensor device, the system or method comprising:

[0018] a) a core microcontroller and a Bluetooth Low Energy (BLE) radio;

[0019] b) a first triad low inertia acceleration and rate gyrometric sensor;

[0020] c) a second triad high inertia acceleration and rate gyrometric sensor;

[0021] d) a triad magnetometer sensor; and

[0022] e) Batteries and battery monitors Includes.

[0014]

[0023] In another embodiment, the invention encompasses a swing measurement and analysis system or method including a sensor device, the system or method comprising:

[0024] a) a core microcontroller and a Bluetooth Low Energy (BLE) radio;

[0025] b) a first triad low inertia acceleration and rate gyrometric sensor;

[0026] c) a second triad high inertia acceleration and rate gyrometric sensor;

[0027] d) a triad magnetometer sensor; and

[0028] e) persistent storage for logging and storing, for example, 200 swings;

[0029] f) a Qi wireless charging controller;

[0030] g) a battery monitor; Includes.

[0015]

[0031] In certain embodiments, the systems and methods include an interface with antenna designs (eg, from TaoGlas) that can also perform performance tuning and pre-compliance validation.

[0016]

[0032] In certain embodiments, a system includes a printed circuit board assembly.

[0017]

[0033] In another embodiment, the system includes one or more inertial measurement units (IMUs) and a low power device running on a battery.

[0018]

[0034] In a particular embodiment, the system includes a user interface over Bluetooth Low Energy (BLE), whereby: a. receiving data from a live session; and b. Capturing inertial data (when connected with BLE) a. Uses a high G sensor to measure ball impact, b. Using low G sensors to record swing movements capturing inertial data by Do the following: c. On-board processing is required, which a. Detect when the club hits the ball (end of swing), b. storing the live session data in volatile memory (e.g., RAM); c. Communicate via BLE i. reporting live inertial data about the last detected swing; ii. Expose the battery level; The system optionally further comprises: d.Qi charging function, e. Persistent storage of session logs; f. Generation mode (standby mode, sleep mode, on mode), and g. BLE custom service for accessing logs Includes.

[0019]

[0035] In various embodiments, the present invention encompasses golf swing analysis systems and methods, including golf swing analysis methods and programs that use measurement devices including the sensors of the present invention to enable modeling of the swing motion of a golf club, matching the swing motion to an ideal or optimal motion.

[0020]

[0036] In another embodiment, the present invention encompasses a golf club fitting device and program that specifies an optimal swing profile and, accordingly, specifies an optimal golf club, shoe, or ball.

[0021]

[0037] In various embodiments, the systems and methods of the present invention can be used to analyze an individual's motion, generate a profile that matches the individual's "actual" motion with an "ideal" profile of the individual's motion, and use the profile accordingly to match the motion with desired equipment for a particular activity. For example, the systems and methods can be used to generate a motion profile based on an individual's motion swinging an object, including, but not limited to, a golf club, a bat, a hockey stick, a lacrosse stick, a field hockey stick, a fishing rod, and can also be used to match an individual with optimal sports shoes, golf balls, and related equipment depending on the sport or activity that is desired to be analyzed.

[0022]

[0038] In certain embodiments, the present invention encompasses systems and methods for swing analysis (e.g., golf swing) for analyzing, for example, a swing motion of a golf club, including an acquisition device configured to acquire one or more measurements acquired by measuring the swing motion using the acquisition device based on, for example, torque, sonification, and other measurement data acquired during the motion (e.g., swing motion) based on the behavior of the motion (e.g., swing).

[0023]

[0039] In various embodiments, the swing motion is analyzed based on data collected from devices that include various sensors, including one or more gyrometers or accelerometers, as described herein. Thus, the swing motion of a golf club can be modeled using values ​​measured by measurement devices attached to the object in motion.

[0024]

[0040] In certain embodiments, the device is configured to measure or calculate angular velocity based on the swing motion. In certain embodiments, the device is configured to measure or calculate torque based on the swing motion. In certain embodiments, the device is configured to measure or calculate sonification based on the swing motion.

[0025]

[0041] In an embodiment of the invention, the subject's movements while performing a swing are measured or detected by a system or device of the invention, and the measurements are used to provide values ​​for a number of parameters, which are analyzed and compared to "ideal" target values ​​for those parameters. There are a variety of methods known in the art that can be used to measure the body and club movements associated with a golf swing.

[0026]

[0042] The use of motion analysis provides precise measurements from which values ​​of multiple parameters can be calculated, from which the evaluator can pinpoint problems in a subject's swing.

[0027]

[0043] Motion tracking or motion capture in some embodiments is accomplished using a device of the invention, for example, a subject attaches the device to an object (e.g., a golf club) that is to be swung and identifies the movement by its position or angle during the swing. One or more acoustic, inertial, magnetic, accelerometer, gyrometer, or other sensors, or combinations thereof, are included in the device and used to sense and analyze data, optimally at least twice during the frequency rate of the desired movement. The device typically generates data with at least three degrees of freedom.

[0028]

[0044] In various embodiments, inertial motion capture technology is based on micro- or nano-sized inertial sensors, biomechanical models, and sensor fusion algorithms. The motion data of the inertial sensors (inertial guidance systems) is wirelessly transmitted to a computer where the motion is recorded or displayed. In certain embodiments, the inertial system uses gyroscopes to measure the rate of rotation. In certain embodiments, the more gyroscopes, the more natural the data. No external cameras, emitters, or markers are required for relative motion. The inertial system captures the body motion of the highest six degrees of freedom of a human in real time, and can optionally provide directional information if it includes magnetic sensors. Advantages of using an inertial system include no resolution, portability, and large capture area.

[0029]

[0045] In certain embodiments, magnetic systems can calculate the position and orientation of the swing. The relative strength of the voltage or current allows these systems to calculate both range and orientation by mapping a tracking volume. The sensor output has six degrees of freedom. The sensor response is non-linear, especially towards the edges of the capture area.

[0030]

[0046] In addition to measuring body movement, forces can be measured as well. One method of measuring forces involves using a suitably programmed computer to model the forces within the body by attributing mass and moments of inertia to the body segments and the club, and deriving a model using the body and club motions measured by the device. The computer analyzes the motions and identifies the associated forces (e.g. torques) and motions (e.g.).

[0031]

[0047] The main components of one embodiment of the device of the present invention include accelerometer, sonification, gyrometric, and inertial sensors that collect data by attaching the device to a golf club, and the sensors communicate by wireless means with a transceiver that is part of a computer-based motion analysis system that includes control and display capabilities, such as a laptop computer, with suitable application software and an on-board or connected database. Other sensory devices and other sensors are also connected to the system by wires or wirelessly (e.g., Bluetooth). The system processes the motion data and generates, displays, and / or transmits reports, graphs, and / or feedback, and can also provide prescriptions, as described in more detail below. Training equipment may or may not be directly linked to the motion analysis system or other related components, but may be used by the evaluator or subject during the performance of exercises prescribed by the system after testing and analysis.

[0032]

[0048] As described with respect to the motion capture system of the present invention, devices used in measuring the movements that can be used to calculate the values ​​of various parameters include sensors placed on desired parts of the object. These sensors optionally include, but are not limited to, one or more reflective or illuminated markers; gyroscopes to measure angular velocity; accelerometers to measure tilt or linear acceleration; magnetometers to measure magnetic field strength or direction; electrodes to measure changes in electrical potential (e.g., EEG monitors), muscle activity, or heart rate; pressure, force, or torque sensors to measure kinematic interactions; cameras coupled with or without markers to measure the position of body segments or objects; force platform systems to measure ground reaction forces and moments; goniometers to measure joint angles; speed sensors to measure speed; and temperature sensors to measure body part or ambient heat. The sensors are optionally connected to a processor or a network of processors that acquires the movement data during the test session by sampling and storing signals from the sensors at a predefined sampling frequency.

[0033]

[0049] The data acquired by the sensors during the individual's movements and stored in memory are processed to obtain values ​​of certain descriptive parameters, such as, for example, the torque, rotational speed, free moment, position of the object, speed of the individual's swing, etc. The measurements in most embodiments use a "comparative model" to compare the values ​​of the parameters acquired from the subject with "ideal" target values ​​of these parameters to provide guidance on how the subject can modify his / her technique to improve performance and / or reduce the risk of injury, or alternatively, to match the individual's swing with equipment best suited for that individual. Thus, the comparative model involves comparing the subject's actual movements (described by a set of discrete parameters) with the target (ideal reference) parameters.

[0034]

[0050] Analysis of the acquired data typically involves custom algorithms, including, but not limited to, modifying the units of measurement, calibrating the signal to account for the location of the sensor on the club, on the golf club, on the golf ball, or in the environment, smoothing or filtering the signal, analyzing and combining the signal to obtain specific descriptive parameters, extracting discrete metrics from the descriptive parameters, and using comparative models to compare an individual's movements (described by a set of discrete parameters) to target parameters to assess performance or injury risk, and optionally provide data on how a subject can improve their technique or identify optimal equipment.

[0035]

[0051] In certain embodiments, the algorithm includes using a swing model to identify the torque applied by the golfer, determining an ideal torque profile through regression techniques, and providing a torque profile tailored to the biomechanics of each individual golfer. In certain embodiments, the ideal torque represents a smoother profile with lower frequency content, leading to a more consistent and controlled swing. In certain embodiments, the algorithm compares the ideal torque to the sensor-recorded torque of the golfer using a normalized root mean square error (NRMSD). This provides a measure of signal deviation and ultimately swing abnormality, and a linear transformation of the NRMSD value scale to a range of 0 to 10 is used to provide a swing profile value or number.

[0036]

[0052] The first step of the analysis is to perform a calibration routine for each sensor to obtain a signal in the appropriate units. The units of the signal from the sensor are converted from the unit of measure (e.g., volts) to the desired unit of measure (e.g., degrees per second or gravitational acceleration) using a predefined calibration equation. The signal can be modified based on the placement of the sensor and the conditions of the test session. The calibration procedure is customized for the particular type of sensor being used and the location of the sensor on the object being swung. For example, a calibration routine for an accelerometer attached to a segment of a golf shaft will identify the position and orientation of the sensor relative to that segment. Additionally, in some cases, the sensor is adjusted based on the initial conditions of the test session (initial position, orientation, etc.). The data may be filtered or smoothed using standard signal processing filters (e.g., Butterworth, Gaussian, etc.) or custom designed filters specific to the application.

[0037]

[0053] Once the data is in the appropriate units and adjusted, a customized analysis of the movement is completed, depending on the type of sensor used and the movement being analyzed. In general, the signals are processed to obtain values ​​of key parameters, and then critical temporal features of the movement are identified (e.g., start of backswing, early downswing, mid downswing, late downswing, ball impact, end of follow-through, etc.), and discrete metrics are identified either as values ​​of descriptive parameters at these critical time points or as statistical drops in parameters between time points. If the desired parameters are not measured directly, the data from the sensors are manipulated to generate the desired parameters, e.g., position may be the desired descriptive parameter, but velocity is actually measured, and thus velocity is integrated to calculate position. To reproduce measurement errors, methods such as Kalman filtering may be used to combine sensor data, e.g., position data calculated from velocity can be refined using occasional position data to compensate for non-ideal velocity sensor variability.

[0038]

[0054] Once the data is obtained and analyzed through motion measurements and parameter value calculations, feedback is provided to the evaluator and / or directly to the subject. The feedback may be provided in any number of ways, for example, various graphical displays may be used to provide the feedback. For example, the feedback may be provided by displaying the output of the comparison model in the form of a chart or graph on a monitor or other display, as shown in FIG. 7. The chart or graph may display not only the most recent variables, but also a series of variables measured over time and target parameters, etc. Alternatively or additionally, the graphical display may include an image (or avatar) of the subject performing the motion, overlaid with a visual indicator indicating optimal motion achievement. In some embodiments, the display may use perspective techniques to display three-dimensional animations so that the body's motion can be viewed from different perspectives in three-dimensional space. Additionally, the graphical display may include videos of other subjects performing ideal or non-ideal golf swings, along with explanations and / or comparisons with the current subject's motion. Additionally, information such as text or animation may be provided. The graphical display may be on a computer monitor, TV, smartphone, or provided by a projector or other type of display platform. Visualization methods similar to the one presented in this paragraph are also used to communicate the results of performance evaluations to individuals.

[0039]

[0055] In addition to the use of graphical displays, feedback may be provided to the subject by an audible signal, such as a bell, beep, buzzer, chime, alarm, a synthetic voice giving instructions, or other suitable sound. In some embodiments, the sound audible to the subject may be descriptive: one sound (e.g., a click, buzzer, beep of one tone) may indicate that a movement was performed improperly; another sound (e.g., a whoosh, a pleasant chord, or a beep of a different tone) may indicate that a movement was performed properly. In some implementations, the pitch, intensity, and / or frequency of the sound may be altered to provide information about the extent to which the individual's movement and / or position deviates from a particular value or a particular range of values, or to provide other information to the subject. The audible feedback may be provided from a feedback device located on or separate from the individual's body.

[0040]

[0056] The present invention also includes techniques for sonification of golf swing data. In one technique, recorded golf swing data was used to generate synthetic sounds. Club head speed and X-factor each controlled the vocal style frequency, with X-factor also controlling left and right stereo panning. Human sounds are known to be easily recognizable and reproducible, and therefore serve as an effective method of sonification or mapping sounds from useful feedback and data. In certain embodiments, identification of golf swing rotational biomechanics is more perceptible to participants given audio-based feedback compared to feedback using visual observation of video, and demonstration of the sound sonification can provide a valuable training modality for real-time identification of golf swing biomechanics essential for force production.

[0041]

[0057] In certain embodiments, the device includes a sonification sensor or ultrasonic trigger or uses a sensor to calculate this information. The ultrasonic trigger may have one or more ultrasonic emitters capable of emitting sound waves to one or more objects in the target area. The sound waves are emitted periodically and travel toward the target area. When the sound waves reach an object in the target area, they impact the object and are reflected. Once received, the sound waves may be analyzed to determine the kinematic characteristics of the golf club and golf ball, such as speed, spatial position, etc. These characteristics can be used to calculate the activation time of the image capture device.

[0042]

[0058] In certain embodiments, the sonification sensor can function without the aid of any secondary reflector. In other words, the present invention does not require any additional device to focus the sound waves towards the receiver. The present invention can more reliably determine the position and velocity of a golf club or golf ball based solely on the wave motion without the need for additional equipment.

[0043]

[0059] The frequency of the emitted sound waves can be selected such that the likelihood of interference from external sources, such as ambient sound, is eliminated or substantially reduced. Preferably, the emitted sound waves have a frequency that is less susceptible to external interference. Having a wave that is less susceptible to external interference helps enable the device to function without a secondary reflector.

[0044]

[0060] In a preferred embodiment, the device includes an ultrasonic sensor that may be used in conjunction with devices that measure the kinematics of the golf club and golf ball. One embodiment of the present invention provides an ultrasonic transducer that periodically emits sound waves and receives echoes. The sound waves and received echoes can be used to determine the position and velocity of the golf club and / or golf ball. The use of sound waves prevents nearly all external interference from interfering with the measurements.

[0045]

[0061] The present invention functions as a system that can be adapted to work with any type of device that measures the kinematics of an object.

[0046]

[0062] In one embodiment, the device can be used with any type of golf equipment and can be positioned in any orientation that may be dictated by a particular application.

[0047]

[0063] In one embodiment, any type of sensor or any combination of sensors can be used. Alternatively, one or more sensors can be coupled or operatively connected. Any type of sensor known to one of skill in the art can be used. In a preferred embodiment, the sensor is capable of detecting sound waves in an ultrasonic frequency range, which is about 10 to about 500 KHz. In a preferred embodiment, the frequency is about 100 KHz. One of skill in the art will recognize that the pulse duration and pulse frequency of the sensor can be varied according to the particular application.

[0048]

[0064] In some embodiments, the number of sensors combined may be based on the type of motion or type of swing being observed. In embodiments, it may be desirable to have a larger number of sensors to obtain more data. In embodiments, fewer sensors may be required to obtain the desired data.

[0049]

[0065] In one exemplary embodiment, the method according to the present invention includes emitting sound waves by movement of a device attached to a golf club. The sound waves are preferably focused towards a predetermined target area. The size of the area may depend on the type of object used in a particular application. In a preferred embodiment, the area should be large enough for a player to swing the golf club.

[0050]

[0066] In another embodiment, the invention encompasses a golf swing analysis system configured to obtain measurements of swing motion measured by an acceleration sensor and an angular velocity sensor attached to the golf club that function as a measurement device, where the swing analysis is based on the acceleration sensor and the angular velocity sensor attached to the golf club. The golf swing analysis system may be further configured to obtain measurements of swing motion measured by a geometric sensor attached to the golf club that functions as a measurement device, where the device includes the geometric sensor in addition to the acceleration sensor and the angular velocity sensor, and the device is attached to the golf club.

[0051]

[0067] In another embodiment, the present invention includes a golf swing analysis system with a device further including various sensors configured to calculate at least one of head speed, torque exertion, average torque, average power, and energy exertion as a swing profile characterizing a swing motion based on data collected by sensors in the device.

[0052]

[0068] Here, at least one of the head speed, the torque exerted, the average torque, the average power, and the energy exerted is calculated as a swing profile that characterizes the swing motion. Thus, the swing motion can be evaluated based on at least one of the head speed, the torque exerted, the average torque, the average power, and the energy exerted.

[0053]

[0069] In certain embodiments, a golf swing analysis method for analyzing a swing motion of a golf club includes measuring the swing motion using a device including the sensors described herein, deriving parameters of the golf club that occur during the swing motion based on the measurements of the swing motion, and deriving a swing profile.

[0054]

[0070] In another embodiment, the present invention encompasses a golf swing analysis system stored on a non-transitory computer readable medium of the present invention for analyzing a swing motion of a golf club, the system having a computer perform the steps of obtaining measurements obtained by measuring a swing motion using a measurement device, deriving a behavior of the golf club occurring during the swing motion based on the measurements, and deriving a swing profile.

[0055]

[0071] The inventors have found that there is a relationship between a certain swing index, such as head speed and torque exertion, and the swing characteristics of a golf club, such as the weight of the club and the moment of inertia at the end. For example, the heavier the club, the less the golfer can swing the golf club freely, and the head speed decreases. Conversely, the head speed plateaus after the weight of the club drops to a certain level. This is because the club cannot be swung with more power than that used for a full swing. On the other hand, the torque exertion during the swing increases as the golf club becomes heavier, but when the weight of the club increases to a certain level, the golfer reaches his limit and the torque exertion plateaus. That is, when the golfer reaches his limit, the swing index stops increasing, even when the club becomes easier or harder to swing.

[0056]

[0072] Another embodiment of the present invention includes a golf club fitting device, comprising an acquisition unit, an index calculation unit, and an optimum index designation unit. The acquisition unit is configured to acquire measurements obtained by measuring a swing motion performed with each of a plurality of golf clubs. The index calculation unit is configured to calculate a swing index, which is an index characterizing a swing motion of each of the golf clubs, based on the measurements. The optimum index designation unit is configured to designate an intersection point between a first regression line and a second regression line, based on the swing index calculated by the index calculation unit, and designate one of an optimum index and an optimum index range, which is a swing characteristic at or near the intersection point. The first regression line is a regression line of the swing index in a constant region where the swing index is roughly constant with respect to the swing characteristic index. The second regression line is a regression line of the swing index in a proportional region where the swing index is roughly proportional to the swing characteristic index.

[0057]

[0073] Therefore, an optimal swing characteristic index that realizes an optimal swing index corresponding to the limit of the golfer is specified here. That is, the optimal swing characteristic can be specified for the golfer.

[0058]

[0074] Another embodiment of the present invention is a golf club fitting device including the golf club fitting device of the present invention, wherein the acquisition unit is configured to acquire a first measurement value acquired by measuring a swing motion of a golf club having a very small swingability index and a second measurement value acquired by measuring a swing motion of a golf club having a very large swingability index, and the optimal index designation unit is configured to designate a first regression line based on a swing index based on the first measurement value and a second regression line based on a swing index based on the second measurement value, or designate a first regression line based on a swing index based on the second measurement value and a second regression line based on a swing index based on the first measurement value.

[0059]

[0075] Another embodiment of the present invention includes a golf club fitting device, wherein the swing index includes at least one of head speed, torque exertion, average torque, average power, and energy exertion, and wherein the swing index is evaluated based on at least one of head speed, torque exertion, average torque, average power, and energy exertion.

[0060]

[0076] Another embodiment of the present invention includes a golf club fitting method comprising the steps of:

[0077] (1) measuring a swing motion made with each of one or more golf clubs using a measurement device of the present invention including a sensor of the present invention;

[0078] (2) calculating a swing index, the swing index being an index that characterizes a swing motion with each golf club based on measurements of the swing motion;

[0079] (3) specifying a first regression line of the swing index in a certain region where the swing index is roughly constant relative to a swingability index that represents the ease of swinging a golf club based on the calculated swing index;

[0080] (4) specifying a second regression line of the swing index in a proportionality region where the swing index is roughly proportional to the swingability index based on the calculated swing index; and

[0081] (5) A step of designating an intersection point between the first regression line and the second regression line, and designating one of an optimum index, which is a swing index at or near the intersection point, and an optimum index range. [Brief description of the drawings]

[0061] BRIEF DESCRIPTION OF THE DRAWINGS [Figure 1]

[0082] 1 shows a schematic overview of how data is processed using the system and method of the present invention. [Diagram 2]

[0083] Additional features that may be included in the systems and methods for processing data are presented. [Diagram 3]

[0084] 1 shows an overview of how data is processed using the systems and methods of the present invention. [Figure 4]

[0085] 1 shows a schematic overview of how data is plotted using the systems and methods of the present invention. [Diagram 5]

[0086] 1 shows an overview of how data is plotted to generate a swing profile curve using the systems and methods of the present invention. [Figure 6]

[0087] It shows an overview of how a golfer's swing can be used to determine how a swing profile can be generated based on collecting data using sensors to obtain swing torque data. [Figure 7]

[0088] 1 shows an exemplary swing analysis of golf swing magnitude plotted against time. The graph shows the ideal swing torque compared to the golfer's swing torque and the deviation between the two that can be used to match a club or training paradigm to the golfer's swing. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0062] DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

[0089] The present invention encompasses systems and methods for acquiring data in real time, analyzing the data, and providing real-time feedback to a user. Various embodiments allow a user to use the system to track, quantify, and evaluate the swing motion of an equipment. For example, while swinging a bat or golf club, the systems and methods of the present invention process sensor data that quantifies relevant characteristics of the user's swing motion and simultaneously compare it to a stored data set of optimal swing devices and then evaluate the data by matching one or more optimized swing devices to the user. The data collection and comparison can be performed remotely, for example, while the user is at home or any venue, by transmitting data from a sensor device attached to standard personal equipment at a remote location by the user and then transmitting the data to a computing device or server that contains data regarding the optimized swing device.

[0063]

[0090] As used herein, the terms "comprises," "comprises," "having," "including," "including," or any variation thereof, are intended to refer to a non-exclusive inclusion, such that a process, method, article, composition, or apparatus that includes a listing of elements may include not only the recited elements, but may also include other elements not expressly recited or inherent to such process, method, article, composition, or apparatus. Other combinations and / or variations of the above-described structure, arrangement, application, proportions, elements, materials, or components used in the practice of the invention, in addition to those not specifically recited, may be modified or otherwise specifically adapted to a particular environment, manufacturing specifications, design parameters, or other operating requirements without departing from their general principles.

[0064]

[0091] In certain embodiments, a user may use an application (app) or sign in or register on a website to receive feedback in real time via a cell phone or personal computing device (e.g., tablet or computer). In certain embodiments, the feedback is provided throughout the entire swing, providing data at various points (e.g., mid-swing or after swing completion) to indicate whether a movement or action fits a template (e.g., "ideal") data set of data and allowing the user to select optimized equipment specific to the user's swing motion data. For a single exercise or multiple exercises performed throughout a discrete session, the system of the present invention may provide a quantified measurement of the motion, such as a normalized "score" or percentage deviation between the test and the template data set.

[0065]

[0092] Additionally, the system and method can be used to provide swing information over time to monitor the progress of a swing. For example, the system can automatically adapt templates based on progress from previous measurements and long-term goals to ensure that data captured from a user's swing motion or motions meets desired criteria, such as motion direction, speed, and / or range. Over time, the system can enable cloud-based review of progress by the user or a third party (e.g., sports trainer or equipment specialist), highlighting results, which may include modified or instructional exercises, different exercise instructions, all or various combinations of regimens.

[0066]

[0093] Embodiments provide improved capabilities for describing and correlating various variables associated with swing motion. Various embodiments of the present invention provide improved solutions by correlating various data tracked over time and recognizing relevant trends or patterns in the data that may not be apparent without multi-dimensional correlation.

[0067]

[0094] Various embodiments provide an improved solution by providing an automated, immediate, and accurate indication to the user that the acquired data matches the desired characteristics, as well as receiving and providing feedback to the user in real time. For example, embodiments provide real-time feedback to the user based on a pattern matching algorithm. If the received sensor data matches predefined rules, the sensor or associated device may provide an indication to the user (e.g., visually, audibly, and / or overtly) that the swing motion matches the preferred optimal swing equipment. In other examples, the real-time data is provided to a third party (e.g., a coach, trainer, expert), providing the third party with the option to input feedback, which the system then transmits to the user in real time. The automated and / or manual associated real-time feedback allows the user to adapt and receive user information feedback more effectively.

[0068]

[0095] In another embodiment, the present invention provides an improvement that highlights relevant trends over time between different data profiles. Additionally, embodiments may track relevant progress such as range of motion or deviation from a template profile. For example, the system and method may identify when a user's swing motion deviation has derailed by a particular magnitude or duration.

[0069]

[0096] Embodiments may include the generation, utilization, and / or manipulation of data profiles. In some implementations, a data profile may be a portfolio of multi-dimensional data streams over time or multiple time-composite streams of data. A data profile may correlate two or more time-dependent sets of data, such as data received from various sensors. For example, a data profile may represent acceleration in three axes over time. In another example, a data profile may include cumulative magnitude of movement (e.g., activity measurement metrics) and activity measurement metrics recorded over time. In a further example, data from various sensors, including but not limited to one or more inertial sensors, magnetometer sensors, gyro sensors, pressure sensors, force sensors, and electrical sensors, may be correlated with information such as swing speed or swing motion. Other types of data streams may be generated using sensors and the types of data discussed herein, consistent with the disclosed embodiments. In other embodiments, correlation of performance to other external factors is possible.

[0070]

[0097] Embodiments may include generating, utilizing, and / or manipulating a motion swing profile. In certain embodiments, a swing motion profile may be a data profile describing the motion of one or more swings over time. A swing motion profile may include a temporal multi-dimensional record of motion. For example, a swing motion profile may include three-dimensional acceleration data, three-axis orientation data, three-axis angular velocity data, and / or three-axis gravity information over time. In some embodiments, the acceleration data and / or orientation data may include data with less than three dimensions, such as single-axis or two-axis acceleration data and / or orientation data. A swing motion profile may combine unique signals of the same motion, such as a correlation between linear acceleration and angular velocity.

[0071]

[0098] In various embodiments, based on the motion profile, the embodiment includes rendering a graphical representation of the corresponding motion in space. In the example of a three-dimensional motion profile, the disclosed embodiment may include rendering a line in three-axis space showing the path of the object. In further embodiments, the rendered display may include an animation showing an icon that is oriented (e.g., based on the orientation data) and moves along the path at a rate commensurate with the acceleration data of the motion profile. Such data may be rendered alongside or overlaid on top of the synchronized captured video data.

[0072]

[0099] The embodiments include comparing one or more swing motion profiles, or more generally swing data profiles, to the stored data and using a computer algorithm to match the swing data to an optimal swing implement optimized based on the user's swing data. In some embodiments, the systems and methods may identify the magnitude of the difference between the two profiles. Such a difference may indicate the degree to which two data sets, such as two swings of a golf club, match. The difference may be quantified using different calculations. In one example, the disclosed embodiments may sum up the aggregated differences over a period of time (e.g., integrate the differences). Some embodiments may normalize the integrated amount on a unit time basis. Additionally or alternatively, the claimed embodiments may include comparing the two profiles by determining that the two profiles differ by more than a threshold amount (e.g., a predefined threshold or an automatically adjusted threshold) at a predefined set of time points (e.g., one or more temporal data points).

[0073]

[0100] Embodiments may include utilizing an event model to recognize data profiles or portions thereof that meet certain criteria. These criteria may include simple thresholds or complex curve matching algorithms. In an example of complex curve fitting, an event model may be defined by a contour assigned to a particular variable of the profile such that the displacement (e.g., ordinary least square difference) or orthogonal distance (e.g., global least square difference) between the contour assigned to the particular variable of the profile and an axis is below a threshold amount. The amount may be normalized based on the type of application or the magnitude of the test profile data.

[0074]

[0101] An embodiment may use one or more of these concepts individually or in combination, as discussed herein.

[0075]

[0102] In certain embodiments, the present invention encompasses an exemplary real-time data acquisition, quantification, analysis, and feedback system according to certain embodiments of the present disclosure. The system may include one or more sensor devices, computing devices, networks, and servers.

[0076]

[0103] The system and method of the present invention may include one or more sensor devices for aggregating sensor data. A sensor device represents one or more sensors that provide data to the system. Each sensor device may include the same sensor capabilities or different sensor capabilities. For example, one sensor device may include an inertial measurement unit, while a second sensor device provides gyrometric data. In different examples, the entirety of the sensors shown may include only an inertial measurement unit, but may be located on a single device or at different points on a single device. As further discussed below, the sensors may provide various sensed data to the system.

[0077]

[0104] The systems and methods may include a computing device. In some embodiments, the computing device may be a general-purpose computer, a tablet device, a smartphone, or a smartwatch. The computing device may include a processor, memory (e.g., RAM, flash memory, and / or hard disk), various wired and wireless interfaces (e.g., Bluetooth, IEEE 802.11, Ethernet, USB, USB-C, and / or proprietary ports), input devices (e.g., touch screen, keyboard, mouse), and a display. The computing device may operate programmable instructions stored locally or remotely to perform the disclosed processes.

[0078]

[0105] The computing device interacts with one or more sensor devices. The computing device may receive sensor data from the sensor devices. For example, one or more sensors on a golf club may transmit data received from the sensors in real time. The sensor data may be high resolution data, and the connection between the sensor device and the computing device may be a high bandwidth connection, such as a Bluetooth wireless connection. Such high bandwidth wireless technologies may use more power than alternatives (e.g., Bluetooth "low energy"), but the increased data resolution that the system may use may require a higher bandwidth wireless interface.

[0079]

[0106] In various embodiments, the systems and methods may include a controlled device that performs functions based on received instructions. For example, the controlled device may include output devices such as a remote display, a speaker, and a haptic engine that provides feedback to a user of the sensor device. These types of controlled devices may provide status indicators to the user based on the sensor data, such as displaying a green light, playing a positive tone, or tapping the user via a worn haptic engine to inform the user that the sensor device is providing an acceptable data profile.

[0080]

[0107] In some embodiments, the network may be a wired and / or wireless network. For example, the network may be a LAN, a WAN, a WLAN, or the Internet. The system may connect various devices using the network. For example, the computing device may connect to a server, a controlled device, and / or a sensor device using the network. Alternatively, the computing device may directly interface with the sensor device and / or the controlled device. For example, the computing device may form its own wireless access point to connect to other devices.

[0081]

[0108] In various embodiments, the system includes a server to provide networked storage and analysis. The server may be a networked computer. The server includes at least one central processing unit, such as a data processor, that executes program components to fulfill user or system generated requests. The processor may include a dedicated processing unit or a general purpose microprocessor.

[0082]

[0109] The server facilitates network-based (e.g., "cloud") storage and data interaction. For example, a computing device transmits a data profile and the underlying raw data to the server for storage. In one embodiment, the server analyzes the data profile in real-time or over time and may provide feedback based on the sensor data. The server sends notifications (e.g., data uploads, database updates) based on the analysis of the data.

[0083]

[0110] In some embodiments, the server acts as a portal that allows users to interact with the archived data profiles and the raw data, for example, the server may provide a graphical user interface that presents data profiles organized by particular categories of swing equipment.

[0084]

[0111] The sensor device may include a processor, storage, input / output, an IMU (inertial measurement unit), a gyro sensor, a wireless transceiver, and / or a power source.

[0085]

[0112] In some embodiments, the processor is a general purpose processor, a programmable microcontroller, a programmable processor (e.g., a field programmable gate array (FPGA) or complex programmable logic device (CPLD)), or an application specific integrated circuit (ASIC).

[0086]

[0113] In some embodiments, storage may include internal storage and / or external storage. Internal storage may include on-board memory, such as, for example, flash memory or RAM. External storage may include removable memory media, such as, for example, compact flash cards, secure digital cards, memory sticks, optical disks, etc. In some implementations, storage may include non-transitory computer-readable media that store instructions that, when executed by a process (e.g., a processor), cause the processor to perform the disclosed functions and processes.

[0087]

[0114] In some embodiments, inputs and outputs may include outputs and inputs. In some embodiments, outputs may include lights (e.g., LEDs that are on or off, LCD displays, lasers, projectors), speakers (e.g., piezoelectric speakers, buzzers, sirens, loudspeakers), and haptic engines (e.g., vibrators, haptic feedback mechanisms). Lights may include lights on various surfaces and sensor devices at different angles.

[0088]

[0115] The input may allow a user to activate and interact with the sensor device. In some embodiments, the input may include a physical input mechanism (e.g., a button, a switch, a capacitive interface) or a method for receiving input (e.g., an infrared receiver, a light receiver, a USB, or a serial port). The physical input mechanism may, for example, allow a user to turn the sensor device on and off, sync with a computing device, and / or change modes.

[0089]

[0116] In some embodiments, the systems and methods include one or more sensors, which may include one or more IMUs, that may capture multi-dimensional acceleration and orientation data. In certain embodiments, the sensors may include magnetometers, gyroscopes, and / or accelerometers. In certain embodiments, the processor may sample the acceleration and orientation data at a rate of about 50-500 samples / second. In some embodiments, multiple IMU devices may be "stacked" and then time sliced ​​to allow for an N-fold increase in the sample rate, such that two such devices can generate 100 samples / second or more.

[0090]

[0117] In some embodiments, one or more sensor devices may include multiple instances of a measurement as a redundancy measure to filter outlier measurements. For example, a processor may receive three-axis acceleration data from two or more sensors in the device. The processor may average the acceleration data to increase accuracy, or if there is more than one sensor, the processor may not utilize the highest and lowest readings, but may average the remaining readings to reduce measurement inaccuracies.

[0091]

[0118] The sensor device may include a variety of sensors. In some embodiments, the sensors may be implemented within the sensor device as internal sensors. In some embodiments, the sensors may be interfaced with the computing device through a port or physical interface as external sensors.

[0092]

[0119] In some embodiments, the sensor device may include a wireless transceiver. The transceiver may facilitate communication with a computing device, a network, and / or a controlled device. In some embodiments, the transceiver may include a Bluetooth transceiver and / or a Wi-Fi transceiver. In one example, the Bluetooth transceiver may be a Bluetooth "Classic" transceiver rather than a Bluetooth "Low Energy" transceiver to provide increased bandwidth for transmitting high resolution sensor data in real time (e.g., to a computing device). In another example, the Wi-Fi transceiver may be an IEEE 802.11a / b / g / n / x transceiver. Consistent with the bandwidth requirements of the disclosed systems and processes, additional wired and / or wireless standards may be used.

[0093]

[0120] The sensor device may include a power source that provides electricity to components such as a processor and storage, among other elements. In some embodiments, the power may include a direct current power source, such as a battery. For example, the power may include a Lithium-Ion Polymer (LiPo) battery, a Nickel Metal Hydride (NiMH) battery, and / or a Nickel Cadmium battery. If the power includes a battery, the power may further include a charging circuit, such as an electrical port, a removable battery, and / or an inductive charging circuit.

[0094]

[0121] In certain embodiments, the sensor device may be removably and temporarily attached to the golf club during data acquisition. In some embodiments, a mount may be used to secure the sensor device to the golf club. The golf club may be a driver, a wearway wood, a hybrid club, an iron, and a pitching wedge or a putter, any of which may all function as a golf club. The sensor device may be connected to the golf club, for example, at the base of the grip. This positioning of the sensor device may advantageously provide more accurate inertial data of the swing motion. A "swing" may refer to, for example, the motion of teeing off with a driver, swinging an iron down the fairway, and / or putting with a putter. Additionally, placement at the base of the grip allows the user to swing the golf club without the sensor device interfering with the user's line of sight. However, in other embodiments, the sensor device may be mounted at other locations on the golf club. In still other embodiments, multiple sensor devices may be mounted at different locations on the golf club, such as near the head, along the shaft, and / or at various locations on the grip.

[0095]

[0122] In certain embodiments, the sensor may include a feedback mechanism (e.g., a light or speaker) that notifies the user when the system is active and ready to transmit data. This notification may occur just prior to the swing and / or during the golf swing. Alternatively, the feedback mechanism may be programmed to notify the user after the completion of the golf swing, either automatically or in response to a user request for feedback and / or sensor data.

[0096]

[0123] In certain embodiments, to provide data to the system, the sensors may form independent sensor devices (e.g., a version of a sensor device). For example, the sensors may independently transmit data to a computing device via a wireless connection (e.g., a Bluetooth connection). Similarly, the independent sensor devices may join a sensor mesh network to transmit data through the system. Alternatively, the sensors may interface with the sensor device (e.g., as one or more external sensors) to provide data to the system. For example, the sensors may transmit data to a processor for handling via an external sensor interface in the sensor device.

[0097]

[0124] Alternatively, in other embodiments, the device includes a sensor that preferably emits ultrasonic waves across a sonification area. Preferably, the sonification area is a generally circular area having a diameter of about 6 inches to about 2 feet. More preferably, the sonification area is a generally circular area having a diameter of about 1 foot to about 2 feet.

[0098]

[0125] When sound waves are emitted at one or more desired points, at least some of the emitted sound waves will be reflected by objects in the target area. Other portions of the emitted sound waves may not strike objects and continue traveling through space until dissipating. In a preferred embodiment, the reflected sound waves are received by a number of sensors. The sensors may be connected to any type of computing device. The computing device may include one or more microprocessors, electronic gates, etc. In a preferred embodiment, the output of the sensors is analyzed to determine various characteristics of the motion of the golf club and / or golf ball. Position and velocity of the object are among the characteristics that may be calculated. In other embodiments, other desired characteristics may be calculated.

[0099]

[0126] In one embodiment, the present invention determines the position and velocity of an object and then transmits this information to a computing device, such as a processor.

[0100]

[0127] In another embodiment, the systems and methods of the present invention may be used with tennis rackets, hockey sticks, baseball bats, fishing rods, etc. that include sensor devices.

[0101]

[0128] Similar to the systems described above, in tennis racquet, hockey stick, baseball bat, fishing rod, etc. systems, a sensor device (e.g., including one or more sensors) may be attached to the tennis racquet, hockey stick, baseball bat, fishing rod, etc. In some embodiments, a mount may be used to secure the sensor device to the tennis racquet, hockey stick, baseball bat, fishing rod, etc. This positioning of the sensor device may advantageously "hide" the sensor device from the user's line of sight while the user is manipulating the tennis racquet, hockey stick, baseball bat, fishing rod, etc. In still other embodiments, the sensor device may be mounted in different locations, such as on the top, along the shaft, and / or on the grip.

[0102]

[0129] In addition, one or more additional sensor units may be used. For example, the additional sensor may measure the pressure of the user's hand. Such sensor data may be used to generate a pressure map. Based on the sensor data, the system may provide feedback on the orientation of the equipment during the stroke. For example, with respect to golf, the club may be less efficient when the club face is not held perpendicular to the swing direction. The calculated pressure map may reveal points where the club is not swung efficiently, and the system may provide feedback to the user to adjust the orientation to provide a maximum swing.

[0103]

[0130] The sensor devices may be attached to other equipment, such as apparel (e.g., belts, bracelets, shirts, shoes), mobility assistance devices (e.g., canes, walkers, scooters, crutches, and / or fitness equipment (e.g., medicine balls, skipping ropes, helmets, elastic bands).

[0104]

[0131] In some embodiments, various sensors may interface with a single sensor device. The sensors may be connected to the sensor device (e.g., as external sensors). Two or more sensors may identify two or three dimensions. Based on the multi-dimensional data, the system may generate a map and provide feedback to the user in real time to improve their swing. For example, various portions of the output may be used to indicate to the user that they should increase their swing speed.

[0105]

[0132] In various embodiments, the sensor device may perform certain data aggregation, calculation, and / or feedback functions locally.

[0106]

[0133] In some embodiments, the sensor device may initiate a connection to the computing device based on input from a user (e.g., using input such as a button). For example, the sensor device may connect to the computing device using a Bluetooth pairing procedure or via a Wi-Fi connection. In some embodiments, the computing device may search or seek out sensor devices that are attempting to connect or have available connections. In some embodiments, this may include a calibration procedure.

[0107]

[0134] The sensor device may calibrate sensors, such as inertial or gyrometric sensors, before pairing with the computing device. For example, the sensor device may provide instructions to the user (e.g., blinking lights) that indicate to the user to rotate the sensor device so that the IMU can align its axes and adjust scaling factors to increase the accuracy of position and orientation calculations. In other embodiments, calibration may occur during or after pairing, such as when the system determines that data from the sensors lacks the required accuracy.

[0108]

[0135] In certain embodiments, the present invention encompasses systems and methods for analyzing swing profiles. As used in this disclosure, the term "swing profile" may refer to various golf motions, including, for example, swinging a driver, performing a chip shot, and / or putting with a putter. The terms "putt profile" and "swing profile" may be used interchangeably. A computing device may retrieve a data profile (e.g., a motion profile) that serves as a template or pattern for comparison purposes. The data profile may include multi-dimensional acceleration and / or orientation data corresponding to a golf swing. In one embodiment, the template swing profile may be recalled from local or network storage. For example, a computing device may request a particular template swing profile from a server or other cloud storage.

[0109]

[0136] In one embodiment, loading the template swing profile may include recording one or more motion profiles of an actual swing. For example, a user may provide an initial motion at the beginning of a practice session that acts as a template, and may want to repeatedly practice the initial template motion. To record the initial template motion, the system may receive data recorded during the swing motion from a sensor device. For example, one or more inertial or gyrometric sensors may record, for example, acceleration data and / or orientation data along three or fewer axes during a particular swing motion. The sensor device may transmit the sensor data to a computing device, and the computing device may store the IMU data of the swing motion as a motion profile. For example, an "ideal" template swing may be recorded at a preferred setting, such as with a trainer, and may be recalled later when the user practices without the trainer nearby.

[0110]

[0137] In another embodiment, the system and method may include recording a motion, generating a template, storing the template on a networked server (e.g., a server), and / or requesting the stored template from a networked storage, and matching the swing profile with an optimized equipment. In a further embodiment, the system and method may include receiving a motion profile generated from a software application rather than recorded from a live motion. For example, a computing device may receive a motion profile generated by a sensor and then match the motion profile with an optimized type of equipment, driver, wood, iron, or putter. Additional combinations or intermittent processes may be used to receive a data profile or motion profile for use by the computing device consistent with the remaining steps of the process.

[0111]

[0138] In certain embodiments, the systems and methods may receive real-time sensor data. The computing device may receive the real-time data from the sensor device. In some embodiments, the computing device may receive the sensor data in real-time via wireless transmission technology such as Bluetooth or Wi-Fi (e.g., using a Bluetooth transceiver and / or a Wi-Fi transceiver). The computing device may receive data packets containing real-time data samples from one or more internal and / or external sensors. For example, the computing device may receive one or more packets containing 1-10 samples of data for a given sensor over an interval of 1-5 milliseconds with a delay of less than 5 milliseconds from capture by the sensor device. The samples may be stored in an array as time-value pairs, such as a sensor sample value paired with a timestamp value in a list. In some embodiments, the computing device may continue to receive sensor data packets as long as the sensor device captures relevant data. Following data capture, the computing device may then match a swing profile based on the sensor data to various stored club information profiles such that a user is matched with an optimized golf club based on the user's swing profile.

[0112]

[0139] In certain embodiments, the systems and methods of the present invention may calculate a test swing profile. The computing device may aggregate the received sensor data into a combined temporal arrangement of sensor readings. In some embodiments, the computing device may create a new data structure that organizes the sensor data of a given motion. The data structure may store an abbreviated form of the raw sensor data in a data object along with standardized metadata. For example, the computing device may receive raw sensor data with various fidelities (e.g., different sample rates and / or data precision). The computing device may organize the data such that classes of data structures that are generated have sample data with consistent sampling rates and / or consistent resolutions (e.g., values ​​with the same number of significant figures). For example, the computing device may downsample sensor data with a sampling rate greater than the standardized sampling rate or sampling rate range of a given class or type of swing profile (e.g., a type of motion profile) data structure. If the computing device receives sensor data with a sampling rate lower than the minimum sampling rate of a given class of swing profile, the computing device may interpolate additional data points to achieve the desired sampling rate (e.g., using curve fitting or regression analysis).

[0113]

[0140] In some embodiments, a swing profile (e.g., a data profile or motion profile) may include standardized metadata. For example, a swing profile class may include fields for standardized data analysis variables such as mean and median values ​​of sensor data and standard deviation, high values, low values, minimums, maximums, and inflection points. Additional data analyses discussed throughout this disclosure may be stored as part of the swing profile.

[0114]

[0141] In some embodiments, the calculation may include comparing the test swing profile to a reference profile, such as a template swing profile. The computing device may compare the two profiles to determine where the two profiles deviate and the degree to which the two profiles deviate. In one embodiment, the computing device may generate a profile that indicates differences over time. Consistent with the data profile and motion profile comparisons discussed in this disclosure, additional comparisons may be made.

[0115]

[0142] In certain embodiments, the systems and methods of the present invention may provide feedback based on a calculation made by a computing system. The feedback may include visual, tactile, and / or audible signals directed to the user and / or a third party. The feedback may be based on the calculated test swing profile, metadata associated therewith, or a comparison based on the calculated test swing profile. The calculation may act as a trigger for the feedback. For example, if the test swing profile deviates by more than a predefined amount, the system may generate feedback and match the swing with a particular optimized golf club. In another example, the system may generate feedback if the test motion profile matches a particular criterion, such as an average or standard deviation value. Such values ​​may be user-defined or predefined (e.g., from loading a template profile). The feedback may be provided to the user, for example, 5-20 milliseconds after receiving data from the sensor.

[0116]

[0143] In some embodiments, the computing device may provide feedback to the user. For example, the computing device may generate a graphical user interface that displays an analysis of the sensor data. The graphical user interface may show different views of a swing motion such as that shown in the user interface and then recommend optimal equipment.

[0117]

[0144] In certain embodiments, the systems and methods of the present invention provide real-time data acquisition and feedback graphical user interface according to certain embodiments of the present disclosure, and the graphical user interface may include regions showing real-time related feedback and a representation or abstraction of sensor data previously received from the sensor device. In some embodiments, the interface may include one or more dimensional views including a functional space map of the processed sensor data, such as a top view region, a side view region, and a back view region. Each of the multiple views may display an elevational view (e.g., side, top, back) of a golf motion (e.g., golf putt, golf swing) plotted on each axis. In some embodiments, the depicted path may be annotated or colored to convey additional data about the putt profile data. For example, the line path may be color-coded to indicate motion acceleration (e.g., green for acceleration, red for deceleration) or deviation in the template putt profile (e.g., an increase in red hue corresponds to the amount of deviation). Other color coding may be used to convey different putt or swing variables.

[0118]

[0145] In certain embodiments, this includes a metadata display area, which may display various labels such as a timestamp for a putt profile and an identification number and / or title for a motion profile. Although not shown, area 705A may also include location data such as GPS coordinates, geographic area, and / or hole number and corresponding golf course.

[0119]

[0146] In addition to the real-time data, the interface may include a record of previous data, may include a list of previous profiles, and a selection area shows the selected putt profile in a full screen display.

[0120]

[0147] The user interface may include a computing device (e.g., a computing device). With a smaller area, the user interface may display a metadata display region and a top view region at once. However, the user may interact with the user interface to retrieve additional data (e.g., regions of the interface) via menus. For example, the user may scroll through different elevations of a putt profile and select a "back" arrow shown to return to a list of previously recorded putt profiles.

[0121]

[0148] The computing device may send one or more signals to the sensor device to trigger an output based on the calculation. For example, the computing device may send instructions (e.g., using a wireless transceiver) to activate a light, a speaker, and / or a haptic engine. In one example, if the computing device determines that the test swing profile matches the template swing profile, the computing device may send a signal to display a green light. However, if the test swing profile deviates by more than a specified amount, the instructions may activate a red light on the sensor device. In another example, if the test swing profile meets certain criteria, the computing device may send instructions to the sensor device to cause the haptic engine to perform a certain vibration and / or cause a speaker to play the sound of a crowd cheering. Other combinations of triggers and feedback mechanisms may be used based on environmental conditions.

[0122]

[0149] In other embodiments, the sensor device may perform the calculations locally and initiate feedback without instructions from the computing device, for example, a processor may perform the above functions of the computing device and initiate one of the feedback mechanisms locally.

[0123]

[0150] In some embodiments, the sensor device may identify the particular sensor that sampled the data. For example, the processor may identify the sensor that captured the data.

[0124]

[0151] In certain embodiments, the systems and methods of the present invention may obtain data from one or more sensors. In some embodiments, the sensor device may initiate recording and processing of sensor data from one or more sensors (e.g., inertial or gyrometric sensors). For example, the sensor device may record sensor readings in temporary storage for a predefined interval.

[0125]

[0152] In some embodiments, the sensor device may evaluate whether the data is eligible for certain processing. Certain types of data may represent noise or other non-useful data. For example, the sensor device may evaluate whether the motion data corresponds to a golf swing rather than an erroneous, non-swinging motion of the club (e.g., walking with the club, placing the club in a golf bag, preparing to swing). The sensor device may filter out motions that are determined not to correspond to a golf swing. In some embodiments, the filter may calculate the amount of deviation and filter out data that is too noisy to represent a simple swing motion. For example, a swing may typically include two consecutive motions (e.g., a backswing and a forward swing) that occur over a relatively standard range of time. In other embodiments, a simple filter where readings below a specified magnitude are filtered out. For example, the sensor device may identify and discard insignificant motions that have acceleration values ​​below a threshold amount. In still other embodiments, the sensor device may power down or go into a "hibernation" mode to conserve power if satisfactory data is not received.

[0126]

[0153] In certain embodiments, the systems and methods may determine whether a backup copy of the captured data needs to be saved. In some embodiments, the sensor device may determine whether sufficient storage exists and / or preferences indicate that the sensor data should be archived locally.

[0127]

[0154] In certain embodiments, the systems and methods may transmit the acquired sensor data to local storage. For example, if the sensor device determines that the preferences indicate that the data should be stored locally and the sensor device detects an external storage device, the sensor device may save the sensor data in local storage (e.g., internal storage and / or external storage).

[0128]

[0155] The sensor device may transmit the acquired sensor data. In some embodiments, the sensor device may transmit the sensor data to a computing device or server. For example, when the cumulative sensor data reaches a predetermined threshold, such as a percentage of the amount of local storage used, or for a predetermined duration, the sensor device may format the acquired sensor data and transmit it to a computing device for further processing.

[0129]

[0156] In certain embodiments, systems and methods provide real-time data acquisition and feedback in accordance with some embodiments of the present disclosure. In certain embodiments, systems and methods are discussed with respect to a computing device analyzing sensor data and recommending an optimal swing device. However, additional devices may perform all or part of the disclosed functionality. For example, a sensor device may perform certain feedback calculations locally rather than receiving instructions from a computing device.

[0130]

[0157] In certain embodiments, the systems and methods include a computing device that recalls user data from networked or local storage. For example, the computing device may send a request, including a user identifier, for past data associated with the user. Based on the request, the computing device may receive past user device (e.g., data profile), user preferences for a particular movement, an athletic profile, etc.

[0131]

[0158] In certain embodiments, the systems and methods include a computing device that receives real-time data from one or more sensors and matches such data based on a user's swing with an optimal piece of equipment, e.g., a golf club, that is optimized based on an analysis of the data from the sensors to match the user's swing profile with the optimal club. In some embodiments, the computing device may receive the sensor data in real-time via wireless transmission technology, such as Bluetooth or Wi-Fi (e.g., using a Bluetooth transceiver and / or a Wi-Fi transceiver). The computing device may receive data packets that include real-time data samples from one or more sensors. For example, the computing device may receive one or more packets that include 1-10 samples of data for a given sensor over an interval of about 1 to about 5 milliseconds with a delay of less than about 5 to about 20 milliseconds from capture by the sensor device. The samples may be stored in an array as time-value pairs, such as a sensor sample value paired with a timestamp value in a list. In some embodiments, the computing device may continue to receive sensor data packets as long as the sensor device captures the associated data.

[0132]

[0159] In some embodiments, the computing device may receive multiple motion data streams simultaneously. The simultaneous streams may be from multiple sensors located on the swing device. In the sensor example, the system may receive the sensor data and interlace it to increase the resolution of the data. For example, the sensor data streams may have the same sample interval, but the system may control the sampling time to offset each stream based on the number of streams to increase the resolution. The computing device may calculate the offset amount by dividing the sampling rate by the number of streams. The computing device may provide instructions to each sensor to begin sampling at a multiple of the offset amount so that no two sensor devices sample at the exact same time. The computing device may combine the sample streams by interlacing discrete data points based on their associated timestamps.

[0133]

[0160] In the example of multiple motion streams from different locations, sensors may be placed on different parts of a user's club to account for swing orientation changes, for example. Such an exemplary sensor placement may be used to measure a variety of motions. Such placement may advantageously provide a more accurate range of motion data because the sensor devices take into account relative orientation in three dimensions, rather than being limited to angles in one dimension.

[0134]

[0161] In other embodiments, the method and system may obtain various measurements using only a single sensor device. For example, a single sensor device may provide three-axis orientation data over time. A user may removably fasten a sensor device, for example, to a golf club, and the sensor device may transmit orientation data indicative of the orientation over time. In this example, the sensor may be used to calculate various movements by determining the magnitude of orientation data of a particular axis or combination of axes over time. Based on the orientation data from multiple axes, the computing device may calculate an equation (e.g., ax+by+cz=0) that defines a three-dimensional plane in which the movement is performed. Then, within that plane, the computing device may determine the number of degrees through which the orientation passes. Thus, even though a user's movement may not be entirely performed to occur along only one axis, such as when the user performs a swing while leaning forward slightly, the computing device may calculate various movements on a coordinate system normalized to the body's orientation while the user is performing a given movement.

[0135]

[0162] The computing device may create a data profile based on real-time data received from various sensors. For example, the computing device may organize the real-time data into measurements that are standardized in sampling rate and number of significant figures. If necessary, the computing device may convert units of measurement (e.g., to use metric units) to be consistent across all data profiles of the same type.

[0136]

[0163] In creating the data profile, the computing device may manipulate the raw data received from the sensor devices. For example, the computing device may discard data values ​​above and / or below predefined maximum and / or minimum values. The computing device may also fit a curve or polynomial function to the data. If multiple sensor data streams are received, the computing device may triangulate data from the multiple streams to increase accuracy and reduce noise in the generated data profile.

[0137]

[0164] In some embodiments, the computing device may correlate real-time data from multiple sensor devices simultaneously.

[0138]

[0165] The systems and methods of the present invention may determine whether to send automatic feedback. The computing device may compare the calculated data profile to various criteria. For example, the computing device may determine whether characteristics of the data profile, such as the mean, standard deviation, slope, and inflection point, match criteria, such as criteria loaded from an optimized club profile. In certain embodiments, the systems and methods may compare the data profile to a template to determine how much the data deviates from a desired template data profile. For example, the computing device may compare the calculated data profile to the template data profile based on an amount of deviation, such as an average deviation, a combined total deviation, or a maximum deviation. Based on this comparison, the computing device may determine that automatic feedback should be provided to match the swing profile to the optimized swing profile.

[0139]

[0166] In certain embodiments, the systems and methods of the present invention may provide iterative feedback. The computing device may compare the swing data profile to multiple template profiles to determine which template profile most closely matches the swing profile. The comparison may be based on a least squares difference between the test and template curves. In other examples, Fourier transforms may be compared. Additionally, the comparison may be performed by identifying deviations between the test data (e.g., test motion profile) and template data (e.g., template or club optimized motion profile) over time to identify deviations in the test motion profile that indicate how the test motion deviated from or was performed in the target motion profile. Additional comparisons of data profiles may be used as described throughout this disclosure. Each template profile may be associated with a different type of feedback (if any), which may be based on which template profile is the best match (e.g., has the least amount of deviation, highest correlation, lowest least squared difference), and the computing device may provide feedback including, in one example, which type of optimized golf club to recommend, and one or both of the control device and controlled device are selected based on the template data profile that most closely matches the test data profile. Such feedback may include an indication of which profile the test data profile most closely matches and a quantitative "score" of the movement based on the amount of deviation.

[0140]

[0167] The systems and methods of the present invention may transmit a feedback generating signal. Based on the received real-time data meeting certain qualifying conditions, the computing device may transmit a signal to generate feedback, such as feedback at a sensor device using one or more of the outputs. For example, in response to determining that the average value of a particular sensor falls within a particular range.

[0141]

[0168] In some embodiments, each criterion or qualifying condition may be paired with a particular type of feedback. Such pairings may be stored in a user profile. For example, if the sensor device falls within a particular range, a given type of feedback may be provided. In an embodiment where different templates are mapped to different types of feedback, the computing device may send corresponding feedback instructions to the sensor device. In certain embodiments, if the user's swing motion results in a motion profile that matches the template motion profile, the computing device will recommend one or more optimized golf clubs.

[0142]

[0169] For example, if the computing device is a tablet computer, the computing device may display the data profile on a touch screen of a user interface. In some embodiments, the user interface may display the analysis along with a selection area for providing feedback to the user. For example, a coach, trainer, or equipment specialist may hold a tablet that displays a real-time analysis of the user's swing. The user interface on the tablet may show the acceleration data in one or more dimensions along with an individual's metric score. In one embodiment, the metric score may be displayed in bold, italics, underlined, or in a particular color if it falls within a predetermined range, and an optimized golf club may be recommended.

[0143]

[0170] The systems and methods of the present invention may determine whether manual feedback has been received. In one embodiment, the user interface of the computing device may provide an area to select user feedback. For example, a third party may manually select the type of feedback and when to send the feedback. The third party may monitor real-time data profiles and associated metrics to determine when to initiate feedback.

[0144]

[0171] In one embodiment, the system may monitor manual feedback, including the type and timing of manual feedback that suggests automatic rules for providing feedback. For example, the system may capture the resulting data profile and associated metrics with each manual feedback request, accumulating the information to provide recommendations for optimized swing equipment. The system may recognize trends in the timing and type of feedback. For example, the system may determine that a third party switched from using lights for feedback to using haptic feedback midway through a movement or exercise. In another example, the system may recognize that feedback has been initiated when a particular metric falls below a threshold or matches a particular pattern. In yet another example, the system may recognize variable thresholds imposed by manual feedback.

[0145]

[0172] In certain embodiments, the computing device may provide a graphical user interface that allows a third party to view relevant data and highlight data of interest. For example, if a metric exceeds a threshold tailored to an individual, a graphical indication (e.g., color highlighting or bold font) may be used to highlight the particular metric and corresponding user in real time. The computing device's graphical user interface may sort the list of the user's data to show the highlighted data at the top of the list of user's data. In a further example, the computing device may calculate data for a subset of users. For example, the computing device may receive an input that defines a set of individual identifiers as belonging to a group. The computing device may then calculate and display the relevant data for the group.

[0146]

[0173] The user interface may then receive input from a third party (e.g., using a touch screen) to select a user or group of users for which to provide feedback. The computing device may receive one or more user identifiers, group identifiers, or a selection of all users and the type of feedback to be sent to each sensor device for the selected users. For example, the computing device may receive instructions to send instructions to the sensor devices to generate a particular pattern. Further embodiments allow a third party to select a subset of users for which to provide additional data, such as a live plot of a particular selected metric over time (e.g., multiple golf swings).

[0147]

[0174] In another example, a golfer's club may be equipped with sensor devices. Each sensor device may transmit acceleration data and gyrometric sensor data to a computing device that may be operated by a third party, such as an assistant coach, trainer, or expert. The computing device may calculate the associated acceleration metrics for display in a graphical user interface. At the end of each play, the computing device may highlight individuals who may warrant detailed swing analysis. For example, the computing device may include acceleration and gyrometric thresholds used to identify various events. The computing device may further generate a time correlation of acceleration data, impact events, and gyrometric activity to identify specific user identifiers to a third party using a graphical user interface. For example, a swing with no acceleration magnitude may be used to highlight individuals who may have suffered, for example, trauma. The computing device may transmit commands to the sensor devices of the generated or identified users to generate sounds or activate on-board lights so that individuals may be easily identified by training staff or other experts.

[0148]

[0175] The systems and methods of the present invention may transmit a feedback generating signal. For example, a computing device may transmit a command to a corresponding sensor device to initiate feedback. The systems and methods of the present invention may determine whether monitoring should continue. In one embodiment, the systems and methods of the present invention may continue while the sensors continue to provide real-time data. Once the exercise is completed and the sensor device is deactivated, the process may end.

[0149]

[0176] In various embodiments, the systems and methods of the present invention may associate sensor data with a particular piece of equipment. For example, the systems and methods of the present invention may be used to evaluate different golf clubs and other sports equipment. The associated sensor data from a user performing a given activity may be used to quantitatively rank the equipment. For example, a user may swing a club multiple times, and each time the user swings, the systems and methods of the present invention may receive sensor data, such as acceleration data, gyrometric data, and other data, to calculate different metrics for each swing. By comparing the data profile acquired while swinging to the stored profiles, the computing device may determine a quantified score to evaluate the swing and a recommended optimized swing device (e.g., a golf club). For example, the score may represent the difference of the test swing profile of each swing compared to a template motion profile of the most efficient swing technique, the speed of the swing (e.g., adjusted based on the number of previous swings), and a normalized aggregation of personal data (e.g., from the sensor data). Additional variables may be used to evaluate equipment consistent with the present disclosure.

[0150]

[0177] The systems and methods of the present invention optionally include a server that may receive a user profile, including, for example, a swing profile of the user.

[0151]

[0178] The systems and methods of the present invention may receive aggregate sensor data. A server may receive a data profile acquired while the user is performing an activity. For example, the computing device may upload the calculated data profile at regular intervals (e.g., every minute, hour, daily) or upon session completion (e.g., when all prescribed swings and / or movements in the plan are determined to be completed for a given day after no additional data has been received for a predefined period of time). The server may, for example, automatically add the received data to the user's record in the electronic record and match the data with optimized equipment by interfacing with existing records in the database software. For example, the server may determine whether the uploaded data profile indicates whether the user is suited to use a particular type of equipment, such as a particular shaft of a golf club. For example, the server may determine that the user's swing meets a desired threshold for the stored equipment. In another example, the server may determine that the sensor data indicates that the sensor device has an increased range of motion beyond a target range, such as being suitable for multiple different types of optimized, stored equipment (i.e., the user swing profile matches multiple clubs or shafts stored on the server.) In a further example, the server may determine that the sensor data matches a pattern, which may be determined to indicate that there is or is not sufficient confidence in matching the swing profile with an optimized club or shaft.

[0152]

[0179] The systems and methods of the present invention may include additional calibration steps that provide additional levels of improvement after the secondary calibration. In some embodiments, the systems and methods of the present invention may include tertiary and / or quaternary calibration. Initially, the system may perform these additional tertiary and quaternary calibration steps based on available processing power, complexity, and application demands. For example, the system may determine that high accuracy is possible based on a particular activity or measurement type, etc. Based on this determination, the system may perform tertiary, quaternary, and subsequent "n-level" calibrations based on the activity and / or measurement demands. For example, the server may receive the activity and / or measurement type identifiers and retrieve or otherwise derive the number of calibration iterations required.

[0153]

[0180] In some embodiments, tertiary, quaternary, and / or "n-level" calibrations may be performed using the same process as described for secondary calibration. However, these additional calibration steps may be performed with greater precision. For example, in an embodiment where secondary calibration identifies the position and orientation of the sensor device to the nearest 0.1 unit (e.g., cm, inch, degree, radian), tertiary calibration may confirm the 0.1 unit measurement and calculate a position and orientation with an accuracy of 0.01 units. Continuing this example, if the system determines that a quaternary calibration needs to be performed, the quaternary calibration may calculate a position and orientation with an accuracy of 0.001 units. The confidence thresholds and / or pattern matching discussed for primary and secondary calibrations may be applied to tertiary, quaternary, and / or "n-level" calibrations.

[0154]

[0181] In one embodiment, the server may receive updates to the plan from a third party via a user interface. For example, the third party may approve or modify suggested automatic changes to the plan. In an alternative example, the third party may reject the proposed changes and / or include a message (e.g., text, video, audio) to the user.

[0155]

[0182] The systems and methods of the present invention may send automatic updates without manual review and / or updates. The server may send the revised plan to the computing device. The revised plan may include instructions such as recommendations for different optimized swing equipment, explanations of any modifications, and / or messages from a third party. For example, an expert may indicate that the user is performing a particular athletic activity incorrectly, along with an instructional video that overlays a rendering of the user's movements captured in the data profile onto a template movement.

[0156]

[0183] In some embodiments, the template motion profile may be generated by recording a person performing an "ideal" template motion while wearing a sensor at a particular golf club location. However, in other embodiments, it may be desirable to electronically generate a motion profile for a particular motion, such as when a person is not available to perform the "ideal" motion. The systems and methods of the present invention allow a user (e.g., a sensor wearer) and / or a third party (e.g., an expert) to electronically create a motion profile without the need to record a person performing the template motion ideally or in person.

[0157]

[0184] The system and method of the present invention calculates the motion of the sensor device based on the swing motion of the user. In one embodiment, the computing device can determine the path taken by the sensor device. Based on the motion, the computing device can extrapolate the orientation and acceleration of the sensor device over time and generate a motion profile of the motion described by the user in a user interface and can be shown on the computing device (e.g., computer, tablet, smartphone).

[0158]

[0185] Some embodiments may use the orientation and displacement characteristics of the swing profile to calculate an angular velocity range across three dimensions. Some embodiments may consider the angular velocity as a pseudo-vector, such as having a magnitude (e.g., angular velocity) and a direction (e.g., axis of rotation based on the right-hand rule, etc.). For example, the disclosed embodiments may determine the velocity of the sensor device based on the recorded displacement data. The system may then combine the three-axis velocity information with the orientation data to derive the angular velocity (e.g., as a vector, as component parts of a vector). For example, the system may determine the angular velocity based on a cross product operation of the velocity and displacement information, etc.

[0159]

[0186] Other embodiments may utilize other calculations to identify significant variables, including angular velocity. Although the initial data received may not be normalized to a particular unit scale, certain embodiments may utilize the relative magnitude or variance of the data, including using vector components and magnitude ratios, to identify the swing profile.

[0160]

[0187] In some embodiments, the system may perform statistical calculations on the raw data profile and / or the calculated data profile. For example, the computing device may calculate the variance, mean, average magnitude, and / or standard deviation of each dimension of temporal data in a data profile (e.g., raw data profile, calculated data profile). In another example, the computing device may calculate the covariance between two dimensions of temporal data in a raw data profile, such as x-axis and y-axis acceleration data.

[0161]

[0188] The system matches the statistical calculations with expected statistical calculations for the potential body regions. In some embodiments, the computing device may calculate the difference (e.g., least squares difference, other statistical deviation measure) between the raw data and the template data for each swing motion. For example, the computing device may calculate the difference between the x-axis, y-axis, and z-axis mean, variance, and / or standard deviation and each template value for each swing motion. The system may identify the optimized equipment with the best match (e.g., lowest least squares difference).

[0162]

[0189] In various embodiments, the present invention encompasses systems and methods for matching data collected from sensor devices with stored data to match a swing profile with a preferred or optimized swing implement (e.g., a golf club or bat).

[0163]

[0190] In certain embodiments, the systems and methods of the present invention include measuring properties of a swing member according to various aspects of the present technology and may operate in conjunction with any suitable shaft and / or swing member. Various exemplary embodiments of the present technology may be applied to any system that measures the effect of an applied load and / or identifies physical properties of a swing member. The swing member may include any suitable device having a longitudinal axis, such as a shaft, beam, rod, tube, column, post, iron, etc. The test member may include a device that is swung during use, such as a golf shaft, baseball bat, tennis racket, hockey stick, etc.

[0164]

[0191] In various embodiments, a sensor device including one or more sensors may transmit data to a computing device, server, or any data acquisition system that may collect sensor data used in calculating one or more parameters of the test member, such as stiffness, linearity, torsional stiffness, and sound attenuation. The data acquisition system may include any suitable system or device that processes the data according to a desired criteria, such as a workstation, personal computer terminal, test station, etc. The data acquisition system may be integrated with the sensor device or may include a separate system communicatively linked to the sensor device. The data acquisition system may be configured to receive, translate, convert, or otherwise process any type of data stream into a format that may be used to calculate a desired property of the test member.

[0165]

[0192] For example, in one embodiment, the data acquisition system may include an analysis engine configured to compare the pre-load and post-load data from the sensor device to calculate a stiffness profile for the test member. The data acquisition system may also be configured to compare the pre-load data against a baseline standard to calculate a linearity value / score for the test member.

[0166]

[0193] The data acquisition system may also be appropriately configured to account for any distortion in the captured data, in one embodiment, the calibration process may account for tangential and radial distortions, including taking several swings to identify a calibration matrix.

[0167]

[0194] The user interface may provide an operator with control of and access to the test stand. The user interface may include any suitable device or system that allows a user or operator to execute test sequences, analyze results, access, copy or transfer test results, or otherwise use tests. In one embodiment, the user interface may include a display and an input device such as a keyboard or mouse. In a second embodiment, the display and input device may be combined into a single component such as a touch screen display, tablet, or table computer linked to a controller.

[0168]

[0195] In certain embodiments, the systems and methods of the present invention may be configured to measure the relative stiffness ("EI" or bending stiffness) of a test member, such as a rod, shaft, beam, or any other similar object. "E" represents the Young's modulus and "I" represents the second moment of inertia of the cross section of the test member. Both "E" and "I" have been found to be variable independently of each other along the length of a particular type of test member, such as a golf shaft. As a result, the sensor device may be configured to implement a technique for measuring the EI of a shaft, which calculates a continuous EI function over the length of the shaft by measuring how the shaft may bend under load, such as during one or more phases of a golf swing.

[0169]

[0196] In certain embodiments, the technique attempts to identify a continuous EI function by treating the golf shaft as a cantilever beam. However, traditional bending equations for a cantilever beam may not provide accurate test results because they generally contemplate a beam that undergoes small deflections under load and generally has a constant EI along the length of the beam under test. Therefore, a more complex model is used by the technique of the present invention to calculate the EI or stiffness profile of the entire shaft, based on data collected by sensors.

[0170]

[0197] In a particular embodiment, the model calculates EI as a function of x along the length of the shift associated with a corresponding deflection of the shaft along the vertical y direction.

[0171]

[0198] Thus, for a given deflection profile of the shaft, represented by the deflection in the x direction or in the y direction as a function of length along the shaft, the first and second derivatives may be determined and EI may be determined at any point along the shaft.

[0172]

[0199] In certain embodiments, to account for noise, a high order polynomial is fitted to the deflection profile according to the sensors of the shaft as it is deflected by the load system. The equation can then be solved at any given point along the shaft to calculate the overall EI profile of the shaft.

[0173]

[0200] The test method obtains continuous EI data over the length of the shaft and can more accurately replicate the way the shaft is loaded in a golf swing. This method allows for measurements along the entire length of the shaft between the first and second ends, unlike other methods such as the three-point bend method that use segmented lengths of material. This method also reduces inconsistencies associated with operator-based testing, such as those that may be introduced as a result of an operator using their eyes to read the gauge or moving the material by the operator during testing.

[0174]

[0201] Using the sensor device of the present invention, a data acquisition system may generate a deflection (or bending) profile of the shaft with reference to deflection data established by a straightness calibration acquired by a calibration rod or a straightness line from a straightness profile of the shaft. A polynomial may be fitted to each of the top and bottom data to generate fitted top and bottom values ​​for each x location value. In certain embodiments, the fitted top and bottom values ​​may be averaged and the corresponding y location value from the straightness calibration may be subtracted to obtain an average deflection for each point along the shaft. This average deflection may be converted to deflection meters using a predetermined calibration coefficient. Another polynomial may be fitted to the average deflection data to create a number of polynomial coefficients. The generated coefficients may be saved, exported, or otherwise collected to recreate the calculated deflection profile of the shaft.

[0175]

[0202] Using information related to the applied load, sensor data, deflection data, derivatives, and calculation set, an EI profile may be calculated by the analysis system for each point, pixel location, or other desired criteria along the length of the shaft. For example, the data acquisition system may then calculate the first and second derivatives of a polynomial fitted to the average deflection data using any suitable method. The data acquisition system may then obtain the amount of force being applied to the shaft from the load cell and convert it to a moment as a function of the x location value along the shaft in Newton meters. At each point along the shaft, the EI may be calculated and then such calculations may be used to match an optimized swing implement to the user's swing profile.

[0176]

[0203] A motion analysis system and method (eg, a golf swing analysis device) according to an exemplary embodiment of the present invention can provide a variety of golf swing analysis information to a user by calculating golf club speed and golf ball speed.

[0177]

[0204] In a particular embodiment, to calculate the speed of the golf club and the speed of the golf ball, the golf swing analysis device uses a Doppler radar sensor to emit a transmission wave to the golf club and the golf ball and detect a reflected wave, where the reflected wave from the golf club and the reflected wave from the golf ball are mixed. The Doppler radar sensor detects the frequency shift between the transmission wave and the mixed reflected wave and generates an output signal. The output signal generated by the Doppler radar sensor is a mixture of a signal related to the speed of the golf club and a signal related to the speed of the golf ball.

[0178]

[0205] The sensor system of the present invention calculates golf club swing speed, golf ball speed, carry distance, and hit accuracy by using output signals generated by a Doppler radar sensor that may be incorporated into the systems and devices of the present invention.

[0179]

[0206] In various embodiments, the data acquired by the sensor may be analyzed by a Fast Fourier transform of the signal output from the Doppler radar sensor in accordance with an exemplary embodiment of the present invention.

[0180]

[0207] In certain embodiments, the swing analysis system and device according to an exemplary embodiment of the present invention includes a signal detection unit, a signal pre-processing unit, a swing analysis information calculation unit, a display unit, an input unit, and a memory unit.

[0181]

[0208] In a particular embodiment, the sensor uses a radar to emit a transmission wave to the golf club and / or the golf ball. The sensor detects the reflected wave from the golf club and the reflected wave from the golf ball, detects the frequency shift between the transmission wave and the mixed reflected wave, and generates an output signal. For example, the signal detection unit may include a Doppler radar sensor that uses the Doppler effect.

[0182]

[0209] The output signal of the signal detection unit is a signal related to the speed of the golf club and the speed of the golf ball. The signal pre-processing unit removes noise from the output signal generated by the signal detection unit, converts the analog signal, which is the output signal from which the noise has been removed, into a digital signal, and provides the converted digital signal to the swing analysis information calculation unit.

[0183]

[0210] For example, the signal pre-processing unit may provide an output signal to the swing analysis information calculation unit, where the horizontal axis represents the index of samples of the fast Fourier transform, and the vertical axis represents a signal, as a digital value, that is a mixture of signals related to the speed of the golf club and the speed of the golf ball.

[0184]

[0211] The swing analysis information calculation unit calculates the speed of the golf club and the speed of the golf ball by using the output signal of the signal detection unit.

[0185]

[0212] To calculate the speed of the golf club and the speed of the golf ball, the swing analysis information calculation unit may include a ball speed calculation unit and a club speed calculation unit. The ball speed calculation unit calculates the speed of the golf ball based on the frequency components obtained by converting the output signal of the signal detection unit into the frequency domain. Since the speed of the golf ball is the highest at the moment when the user hits the golf ball, the ball speed calculation unit may calculate the speed of the golf ball by using the highest frequency value in the output signal of the signal detection unit.

[0186]

[0213] One or more additional embodiments include a multi-sensor event analysis and tagging system. An embodiment of the present invention allows for intelligent synchronization and transfer of generally concise event videos synchronized with motion data from motion capture sensors coupled to a user or equipment. In certain embodiments, the sensors of the present invention conserve storage and upload event videos and increase upload speeds by avoiding uploading non-relevant portions of large videos. In certain embodiments, the sensors of the present invention provide for intelligent selection of multiple videos from multiple cameras covering an event at a given time, for example, selecting the video with the least vibration. In certain embodiments, the sensors of the present invention allow for near real-time modification of camera parameters determined by the motion capture sensors during the event, and modification of playback parameters and special effects of the synchronized event videos. Highlight reel creation filtered by metrics, sortable by metrics. In certain embodiments, the sensors of the present invention integrate with multiple sensors to store event data even if other sensors do not detect the event. In certain embodiments, the sensors of the present invention also allow for analysis or comparison of movements associated with the same user, other users, past users, or groups of users. At least one embodiment provides intelligent recognition of events in motion data, including but not limited to motion capture data obtained from portable wireless motion capture elements such as visible markers and sensors, radio frequency identification tags, and mobile device computer systems, or calculated based on analyzed movements associated with the same user, or compared against the user or another user, past users, or user groups. Cropping the motion data and video to correspond to detected events allows for reduced memory utilization for event and video data. This may be performed on the mobile device or a remote server based on the location and / or time of the event, as well as the location and / or time of the video, and may optionally include camera orientation to further limit the video that may include the motion event.The embodiment allows for event-based display, low power transmission of events, and communication with running apps on the mobile device and / or external cameras to define the events, allowing recognition of athletic events and designation of events in an image or video such as a player's shot, movement, or swing, an impact of a player, boxer, rider, or driver, or heat stroke, hypothermia, stroke, asthma attack, epileptic attack, or any other sports or physical athletics involving walking and falling. Events may be correlated with one or more images or videos captured from one or more internal / external cameras or babysitter monitoring cameras to allow for storage of events such as, for example, a child's first steps, a violent shaking event, a sports event involving an impact, or a fall event associated with an elderly person. Impact-related events and other events may be monitored for linear acceleration thresholds and / or patterns and rotational acceleration and velocity thresholds and / or patterns and / or stored on an event-by-event basis and / or transferred via a lightweight connectionless protocol or any combination thereof to generate integrated athletic metrics using sensor fusion of motion capture sensor data and athletic data derived from video analysis.

[0187]

[0214] An embodiment of the present invention allows a user to purchase an application or "app" and a motion capture element and immediately utilize the system with an existing mobile computer, e.g., a mobile phone. An embodiment of the present invention may display athletic information to a user who is monitoring or associated with a motion capture element or equipment. An embodiment may also display information based on previously stored motion capture data or motion analysis data associated with the user or equipment, or based on previously stored motion capture data or motion analysis data associated with at least one user (through functionality such as, but not limited to, comparison). This allows for advanced monitoring, compliance, and interaction with actual motion capture data or patterns, such as, for example, playing a virtual game using actual motion data captured from a user, and responses are generated based thereon using actual motion data captures from the user previously or from other users (or equipment). This capability provides playing with past players, such as playing a virtual tennis match or playing with an "average" professional athlete, and is heretofore unknown in the art.

[0188]

[0215] For example, one or more embodiments include at least one motion capture element configured to couple to a user, equipment, or a mobile device coupled to the user, the at least one motion capture element including a memory, a sensor configured to capture any combination of values ​​associated with an orientation, position, velocity, acceleration (linear and / or rotational) of the at least one motion capture element, a radio, and a microcontroller coupled to the memory, the sensor, and the radio. The microcontroller is configured to collect data including sensor values ​​from the sensor, store the data in the memory, analyze the data to recognize events in the data to identify event data, and transmit the event data associated with the event via the radio. An embodiment of the system may include an application configured to run on the mobile device, the mobile device including a wireless communication interface configured to communicate with the computer, the radio to obtain the event data associated with the event. A computer is coupled to the wireless communication interface, and the computer executes an application or "app" to configure the computer to receive event data from the wireless communication interface, analyze the event data to form motion analysis data, store the event data, the motion analysis data, or both the event data and the motion analysis data, and display information including the event data, the motion analysis data, or both associated with at least one user on a display.

[0189]

[0216] One or more embodiments include at least one motion capture sensor configured to be positioned near the user's head, and the microcontroller is further configured to calculate a location of an impact to the user's head. The at least one motion capture sensor embodiment may be configured to be coupled to a hat or cap, within a protective mouthpiece, using any type of mount, enclosure, or coupling mechanism. One or more embodiments of the at least one motion capture sensor may be configured to be coupled to a helmet on the user's head, and the calculation of the location of the impact to the user's head is based on the physical geometry of the user's head and / or helmet. An embodiment may include, for example, a temperature sensor coupled to the at least one motion capture sensor or the microcontroller.

[0190]

[0217] An embodiment of the present invention may utilize an isolator that surrounds at least one motion capture element and is configured to approximate the physical acceleration damping of the cerebrospinal fluid surrounding the user's brain to minimize translation of linear and rotational acceleration of the event data and obtain observed linear and rotational acceleration of the user's brain. Thus, the embodiment may eliminate the process of translating the acceleration-based forces, acceleration values, or any other values ​​from the helmet into observed brain acceleration values. Thus, the embodiment reduces the power and storage utilized to provide the event-specific data, thereby minimizing the amount of data transfer, thereby lowering the transmission power utilized and even lowering the total power utilized. On a football / hockey / lacrosse player's helmet, different isolators may be utilized based on the type of padding inherent to the helmet. Other embodiments utilized in sports where helmets are not worn or are worn occasionally may also utilize at least one motion capture sensor on a cap or hat, e.g., on a baseball player's hat, along with at least one sensor mounted on a batter's helmet. In sports where caps are not utilized, such as soccer, a headband mount may be utilized, again to identify impacts. In one or more embodiments, the isolator utilized in the helmet may remain in an enclosure attached to the helmet and the sensor may be removed and placed on another piece of equipment that does not utilize an isolator, consistent with a drop in the user's brain fluid.Embodiments may automatically detect the type of motion and identify the type of equipment to which the motion capture sensor is currently attached based on characteristic motion patterns associated with a particular type of equipment, i.e., a surfboard versus a baseball bat.

[0191]

[0218] An embodiment of the invention may be configured to obtain / calculate linear acceleration values, rotational acceleration values, or both. This allows rotational events to be monitored for impact, as well as linear acceleration. Other events may utilize linear and / or rotational acceleration and / or velocity, for example, compared against patterns or templates to not only switch sensor features during an event to dynamically change capture characteristics, but also to characterize the type of equipment currently being utilized with the current motion capture sensor. This allows a single motion capture element purchased by a user to automatically identify the type of equipment or garment to which the sensor is coupled, based on the motion captured by the sensor, when compared against a characteristic pattern or template of motion.

[0192]

[0219] Embodiments of the invention may use connectionless broadcast messages to transmit event data associated with an event. In one or more embodiments, depending on the wireless communication employed, the broadcast message may include a payload that limits the amount of data available to avoid handshaking and overhead of connection-based protocols. In other embodiments, any combination of connectionless or connection-based protocols may be utilized.

[0193]

[0220] In one or more embodiments, the computer may access previously stored event data or motion analysis data associated with the user or equipment to identify, for example, the number of impacts, falls, other swings, or any other motion events. An embodiment may present event data associated with at least one user on a display based on the event data or motion analysis data associated with the user or equipment and previously stored event data or motion analysis data associated with the user or equipment or associated with at least one other user or other equipment. This allows for a comparison of the number or quantification of motion events, for example, maximum rotational acceleration observed by the user or other users in a particular match or in the past. In addition, patterns or templates defining the characteristic motion of a particular equipment for a typical event may be dynamically updated, for example, at a central server or locally, and in one or more embodiments, at the motion capture sensor via a wireless interface. This allows the sensor to improve over time.

[0194]

[0221] An embodiment of the invention may transmit information to a computer or a remote computer, for example, for display on a visual display coupled with broadcast television or the Internet. An embodiment of the display may be configured to accept sub-event time locations to provide a discrete scroll through the timeline of the overall event. For example, a golf swing may include sub-events such as address, swing back, swing forward, strike, follow through, etc. The system may display the time location of the sub-events and accept user input near the location to assert that the video should start or stop at that point, or to scroll or return to that point, for example, to facilitate viewing of the sub-events.

[0195]

[0222] An embodiment of the invention may include an identifier associated with at least one motion capture sensor, user, or equipment. In one or more embodiments, the identifier may include a team and jersey number, a student identifier number, a license number, or any other identifier that allows for a relatively unique identification of a particular event from a particular user or equipment. This allows for identification of a sport or location of a team having multiple players or users for an app configured to receive data associated with a particular player or user. One or more embodiments receive an identifier associated with a player or user, such as a passive RFID identifier, address, or other serial number, and associates the identifier with the event data and the motion analysis data.

[0196]

[0223] One or more embodiments of the at least one motion capture element may further include a light emitting element configured to output a light if an event occurs. This may be utilized, for example, to indicate a potential mild or severe level of impact to the exterior of the helmet without requiring any communication to any external device. Different colors or blink intervals may be utilized to relay information related to the event. Alternatively or additionally, the at least one motion capture element may further include an audio output element configured to output a sound if an event occurs, if the at least one motion capture sensor is out of range of the computer, if the computer is configured to indicate and alert when the at least one motion capture sensor is out of range of the computer, or any combination thereof. An embodiment of the sensor may utilize an LCD that outputs an analysis of the current event, for example, encoded in a Quick Response (QR) code or barcode, so that, for example, a referee may take a snapshot of the analysis locally on a mobile device, so that the event is not seen in readable form on the sensor or wirelessly transmitted to be intercepted by someone else.

[0197]

[0224] In one or more embodiments, at least one motion capture element further includes a location determination element coupled to the microcontroller, which may include, for example, a GPS (Global Positioning System) device. Alternatively or additionally, the computer may triangulate the location in question with another computer, obtain the location from any other triangulation type receiver, or calculate the location based on images captured via a camera coupled to the computer and known to be pointed in a particular direction, where the computer calculates an offset from the mobile device based, for example, on the orientation and size of objects in the image.

[0198]

[0225] In one or more embodiments, the computer is further configured to request at least one image or video including the event from at least one camera in the vicinity of the event. This may include a broadcast message requesting video from a particular nearby camera or cameras pointed in the direction of the event. In one or more embodiments, the computer is further configured to broadcast a request for camera locations in the vicinity of the event or cameras pointed to view the event, and optionally display video from available cameras or cameras spanning a duration before and after the event of interest. In one or more embodiments, the computer is further configured to display a list of one or more times that the event occurred, thereby allowing a user to obtain a desired event video via the computer and / or independently request a video having a desired event time from a third party.

[0199]

[0226] In one or more embodiments, at least one motion capture sensor is coupled to the mobile device, for example using an internal motion sensor in or coupled to the mobile device. This allows for motion capture and event recognition with minimal ubiquitous hardware, for example using a mobile device with a built-in accelerometer. In one or more embodiments, a first mobile device may be coupled to a user recording motion data, while a second mobile device is utilized to record a video of the motion. In one or more embodiments, a user undergoing motion may make a gesture, for example tapping N times on a mobile device, to indicate that the second, user's mobile device should start or stop recording. Any other gesture may be utilized to communicate event-related or motion-related instructions between mobile devices.

[0200]

[0227] At least one embodiment of the motion capture sensor may include a temperature sensor or the microcontroller may be otherwise coupled to a temperature sensor, in which the microcontroller is configured to transmit the temperature obtained from the temperature sensor as a temperature event, for example as a potential indication of heat stroke or hypothermia.

[0201]

[0228] Embodiments of the invention may recognize any type of motion event, including motion-related events associated with at least one motion capture sensor coupled with any combination of a user, equipment, or mobile device, or motion-related events indicative of standing, walking, falling, heat stroke, seizures, violent shaking, impact, collision, gait abnormalities, breathing abnormalities or no breathing, any combination thereof, or any other type of event having a duration during which motion is occurring.

[0202]

[0229] An embodiment of the present invention may utilize data mining on motion capture data to obtain patterns of users, equipment, or use the motion capture data or events of a given user or other users in certain embodiments of the present invention. Data mining involves the discovery of new patterns in large databases where the patterns were previously unknown. Many methods may be applied to the data to discover new patterns, including, for example, statistical analysis, neural networks, and artificial intelligence. Due to the large amount of data, automated data mining may be performed by one or more computers to find unknown patterns in the data. The unknown patterns may include groups of related data, anomalies in the data, dependencies of data elements, classifications and functions that model the data with minimal error, or any other type of unknown pattern. The display of the data mining results may include a summarized display of the newly discovered patterns that is easier for the user to understand than a large amount of pure raw data. One outcome of the data mining process is improved market research reports, product improvements, lead generation, and targeted sales. In general, any type of data that undergoes data mining is cleansed and data mined, and the results are generally verified. Businesses may use data mining to make profits. Examples of benefits of embodiments of the present invention include highly targeted customer relationship management based on patterns discovered in the data. Additionally, market basket analysis data mining allows products purchased or owned by the same individual to be identified, which can be used to offer products to users who own one product but do not own other products typically owned by other users.

[0203]

[0230] Other areas of data mining include analyzing magnitude sets of motion data from different users to improve performance based on performance data from other users. For example, if a user rotates their hips less during the swing compared to the average user, exercises to improve flexibility or strength may be suggested by the system. In a golf course embodiment, a golf course planner may determine which hole length or difficulty should be adjusted to obtain more discrete values ​​for the average number of shots per hole across a large number of users on a golf course, or to identify the amount of time between golfers, for example, at a particular date and time or for golfers of a particular age. Additionally, sports and medical applications of data mining include identifying morphological changes over time in user performance versus, for example, diet or exercise changes to determine what improves performance the most or, for example, which date, temperature, or other conditions produce the swing events that lead to the furthest drives, i.e., the lowest scores. The use of motion capture data for a particular user or for other users can enable medical compliance, for example ensuring that a person with diabetes moves a certain amount on a given day, as well as morphological analysis to identify how a user's movement or range of motion has changed over time. Matches can be played using motion capture data that allows virtual reality matches against past greats or other users. For example, one may want to play against the same person's previous performances or against the motion capture data of a friend. This allows a user to play in a stadium or venue in a virtual reality environment from the past, but using motion capture data previously obtained from the user or other users, for example. Military planners can use the motion capture data to determine which military personnel are best suited and therefore eligible for special operations or who should be discharged, or coaches can determine when a player should rest, for example based on the impact event and its severity suffered by the player, and potentially based on mined time periods when other users performed well after the impact-related event.

[0204]

[0231] An embodiment of the system performs motion capture and / or display using an application running on a mobile device that may include, for example, a visual display and optionally a camera, and that is capable of acquiring data from at least one motion capture element, such as a visible marker and / or a wireless sensor. The system may be integrated with a standalone camera or multiple cameras on multiple mobile devices. The system also allows a user to analyze and display the motion capture data in a variety of ways that provide immediate and easy to understand graphical information associated with the motion capture data. The motion capture elements utilized in the system intelligently store data associated with events, such as, for example, hitting a ball, performing a ski turn, jumping, etc., eliminating false events, greatly improving memory usage, and minimizing storage requirements. In addition, for example, data for more than one event associated with a sports equipment, such as multiple bat swings or an entire golf round, may be stored, if necessary, at least until the data is downloaded to the mobile device or the Internet. Data compression of the captured data may be utilized to store more motion capture data in a given amount of memory. Motion capture elements utilized in the system may be configured to intelligently power down portions of the circuitry to conserve power, for example, powering down a transceiver until a particular type of movement is detected. Embodiments of the invention may utilize flexible battery connectors to couple two or more batteries in parallel to increase the amount of time the system may be utilized before a battery is replaced. Motion capture data is typically stored in a memory, such as a local database or a network accessible database, both of which enable data mining as described above. Embodiments of the invention may be used to perform any other type of data mining, including searching for changes in data over time related to one or more users and / or simply searching for data related to a particular user or equipment.

[0205]

[0232] Other embodiments may display information such as music selections or music playlists to be played based on the athletic-related data, for example to allow other users to compare their performance with that of other users to select the type of music to play, or to compare their performance to thresholds that determine what type of music selections to suggest or display.

[0206]

[0233] For example, sports-directed embodiments of the invention allow RFID or passive RFID tags to be placed on items that a user moves, and system embodiments track the movement. For example, by placing passive RFID tags on certain helmets or caps or protective mouthpieces in boxing, football, soccer, or other contact sports, or on dumbbells in a gym, wearing motion capture elements such as gloves, and using existing mobile devices, such as an IPHONE.RTM., embodiments of the invention provide automatic safety compliance or fitness and / or health care compliance. This is accomplished by tracking the movement and tracking the weight the user is lifting via RFID or passive RFID. Thus, embodiments of the invention may add up the number of repetitions multiplied by the weight indicated by each RFID tag to calculate the number of calories burned by the user. In another example, RFID tags coupled with a stationary bike, or where the stationary bike can mimic and / or wirelessly communicate identifiers to provide performance data, where a mobile computer includes an RFID reader and can count the number of foot revolutions of the user. Any other use of RFID or passive RFID is within the spirit of the invention. This allows a physician to remotely determine whether a user has complied with the physician's medical recommendations or has exceeded linear or rotational accelerations indicative of an impact, for example. Thus, embodiments can be used by users to ensure compliance and by physicians to lower insurance rates, albeit remotely, because they are ensuring that the patient is following the physician's recommendations. Although embodiments of the invention do not require RFID tags for medical compliance, RFID tags may be used.An embodiment of the invention directed to golf also allows for golf shots to be counted for each club associated with a golfer through the use of an identifier such as an RFID tag on each club and a mobile computer equipped with an RFID reader, e.g., !PHONE or IWATCH, that centralizes the processing of golf shot counting to the mobile computer instead of each golf club (or optionally obtained remotely via an identifier associated with motion capture electronics on the golf club or via wireless). An embodiment of the invention also allows for the measurement of orientation (north / south and / or two horizontal and vertical axes) and acceleration using an inertial measurement unit or accelerometer and / or magnetometer and / or gyroscope. While this is not necessary for golf shot counting, one or more embodiments may identify when the golf club strikes the golf ball, e.g., through vibration analysis, and then query the golfer whether or not to count the shot. This functionality may be combined with speed or acceleration thresholds or range detection to determine, for example, whether the golf club was traveling within an acceptable speed or range or acceleration or range for counting a "hit". Wavelets may be utilized to compare valid swing signatures to eliminate counting shots or to eliminate false strikes. The ranges may vary between different clubs, e.g., the driver speed range may be "over 30 mph" while the putter speed range may be "under 20 mph", any range may be utilized with any club desired, or the speed range may be ignored, for example. Alternatively or in combination, the mobile computer may count shots or query the golfer only if the golfer is not moving laterally, i.e., in a golf cart or walking, and / or if the golfer has rotated or may have taken a shot as determined by an orientation or gyroscope sensor coupled to the mobile computer. The location of the stroke may be shown, for example, on a map on the mobile computer.Additionally, GPS receivers with wireless radios may be placed in the tee markers and in the cups to provide daily updates of driving distances and aid in, for example, reading putts and greens. The golfer may wear virtual glasses that allow the golfer to view the golf course map, current location, distance to hole, number of shots on the current hole, total number of shots, and any other desired metrics. If the user travels a certain distance, as determined by GPS, from a shot without counting the shot, for example, the system may prompt the user as to whether or not the shot should be counted. The system does not require the user to turn on the club to count the shot, and does not require LEDs or active or battery-powered electronic circuitry on each club to count the shot. The mobile computer may also accept gestures from the user to count or not count shots, so that the golfer does not have to remove any gloves to operate the mobile computer. In an embodiment that utilizes a position / orientation sensor, the system may count shots only when the club is oriented vertically, for example, only when impact is detected. The device may include an identifier that allows a particular device to be identified. The identifier may be, for example, a serial number. The identifier may originate, for example, from an RFID tag on each golf club, or may optionally include a serial number or other identifier associated with a motion capture element associated with the golf club. Utilizing this apparatus allows for identification of a particular golfer, a particular club, and allows for motion capture and / or display using a system including a television and / or mobile device having a visual display, and an optional camera, capable of acquiring data from at least one motion capture element, such as a visible marker and / or a wireless sensor. The system may be integrated with a standalone camera or multiple cameras on multiple mobile devices.The system also allows the user to analyze and display the motion capture data in a variety of ways that provide instant, easy to understand graphical information related to the motion capture data. The device also allows the system to identify how "centered" the impact was for the ball and the equipment, such as a golf ball. The system also allows for fitting of equipment, including shoes, clubs, etc., and instant purchase of equipment, even if the equipment requires a custom build-to-order request from a vendor. Once the motion capture data, video, or images, and shot count instructions are acquired by the system, they may be stored locally, such as in a local database, or may be transmitted, such as via a telephone or wireless interface, to a remote database. Once in the database, various factors, including any data associated with the user, such as age, sex, height, weight, address, income, or any other relevant information, may be utilized in embodiments of the present invention and / or may be subject to data mining. One or more embodiments allow the user or OEM to pay a fee, for example, to access the data mining capabilities of the system.

[0207]

[0234] For example, embodiments utilizing motion capture elements may allow data obtained from the device to be analyzed and may allow for the presentation of unique displays associated with the user, such as 3D overlays on an image of the user's body, to visually indicate the captured athletic data. Additionally, these embodiments may utilize active wireless technologies, such as BLUETOOTH low energy, with a range of up to 50 meters, to communicate with the golfer's mobile computer. An embodiment of the present invention may also allow for a query to count strokes to be displayed as a result of receiving, for example, a golf club ID, for example, via an RFID reader, or alternatively, via wireless communication using, for example, BLUETOOTH or IEEE802.11. The use of a BLUETOOTH low energy chip allows the club to be in sleep mode for up to three years using a standard coin cell battery, thus reducing the maintenance required. One or more embodiments of the present invention may utilize, for example, two or more radios of two or more technologies. This allows for a level of redundancy that increases the robustness of the system. For example, if one radio, e.g., a BLUETOOTH radio, no longer functions, the IEEE 802.11 radio may be utilized to transfer data and alert the golfer that one of the radios is not functioning while still allowing the golfer to record athletic data and count shots associated with a particular club. In embodiments of the present invention utilizing a mobile device without a camera (or more than one mobile device), sensor data may be utilized to generate a display of the captured athletic data, although the mobile device may optionally obtain images from other cameras or other mobile devices with cameras. For example, display types that may or may not utilize a user's image may include ratings, calculated data, and time series data. Ratings associated with a captured athletic performance, e.g., an "efficiency" rating, may be displayed to the user in the form of numerical or graphical data with or without a user image. Other ratings may include, for example, linear and / or rotational acceleration values ​​to identify impacts or other events.Calculated data such as predicted ball flight path data may be calculated and displayed on the mobile device with or without the use of an image of the user's body. The data shown on the time series may also be displayed with or without the image of the user to show, for example, relative peaks in the speed of various parts of the equipment or the user's body. For example, images from multiple cameras including multiple mobile devices from a crowd of golf fans may be combined to create a bullet time visual effect featuring slow motion of a golf swing shown from all around the golfer at different angles at normal speed. All analyzed data may be displayed locally or uploaded to a database along with motion capture data, images / videos, shot counts, and location data that may be subjected to a data mining process, and the system may, for example, charge a fee to access the results.

[0208]

[0235] In one or more embodiments, a user may, for example, play a golf course or hit a tennis ball, or alternatively, simply swing to generate motion capture data, and when wearing the virtual reality glasses, may see another user's avatar, whether virtual or real, in the augmented reality environment. In other embodiments, a user may move equipment associated with any sport, or simply move the user's own body coupled with a motion capture sensor, and see a virtual reality environment of the user's movements, or the movements of the equipment so equipped, displayed in the virtual reality glasses. Alternatively or in combination, a virtual reality room or other environment may be utilized to project the virtual reality avatar and motion data. Thus, an embodiment of the system may enable a user on an actual golf course to play with another user at a different location who has not actually hit a ball, a past player whose motion data has been analyzed, or a user whose motion data has been constructed by data mining based on one or more motion capture data sequences and utilized by an embodiment of the system to project the past player's avatar. Each of the three players may then play as if they were in the same location.

[0209]

[0236] The motion capture data and / or events can be displayed, e.g., posted to Twitter, on social networks in many ways during or after motion capture. For example, if a certain amount of exercise or movement has been performed, or calories burned, or a new sports power factor maximum has been achieved, the system can automatically tweet the new information to a social network site so that anyone with an Internet connection can be notified. In one or more embodiments, the motion capture data, motion analysis, and video can be transmitted to one or more social media sites, repositories, databases, servers, other computers, viewers, displays, other mobile devices, emergency services, or public agencies. Data uploaded to the Internet, i.e., remote databases, remote servers, or remote memories from the system, can be viewed, analyzed, or data mined by any computer that can gain access to the data. This allows remote compliance tweets, compliance, and / or original equipment manufacturers to determine which equipment is compliant for a given user, or in sports-related embodiments, which sports equipment is performing best and / or which equipment to suggest. Data mining also allows for suggestions to users to improve compliance and / or planning of sporting venues, including golf courses, based on data and / or metadata associated with the user, such as age or any other demographics that may be entered into the system. Remote storage of data also allows for medical applications, such as morphological analysis, range of motion over time, diabetes prevention, exercise monitoring, and compliance applications, as mentioned. Other applications also allow for games that use actual motion capture data from other users or past players, living or deceased, after analyzing their video. Virtual reality and augmented virtual reality applications may utilize motion capture data or past motion data.Military personnel such as commanders and / or medics may use the motion and / or imagery to identify what type of G-force a person experienced from a nearby explosion of an IED and automatically direct the best type of medical aid to the location of the motion capture sensor. One or more embodiments of the system may relay the motion capture data to a commander or the nearest medical personnel via, for example, a wireless communication link via G-force or velocity thresholds. Alternatively, embodiments of the invention may broadcast a lightweight connectionless impact related message to any mobile device listening, for example to a referee's cell phone, to help assist the injured player, the lightweight message including an optional team / jersey number and acceleration related number such as a potential / likely impact warning or indicator.

[0210]

[0237] In one or more embodiments of the present invention, fixed cameras at tennis tournaments, football games, baseball games, car or motorcycle races, golf tournaments, or other sporting events, etc., can be utilized with wireless interfaces placed near players / equipment having motion capture elements to capture, analyze, and display motion capture data. In this embodiment, real-time or near real-time motion data can be displayed on the video for extended video replay. Thus, an increased entertainment level is created by visually displaying how fast the equipment is moving during a shot, e.g., with the rings drawn around the player's waist and shoulders. An embodiment of the present invention also makes images or videos from other players with mobile devices available on a mobile device associated with another user, e.g., so that the user does not have to switch mobile phones. In one embodiment, video captured by a first user of the sports equipment in motion, not associated with a second user with a mobile phone equipped with a video camera, can automatically forward the video to the first user for display with the motion capture data associated with the first user. The videos and images can be uploaded to a database and data mined through image analysis to identify, for example, the type / color of clothing or shoes the user is wearing.

[0211]

[0238] Based on the display of the data, the user can determine the best-fitting equipment via the mobile data and instantly purchase the equipment. For example, when deciding between two sets of skis, the user can try both sets equipped with motion capture elements and the motion capture data can be analyzed to determine which set of skis allows for more efficient movement. In a golf embodiment, when deciding between two golf clubs, the user can swing with the different clubs and quantitatively determine which club performs better based on an analysis of the captured motion data. Through an interface on the mobile device, custom equipment may be ordered from a vendor that can, for example, take orders for customer-built equipment, assemble the equipment, and ship the equipment to the user. For example, the shaft length of a standard length putter can be custom-made for a particular user based on captured motion data, such as the user putting with an adjustable length shaft. For example, based on data mining of motion capture data, shot count data, and driving distance, for example, users with similar swing characteristics can be compared to the current user, and equipment that will deliver greater driving distance at a given swing speed for a user of a particular size and age can be suggested or searched for to improve performance. OEMs can also determine which make and model of club will deliver the best overall performance at a given swing speed. One skilled in the art will recognize that this is true not only for golf, but for all athletic activities.

[0212]

[0239] Embodiments of the system may utilize a variety of sensor types. In one or more embodiments of the invention, active sensors may be integrated into the system that utilize passive or active visible markers to allow the capture of the movement of specific points on the user's body or equipment. This may be performed simply in two or three dimensions, where two or more cameras are configured on the mobile device, or where multiple cameras or mobile devices are utilized to share images to create triangulated three-dimensional movement data from a set of two-dimensional images acquired from each camera. Another embodiment of the invention may utilize an inertial measurement unit (IMU) or any other sensor capable of generating any combination of orientation, position, velocity, and / or acceleration information for the mobile device. Thus, the sensors may acquire data that may include any combination of one or more values ​​related to orientation (vertical or north / south or both), position (through Global Positioning System, or "GPS," or through triangulation), velocity (in all three axes), and acceleration (in all three axes). All motion capture data obtained from the various sensor types, regardless of sensor type, may be stored in a database for, for example, analysis, monitoring, compliance, game play, or other uses, and / or data mining.

[0213]

[0240] In one or more embodiments of the present invention, sensors may be utilized that include passive or active markers on the exterior surface of the sensor, so that the sensor can also be utilized for visual tracking (either 2-dimensional or 3-dimensional) and for orientation, position, velocity, acceleration, angular velocity, angular acceleration, or any other physical quantity generated by the sensor. Visible marker embodiments of motion capture elements may be passive or active, meaning that they may have a visual portion that is visually trackable or may include light-emitting elements such as light-emitting diodes (LEDs) that allow image tracking under low light conditions. This may be implemented, for example, with graphical symbols or color markers at the end of the shaft near the handle or the opposite end of the golf club at the club head. Images or videos of the markers may be analyzed locally or stored in a database for analysis and then utilized for data mining. Additionally, in impact-related embodiments, the visible marker may emit a light indicative of an impact, for example, a flashing yellow light for a mild impact, a flashing red light for a severe impact, or any other visual or optionally audible event indicator or both. As discussed above, the LCD may output a local visually coded message such that it is not intercepted or otherwise readable by those not locally equipped with a mobile device to read the code, so that, for example, confidential medical messages for impact or paralysis related events can only be read by a referee or local medical personnel.

[0214]

[0241] Embodiments of the motion capture sensor may generally be mounted on or near one or more ends of sports equipment, such as anywhere on and / or between golf clubs (in the case of EI measurements), integrated with other sensors coupled to equipment such as weapons, medical devices, wristbands, shoes, shirts, gloves, clubs, bats, rackets, balls, helmets, caps, mouthpieces, and / or attached to a user in any possible manner. For example, a rifle to determine where the rifle is pointing when recoil is detected by the motion capture sensor. This data may be transmitted to a central server using, for example, a mobile computer such as a cell phone or other device, and analyzed, for example, for military exercises. Additionally, one or more embodiments of the sensor may fit into the weight port of a golf club and / or the handle end of a golf club. Other embodiments may fit into the handle or end of, for example, a tennis racket or baseball bat. Safety or health-related embodiments may be coupled to a cap, helmet, and / or mouthpiece and / or in any other type of enclosure. One or more embodiments of the present invention may similarly work with a ball with an integrated sensor. One or more embodiments of the mobile device may include a small mountable computer such as a !PHONE or !WATCH, or may not have an integrated display, which is mounted on the shaft of the sports equipment and is small enough not to affect the user's swing. Alternatively, the system may calculate the virtual flight path of the ball as it comes into contact with the equipment moved by the player. In the example using a golf club with a sensor integrated into a weight port on a baseball bat, or tennis racket, or other part of the end of the club that strikes the golf ball, and a second sensor located at the tip of the golf club handle or in one or more gloves worn by the player, the angle of impact of the club may be calculated. Knowing the loft of the club face, the flight angle of the golf ball may be calculated.Additionally, the quality of the impact can be determined by sampling the sensor at the end of the club fast enough to determine vibrations indicative of where the golf ball was struck on the club face. These types of measurements and their analysis can aid in the improvement of the athlete and, for fitting purposes, allow the athlete to immediately purchase equipment that fits correctly. The centering data can be uploaded to a database and data mined for patterns associated with bats, racquets, or clubs that have the best centering or lowest twist values ​​on average for product improvement on a manufacturer basis, for example. Any other unknown patterns in the discovered data can be presented or suggested to the user, searched for by the user, or paid for, for example, by the manufacturer or user.

[0215]

[0242] One or more embodiments of the sensor may include a charging feature, such as a mechanical eccentric weight, as utilized in some watches known as "self-winding" or "hand-winding" watches, which optionally include a small generator or dielectric fill coil for indirect electromechanical charging of the sensor power supply. Other embodiments may utilize a wall outlet for direct charging of the sensor power supply, or may utilize an electromechanical or microelectromechanical (MEMS) based charging element. Any other type of power micro harvesting technology may be utilized in one or more embodiments of the present invention. One or more embodiments of the sensor may include a power saving feature, including a gesture to power the sensor on or off. Such gestures may include motion, physical switching, contact with the sensor, or a wireless command to the sensor from a mobile device associated with the particular sensor, for example. Other elements that may be coupled to the sensor include, for example, a battery, a low power microcontroller, an antenna and radio, a heat sync, a charger, and an overcharge sensor. Additionally, embodiments of the present invention allow for the powering down of some or all of the components of the system until the club is determined to have been moved, for example, by an electronic signal from an accelerometer or a mechanical switch.

[0216]

[0243] One or more embodiments of the present invention allow for the measurement of elastic inertia or EI, for example, of sports equipment and even body parts. The placement of sensor embodiments along the shaft of a golf club, a tennis racket, a baseball bat, a hockey stick, a shoe, a person's arm, or any other object that is not completely rigid, allows the amount of bending to be measured at the points where the sensors are placed or between the sensors. The angular difference over time at each sensor allows not only the bending profile to be calculated, but also the bending profile that depends on time or force. For example, known EI machines use static weights in between to support the points to determine the EI profile. Therefore, these machines cannot detect whether the EI profile depends on the applied force or on the time that the force is applied, for example, the EI profile may be nonlinear with respect to force or time. Examples of materials that are known to have different physical properties with respect to time include Maxwell materials and non-Newtonian fluids.

[0217]

[0244] The user may also view the captured motion data in graphical form on a mobile device display or on glasses including a video display. The captured motion data obtained from the motion capture element embodiment may be used to augment the user's virtual reality view in the virtual environment. Virtual or augmented reality views of patterns found in the database via data mining are also consistent with the spirit of the present invention. The user may also view augmented information such as aim assist or aim guides that indicate where to attempt to place the shot, for example based on the existing wind conditions or taking into account hazards, for example the desired destination of the ball, i.e. trees in the middle of a golf hole.

[0218]

[0245] One or more embodiments of the invention include a motion event recognition and video synchronization system including at least one motion capture element configured to be coupled to a user, equipment, or a mobile device coupled to the user. The at least one motion capture element may include a memory, a sensor configured to capture any combination of values ​​associated with an orientation, position, velocity, and acceleration of the at least one motion capture element, a radio, and a microcontroller coupled to the memory, the sensor, and the radio. The microcontroller may be configured to collect data including sensor values ​​from the sensor, store the data in the memory, analyze the data to recognize events in the data to identify event data, and transmit the event data associated with the event via the radio. The system may include a mobile device including a computer, a wireless communication interface configured to communicate with the radio to obtain event data associated with the event, the computer coupled to the wireless communication interface, the computer configured to receive the event data from the wireless communication interface. The computer may analyze the event data to form motion analysis data, store the event data, the motion analysis data, or both the event data and the motion analysis data, obtain an event start time and an event stop time from the event, request image data from the camera including at least video captured during the time from the event start time to the event stop time, and display on a display an event video including both the event data, the motion analysis data, or any combination thereof occurring during the time from the event start time to the event stop time, and the video captured during the time from the event start time to the event stop time.

[0219]

[0246] The embodiments may synchronize clocks in the system using any type of synchronization methodology, and in one or more embodiments, the computer on the mobile device is further configured to identify clock differences between the motion capture elements and the mobile device and synchronize the mobile analytics data with the video. For example, one or more embodiments of the present invention provide a procedure whereby multiple recording devices synchronize information about the time, location, or orientation of each device so that data recorded for an event from different devices can be combined. Such recording devices can be embedded sensors, cell phones with cameras or microphones, or more generally, any device capable of recording data related to an activity of interest. In one or more embodiments, this synchronization is achieved by exchanging information between the devices so that the devices can converge on a common measurement for time, location, or orientation. For example, the cell phone and the embedded sensor may exchange messages with the current timestamp of their internal clocks, and these messages allow a negotiation to occur and the two devices converge on a common time. Such messages may be exchanged periodically as needed to take into account clock drift or device movement since a previous synchronization. In other embodiments, multiple recording devices may use a common server or set of servers to obtain a standardized measure of time, location, or orientation. For example, devices may use a GPS system to obtain absolute location information for each device. The GPS system may be used to obtain standardized time. An NTP (Network Time Protocol) server may be used as a standardized time server. By using the server, devices can converge on a common measurement without having to be constantly configured to communicate with each other.

[0220]

[0247] In one or more embodiments of the invention, some of the recording devices are configured to detect the occurrence of various events of interest. Some such events occur at specific moments in time and others occur over a time interval, and detection includes detecting the start of an event and the end of an event. These devices are configured to record any combination of time, location, or orientation of the recording device along with the event data, using a standardized measurement base for time, location, and orientation as described above.

[0221]

[0248] The computer embodiment on the mobile device may further be configured to discard at least a portion of the video outside the time between the event start time and the event stop. For example, in one or more embodiments of the present invention, some of the recording devices simultaneously capture data in memory while waiting for the detection of an event. To conserve memory, some devices may be configured to store data to a more permanent local storage medium or to a server only if this data is temporally close to the detected event. For example, in the absence of event detection, newly recorded data may eventually overwrite previously recorded data in memory. In some embodiments, a circular buffer may be used as a typical implementation of such an overwriting scheme. When an event detection occurs, the recording device may store some configured amount of data before the event starts and some pre-configured amount of data after the event ends, in addition to storing data captured during the event itself. Any pre- or post-time intervals are considered to be part of the event start and event stop times, for example, so that the context of the event is shown in the video. By storing only the video of the event on the camera-equipped mobile device or on the camera itself, significant space is saved and upload times are dramatically reduced.

[0222]

[0249] An embodiment of the system may further include a server computer remote to the mobile device, the server computer configured to discard at least a portion of the video outside the time between the event start time and the event stop time and return video captured during the time between the event start time and the event stop time to the computer in the mobile device.

[0223]

[0250] An embodiment of at least one motion capture element may be configured to transmit an event to at least one other motion capture sensor, at least one other mobile device, or any combination thereof, which is configured to store data associated with the event. For example, in an embodiment in which multiple recording devices operate simultaneously, one such device may detect an event and send a message to the other recording devices that such an event has occurred. This message may include a timestamp of the start and / or stop of the event, using the synchronized time base of the clocks of the various devices. A receiving device, such as another motion capture sensor and / or camera, may use the event detection message to store data associated with the event in non-volatile storage or a server. The devices may be configured to store an amount of data before the start of the event and an amount of data after the end of the event in addition to data directly associated with the event. In this way, all devices may record data simultaneously, but an event trigger from only one of the devices may be used to initiate the storage of event data delivered from multiple sources.

[0224]

[0251] The computer embodiment may be further configured to store the video from the event start time to the event stop time along with the motion analysis data generated between the event start time and the event stop time, or a remote server may be utilized to store the video. In one or more embodiments of the present invention, some recording devices are not in direct communication with each other over the period of time that an event may occur. In these situations, the devices may be configured to store a complete record of all recorded data in permanent storage or on a server. Storing only the data associated with the event may not be possible in these situations since some devices may not be able to receive the event trigger message. In these situations, the stored data may be post-processed to extract only the portion associated with one or more detected events. For example, multiple mobile devices may record video of a player or performer and sequentially upload the video to a server for storage. The player or performer may be separately equipped with embedded sensors capable of detecting events such as specific movements or actions. The embedded sensors may be uploaded to the same server sequentially or at a later time. All data, including the video stream and embedded sensor data, is generally time-stamped so that the video associated with the events detected by the embedded sensors can be extracted and combined on the server.

[0225]

[0252] The server or computer embodiment may be further configured to discard at least a portion of the video outside the time between the event start time and the event stop time while the communication link is open between the at least one motion capture sensor and the mobile device, and to store the video from the event start time to the event stop time together with the motion analysis data generated between the event start time and the event stop time. Alternatively, if the communication link is not open, the computer embodiment may be further configured to store the video, and after the event is received and the communication link is opened, to discard at least a portion of the video outside the time between the event start time and the event stop time, and to store the video from the event start time to the event stop time together with the motion analysis data generated between the event start time and the event stop time. For example, in some embodiments of the present invention, the data may be uploaded to a server as described above, and location and orientation data associated with each device's data stream may be used to extract data related to the detected event. For example, a large set of mobile devices may be used to record video at various locations throughout a golf tournament. This video data may be uploaded to the server continuously or after the tournament. After the tournament, sensor data associated with the event detection may be uploaded to the same server. Post-processing of these various data streams can identify specific video streams that were recorded simultaneously in the physical vicinity of the event that occurred. Additional filters can select video streams where the camera is pointing in the correct direction to observe the event. These selected streams can be combined with the sensor data to form an aggregate data stream where multiple video angles show the event.

[0226]

[0253] The system may obtain video from a camera coupled with the mobile data or from any camera that is separate from or otherwise remote from the mobile device. In one or more embodiments, the video is obtained from a server remote to the mobile device, for example, obtained after querying for video at a location and time interval.

[0227]

[0254] An embodiment of the server or computer may be configured to synchronize the video and event data or motion analysis data via image analysis to more accurately identify the start event frame, stop event frame, or both in the video that most closely correlates with the event start time, event stop time, or both. In one or more embodiments of the present invention, clock synchronization between recording devices may be coarse. It may be desirable to improve the accuracy of synchronizing data feeds from multiple recording devices based on each device's view of the event. In one or more embodiments, processing of multiple data streams is used to observe the signatures of events in the different streams to aid in fine-grained synchronization. For example, an embedded sensor may be synchronized with a mobile device that includes a video camera, but the time synchronization accuracy may only be within 100 milliseconds. If the video camera is recording video at 30 frames per second, the video frame corresponding to the event detection at the embedded sensor may only be identified to within 3 frames based solely on the synchronized timestamps. In one embodiment of the device, video frame image processing may be used to identify the precise frame that most closely corresponds to the detected event. For example, the shock from a snowboard hitting the ground, as detected by an inertial sensor, may be correlated with the frame in which the geometric boundary of the snowboard contacts the ground. Other embodiments may use other image processing techniques or other methods of detecting event signatures to improve synchronization of multiple data feeds.

[0228]

[0255] An embodiment of the at least one motion capture element may include a location determining element configured to determine a location coupled to a microcontroller, the microcontroller configured to transmit the location to a computer on the mobile device. In one or more embodiments, the system further includes a server, the microcontroller configured to transmit the location to the server directly or via the mobile device, and the computer or server configured to form an event video from portions of the video based on the location and the event start time and the event stop time. For example, in one or more embodiments, the event video is trimmed to a specific length of the event, transcoded to any or video quality, and overlaid or otherwise integrated in any manner with motion analysis data or event data, such as speed data or acceleration data. The video may be stored locally in any resolution, depth, image quality, or compression type that stores the video, or in any compression type that minimizes storage, regardless of any other technique that maximizes storage capacity or frame rate or whether a communication link is opened between the mobile device, the at least one motion capture sensor, and / or the server. In one or more embodiments, the velocity or other motion analysis data may be overlaid or otherwise combined with the bottom portion of the video, including the event start and stop times, which may include any number of seconds before and / or after the actual event, to provide, for example, a video of the swing before the ball-hitting event. In one or more embodiments, at least one motion capture sensor and / or mobile device may transmit the event and video to a server, and the server may determine that a particular video and sensor data occurred at a particular location at a particular time, and may construct an event video from several videos and several sensor events. The sensor events may be from one sensor or may be from multiple sensors, for example, coupled with the user and / or equipment. Thus, the system may construct a short video corresponding to the event, for example, significantly reducing video storage requirements.

[0229]

[0256] In one or more embodiments, the microcontroller or computer may be configured to determine the location of the event, and the computer may be configured to determine the location of the event, for example by correlating or averaging the locations to provide a midpoint of the event, and / or minimize erroneous location data from initialization of a GPS sensor. In this manner, a group of users with mobile devices may generate a video of a golfer teeing off, the event location of at least one motion capture device may be utilized, and a server may obtain video from spectators and generate an event video of a professional golfer's swing and ball strike, which may utilize frames from different cameras to generate a BULLET TIME.RTM. video from around the golfer as he swings. The generated video or videos may be trimmed, for example, to the duration of the event from the event start time to the event stop time, and / or with any pre- or post-predetermined time value before or after the event to ensure that the entire event is captured, including any setup time and any follow-through time of the swing or other events.

[0230]

[0257] In one or more embodiments, the computer on the mobile device may directly request at least one image or video that includes the event from at least one camera in the vicinity of the event by broadcasting a request for any video captured in the area by any camera, and that may optionally include orientation information related to whether the camera was positioned in the vicinity of the event as well as whether it was pointed at or otherwise pointing at the event. In other embodiments, the video may be requested by the computer on the mobile device from a remote server. In this scenario, any location and / or time associated with the event may be utilized to return images and / or video near the event, images and / or video captured at a time close to the event, or both. In one or more embodiments, the computer or server may crop the video to correspond to the duration of the event, again utilizing image processing techniques to further synchronize portions of the event, such as a ball being hit, with corresponding frames in the video that match acceleration data corresponding to, for example, a ball being hit with an equipment.

[0231]

[0258] An embodiment of a computer on a mobile device or on a server may be configured to display a list of one or more times that an event occurred or one or more events occurred, in this manner allowing a user to find an event from the list and quickly access the event video.

[0232]

[0259] Embodiments of the present invention may include at least one motion capture sensor physically coupled to a mobile device. These embodiments may include any type of cell phone or camera system with integrated sensors, such as any type of helmet mounted camera or any mount that includes both a camera and a motion picture sensor, that generates event data and video data.

[0233]

[0260] In some embodiments, the system may include one or more computers having a wireless communication interface capable of communicating with the radios of one or more motion capture elements to receive event data associated with motion events. The computer may receive the raw motion data and analyze this data to identify events. In other embodiments, event identification may occur at the motion capture elements and the computer may receive the event data. In some embodiments, a combination of these two approaches is also possible.

[0234]

[0261] In some embodiments, one or more computers may identify a start time and an end time of the motion event from the event data. The computer may then request image data from the camera that captured the video or may request one or more images at some time interval within at least some portion of the time between the event start time and the event end time. The term video herein includes individual images and continuous video, including the case of a camera taking a single snapshot image during the event interval. This video may then be associated with the motion data from the synchronized event data. An event may be gestured by a user, for example, by shaking or tapping a motion capture sensor a certain number of times. Any type of predefined event, including a user gesture event, may be utilized to control at least one camera to transfer a generally concise event video without the need to transfer a large amount of raw video files.

[0235]

[0262] In some embodiments, the request for video from the cameras may occur simultaneously with the capture or analysis of the athletic data. In such an embodiment, the system would obtain or generate a notification that an event has started, and then request streaming of video from one or more cameras to the computer until the end of the event is detected. In other embodiments, the request for video may occur after the cameras have uploaded their video recordings to another computer, such as a server. In this case, the computer would request the video from the server rather than directly from the cameras.

[0236]

[0263] Various techniques may be used to perform synchronization of the motion data and the video data. Such techniques include clock synchronization methods known in the art, such as network time protocol, that ensure that all devices - motion capture elements, computers, and cameras use a common time base. In another technique, the computer may compare its clock with the internal clocks of the motion capture elements and the internal clocks of the cameras by exchanging packets containing the current time as registered by each device. Other techniques analyze the motion data and the video data to align the different time bases for synchronization. For example, a particular video frame showing contact with the ball may be aligned with a particular data frame from the motion data showing a shock on the accelerometer, and these frames can then be effectively used as key frames to synchronize the motion data and the video data. The combined video and motion data forms a synchronized event video with an integrated recording of the event.

[0237]

[0264] In one or more embodiments, the computer configured to receive or process the athletic or video data may be a mobile device, including but not limited to a mobile phone, smartphone, tablet, PDA, laptop, notebook, or any other device that can be easily transported or moved. In other embodiments, such a computer may be integrated into a camera, particularly a camera from which the video data was acquired. In other embodiments, such a computer may be a desktop computer or a server computer, including but not limited to a virtual computer running as a virtual machine in a data center or cloud-based service. In some embodiments, the system may include multiple computers of any of the above types, which may collectively perform the operations described herein. As will be apparent to one of ordinary skill in the art, such a distributed network of computers may divide tasks in many possible ways, and may, if desired, coordinate the work of each to replicate the work of a single central computer. The term computer, as used herein, is intended to mean any or all of the above types of computers and to include a network of multiple such computers acting together.

[0238]

[0265] In one or more embodiments, a computer may obtain or create a synchronized event video sequence. The computer may display a composite summary of this sequence for a user to review past events. For videos associated with each event, in some embodiments, this summary may include one or more thumbnail images generated from the videos. In other embodiments, the summary may include a smaller selection from the full event video. The composite summary may include a display of motion analysis or event data associated with each synchronized event video. In some embodiments, the computer may obtain a metric and display the value of this metric for each event. The display of these metric values ​​is variable in different embodiments. In some embodiments, the display of the metric values ​​may be a bar graph, line graph, or other graphical technique showing absolute or relative values. In other embodiments, color coding or other visual effects may be used. In other embodiments, the numerical value of the metric may be shown. Some embodiments may use a combination of these approaches.

[0239]

[0266] In one or more embodiments, the computer may accept selection criteria for metrics of interest associated with the motion analysis data or event data of a series of events. For example, a user may provide criteria such as metrics above a threshold, or metrics within a range, or metrics outside a range. Any criteria that can be applied to the metric values ​​of an event may be used. In response to the selection criteria, the computer may display only the synchronized event data or summaries thereof (e.g., thumbnails) that meet the selection criteria. As an example, a user capturing golf swing event data may want to see only swings that have swing speeds over 100 mph.

[0240]

[0267] In some embodiments of the present invention, the computer may sort and rank the synchronized event videos for display based on the values ​​of the selected metrics in addition to filtering based on the selection criteria as described above. Continuing with the example above, a user capturing golf swing data may want to see only swings with swing speeds over 100 mph, sorted with the highest swing speeds shown first.

[0241]

[0268] In one or more embodiments, the computer may generate a highlight reel that combines video of events that meet the selection criteria. Such a highlight reel may include the entire video of the selected events or portions of the video that correspond to key moments during the events identified by the motion analysis. In some embodiments, the highlight reel may include data or graphics overlays on the video or on selected frames that indicate the values ​​of metrics from the motion analysis. Such a highlight reel may be generated automatically for the user once the user indicates which events to include by specifying the selection criteria. In some embodiments, the computer may allow the user to edit the highlight reel to add or remove events, lengthen or shorten the video shown for each event, add or remove graphic overlays for the motion data, or add specific effects or soundtracks.

[0242]

[0269] In embodiments using multiple cameras, the motion data and multiple video streams may be combined into a single synchronized event video. Video from multiple cameras may provide different angles or views of the event, all synchronized to the motion data and a common time base. In some embodiments, one or more videos may be available on one or more computers (such as a server or cloud service) and may later be correlated with the event data. In these embodiments, the computer may search for stored videos that were in the correct location and orientation to view the event. The computer may then retrieve the appropriate videos and combine them with the event data to form a composite view of the event with videos from multiple positions and angles.

[0243]

[0270] In some embodiments, the computer may select a particular video from a set of candidate videos associated with the event. The video selected may be the best or most complete view of the event based on various possible criteria. In some embodiments, the computer may use image analysis of each video to determine the best selection. For example, some embodiments may use image analysis to determine which video is most complete in terms of least occlusion or most clearly visible equipment or people of interest. In some embodiments, this image analysis may include analysis of the degree of camera shake during video capture and selection of the video with the most stable image. In some embodiments, the user may make the selection of the preferred video or the user may assist the computer in making the selection by specifying the criteria that are most important to them.

[0244]

[0271] In some embodiments, event data from the motion capture elements may be used to send control messages to cameras that can record video of the event. In embodiments using multiple cameras, control messages may be broadcast or sent to the set of cameras during an event. These control messages may change video recording parameters based on data associated with the event, including motion analysis data. For example, a camera may be on standby and not recording while an event of interest is not in progress. A computer may wait for the event data and send a command to the camera to start recording when the event begins. When the event ends, the computer may then send a command to the camera to stop recording. Such techniques may conserve camera power and video memory.

[0245]

[0272] More generally, in some embodiments, the computer may send control messages to one or more cameras in response to the event data or motion analysis data to change any relevant video recording parameters. These recording parameters may include, for example, video frame rate, resolution, color depth, color or grayscale, compression method and quality, and recording on or off. As an example where this may be useful, the motion analysis data may indicate that when a user or equipment is moving rapidly, the frame rate of the video recording may be increased during periods of rapid motion and decreased during periods of relatively slow motion in response. Using a higher frame rate during rapid motion allows the user to slow down the motion during playback to observe the high motion events in greater detail. These techniques may allow the camera to conserve video memory and efficiently use free memory for events of greatest interest.

[0246]

[0273] In some embodiments, the computer can accept a soundtrack, for example from a user, and integrate the soundtrack into the synchronized event video. This integration can, for example, add an audio soundtrack during the playback of an event video or a highlight reel. Some embodiments can use event data or motion analysis data to intelligently integrate the soundtrack into the synchronized event video. For example, some embodiments can analyze the soundtrack to determine the beat of the soundtrack, for example, based on time points of high audio amplitude. The beat of the soundtrack can then be synchronized with the event using the event data or motion analysis data. For example, such techniques can automatically speed up or slow down the soundtrack as the motion of a user or object increases or decreases. These techniques provide a rich media experience with audio and visual cues associated with the event.

[0247]

[0274] In one or more embodiments, the computer is configured to play the synchronized event video on one or more displays. These displays may be directly attached to the computer or may be remote and on other devices. Using the event data or motion analysis data, the computer may modify the playback to add or modify various effects. These modifications may be made multiple times during playback, or even continuously during playback as the event data changes. For example, during periods of low motion, playback may be at normal speed, while during periods of high motion, playback may switch to slow motion to highlight details of the motion. The change in playback speed may be based on any observed or calculated characteristics of the event or motion. For example, the event data may identify particular sub-events of interest, such as a ball being hit, the beginning or end of a jump, or any other moment of interest. The computer may modify the playback speed to slow down the playback as the synchronized event video approaches these sub-events. This slowing down may increase successively to highlight details of the sub-events. Playback may even stop at the sub-events and wait for input from the user to continue. The playback slowdown may be based on the value of one or more metrics from the motion analysis data or event data. For example, the motion analysis data may indicate the speed of a baseball bat or golf club in motion, and the playback speed may be continuously adjusted to slower as the speed of such an object increases. The playback speed may be made very slow near the peak value of such a metric.

[0248]

[0275] In other embodiments, modifications are not limited to playback speed, but may be made to other playback characteristics. For example, the computer may modify any or all of the playback speed, image brightness, image color, image focus, image resolution, blinking special effects, or the use of graphic overlays or borders. These modifications may be made based on the motion analysis data, event data, sub-events, or any other characteristics of the synchronized event video. As an example, blinking special effects may be added as the playback approaches a sub-event of interest, and borders may be added around objects of interest in the video, such as a ball about to be hit with an implement.

[0249]

[0276] In embodiments that include a soundtrack, the changes to the playback characteristics may include changes to the playback characteristics of the soundtrack. For example, such changes may include changes to the volume, tempo, tone, or audio effects of the soundtrack. For example, the volume and tempo of the soundtrack may increase as the playback approaches a sub-event of interest to highlight the sub-event and provide a more dynamic experience for a user viewing the playback.

[0250]

[0277] In one or more embodiments, the computer may use image analysis of the video to generate metrics from objects in the video. The metrics may, for example, measure some aspect of the object's motion. Such metrics derived from image analysis may be used in addition to or in combination with metrics obtained from motion analysis of data from the motion sensor. In some embodiments, the image analysis may use any of several techniques known in the art to find pixels associated with objects of interest. For example, a particular object may be known to have a particular color, texture, or shape, and these characteristics may be used to find the object in the video frames. As an example, a tennis ball may be known to be generally round, yellow, with a texture associated with the ball's material. Using these characteristics, the image analysis may find the tennis ball in the video frames. Using multiple video frames, the approximate speed of the tennis ball may be calculated. For example, assuming a stationary or nearly stationary camera, the location of the tennis ball in three-dimensional space may be estimated based on the location of the ball in the video frames as well as based on its size. The location in the frame gives the projection of the ball's location onto the image plane, and the size provides the depth of the ball relative to the camera. Using the location of the ball in multiple frames, and using the frame rate to give the time difference between frames, the velocity of the ball can be estimated. Vertical jump estimation can be performed near an object of known size, such as a basketball.

[0251]

[0278] In one or more embodiments, a microcontroller coupled to the motion capture element is configured to communicate with other motion capture sensors to coordinate the capture of event data. The microcontroller may send an event start notification to another motion capture sensor to trigger the capture of event data by that other sensor as well. That other sensor may store the data locally for later uploading or may send the event data to a computer over an open communication link while the event is occurring. These techniques provide a type of master-slave architecture where one sensor can act as a master and coordinate a network of slave sensors.

[0252]

[0279] In one or more embodiments, the computer may obtain sensor values ​​from other sensors, in addition to the motion capture sensors, that are in the vicinity of the event and provide other useful data associated with the event. For example, such other sensors may sense various combinations of temperature, humidity, wind, altitude, light, sound, and physiological metrics (such as heart rate). The computer may retrieve and store these other values ​​along with the event data and motion analysis data to generate an extended record of the event over the time period from when the event started to when the event stopped.

[0253]

[0280] One or more embodiments may acquire and process both sensor data and video to analyze the motion of an object and generate motion metrics that describe the motion. Embodiments may employ various steps and techniques to align data from sensors and cameras both temporally and spatially. The aligned data may then be analyzed using sensor fusion techniques to combine the sensor data and video data into a consistent and robust motion metric.

[0254]

[0281] One or more embodiments may obtain a description of one or more objects of interest, which may include people, equipment, or both. The embodiments may then obtain video captured from one or more cameras, which may include images of these objects in motion. Some or all of the objects of interest may have motion capture elements attached to them, which generate motion sensor data for the objects. One or more embodiments may then combine these three inputs - object description, video, and sensor data - to generate an integrated motion analysis of the object. The motion metrics generated may include, by way of example and not limitation, linear position, linear velocity, linear acceleration, trajectory, orientation, angular velocity, angular acceleration, time of motion, time elapsed between positions, time elapsed from the start of motion to reach a position, and time of impact.

[0255]

[0282] Video analysis of object motion may include identifying objects of interest in a set of frames selected from a video for analysis. Object identification may use a set of distinctive visual characteristics of each object, allowing embodiments to identify objects in the selected video frames. These distinctive visual characteristics may include, by way of example and not limitation, shape, curvature, size, color, luminance, hue, saturation, texture, and pixel pattern. Some embodiments may search every pixel of all selected frames for objects of interest, while some embodiments may instead use various optimizations to search only selected areas of frames that are likely to contain objects of interest.

[0256]

[0283] In one or more embodiments, the camera may be moving during video capture. Therefore, to analyze the motion of an object in a frame, it may be desirable to distinguish this true object motion from the apparent motion of the object caused by the camera motion. Therefore, one or more embodiments may use techniques to identify the pose (location or orientation) of the camera in each frame. The true object motion can then be determined by identifying the object pose relative to the camera pose in each frame, and then transforming this object pose into a common coordinate system using the camera pose in each frame.

[0257]

[0284] One or more embodiments may obtain one or more motion capture data of an object of interest from a motion capture element, for example, from a motion sensor attached to the object. The motion capture data may then be synchronized with the video frames. The motion capture data may then be combined with the video analysis data to form a motion metric for the object. The embodiments may use various sensor fusion techniques to integrate this data from different sources into a motion metric.

[0258]

[0285] One or more embodiments of the method may synchronize the video frames and the sensor data by finding a signature of the same event in both the video data and the sensor data and aligning the frame containing the video signature with the sensor timestamp of the sensor sample containing the sensor data signature. One or more embodiments may synchronize the video frames and the sensor data by calculating motion metrics of a selected reference object from both the video frames and the sensor data and finding a time shift between the video motion metrics and the sensor motion metrics that best aligns the two motion metric graphs. In some embodiments, the maximum clock difference between the sensor data and the video frames may be known in advance, and in these embodiments, the search for a matching event signature or a time shift that aligns the motion metrics may be optimized by searching only within the known clock difference.

[0259]

[0286] In one or more embodiments, the motion analysis may include finding one or more events of interest. Events may be found in the video frames using video signatures and in the sensor data using sensor data signatures. Some activities may falsely trigger one of these signatures, and the activity is not a true event, but instead is a false positive. One or more embodiments may combine the video signature and the sensor data signature to filter out false positives, for example, if an activity matches a sensor data signature but does not match the corresponding video signature, the activity may be classified as a false positive. A true event may be determined if both a video signature and a sensor data signature are present. One or more embodiments may use a multi-stage test to signal event candidates and then use either or both of the video signature or the sensor data signature to verify that the event candidate is a valid event.

[0260]

[0287] One or more embodiments may use visible markers as objects of interest or may use visible markers attached to an object or motion capture elements attached to an object. The pattern of the visible markers may form part of the distinguishing visual characteristics of the object. The visible markers may be designed to have no rotational symmetry so that video analysis can be used to distinguish between markers of different orientations. The visible markers may be configured with high visibility colors.

[0261]

[0288] One or more embodiments may pre-process the video frames to facilitate searching for objects of interest, where pre-processing may include, for example, but not limited to, noise removal, flicker removal, smoothing, color space conversion, color or brightness balancing, and shadow removal.

[0262]

[0289] One or more embodiments may use a sensor to measure the camera pose of a selected video frame, which may be used to transform the pose of an object into a common coordinate system.

[0263]

[0290] One or more embodiments may use one or more of the following techniques to determine the pose of an object identified in a frame relative to the camera pose for that frame: the line on which the object lies can be determined from the object's location in the frame; the object's apparent size in the frame compared to its true size can determine the object's relative distance from the camera; the object's orientation relative to the camera can be determined by calculating a rotation that minimizes the difference between the object's reference pixel pattern and the object's pixel pattern in the frame. The object's reference pixel pattern may be one of the object's distinctive visible characteristics or may be obtained from the object's appearance in the depicted reference frame.

[0264]

[0291] One or more embodiments may create or obtain a physical model of the likely trajectories of one or more objects and use this physical model to estimate likely positions in each frame for the object locations, and then optimize the frame search for the object by searching within these likely regions.

[0265]

[0292] In embodiments that do not use a camera sensor to determine the camera pose, it may be desirable to estimate the camera pose for each frame by aligning each frame with the previous frame and compensating for camera motion. Once each frame is aligned with the previous frame, the frame difference between them can be calculated to identify high motion regions that contain moving objects. The frame search for objects of interest can then be optimized by searching within these high motion regions.

[0266]

[0293] One or more embodiments may use the following technique to align a frame with a previous frame to compensate for camera motion: First, a frame translation may be identified that minimizes the pixel difference between a central region of the frame after the frame translation has been applied and a central region of the previous frame. Then, each frame may be divided into tiles, and a local tile translation may be identified for each tile that minimizes the pixel difference between that tile and the corresponding tile of the previous frame after the frame translation and the local tile translation have been applied.

[0267]

[0294] One or more embodiments may calculate an impact time of one or more objects of interest as one of the motion metrics. The impact time may be calculated by detecting discontinuities in the motion metrics between consecutive frames. This technique may identify an impact time frame, but in some cases may not be able to identify a precise inter-frame impact time. One or more embodiments may determine a more precise impact time by extrapolating the object trajectory forward from frames before the impact time and backward from frames after the impact time, and calculating the time when these two trajectories intersect as the precise impact time.

[0268]

[0295] In one or more embodiments, there may be a desired trajectory for an object, e.g., hitting a golf ball such that the trajectory enters a hole. One or more embodiments may compare this desired trajectory to an observed trajectory and report the trajectory difference as a motion metric. One or more embodiments may determine changes to the initial conditions of the object trajectory necessary to transform the observed trajectory into the desired trajectory. The initial conditions may include, for example, the initial speed and orientation of the object. In the case of golf, as an example, the initial conditions may include the speed and aiming of the golf club at impact with the golf ball.

[0269]

[0296] One or more embodiments may generate a set of highlight or failure frames from the video, where the highlight or failure frames include an activity of interest. The activity of interest may be identified using one or more activity signatures, for example, sensor data may be used to identify when a particular motion metric falls within or outside a particular range that identifies the activity of interest. As an example, a jump may be identified by scanning acceleration data to find acceleration values ​​close to zero, which may indicate that an object is in free fall. A large failure, representing a collision, may be identified by scanning velocity data for abrupt transitions between high velocity and zero or very low velocity. The highlight or failure frames may include specific frames before or after the activity of interest. The highlight or failure frames may be transmitted to one or more of a repository, a viewer, a server, a computer, a social media site, a mobile device, a network, and an emergency service. One or more embodiments may overlay the highlight or failure frames with data or graphics representing the value of the motion metric. One or more embodiments may send a message to the camera that captured the video instructing the camera to discard portions of the video other than the highlight or failure frames.

[0270]

[0297] One or more embodiments may calculate the elapsed time of an activity as an athletic metric. The start time of an activity may be determined, for example, using motion capture data to detect a signature corresponding to the start of the activity. The end time of an activity may be determined, for example, using video analysis to detect a signature corresponding to the completion of the activity, for example, a camera located at a goal may detect an object intersecting the goal.

[0271]

[0298] One or more embodiments of the method may create a synchronized event video of the event that synchronizes the video with event data received from the motion capture element. This synchronization may include calculating or receiving the event data from the motion capture element and obtaining an event start time and an event stop time from the event data. A video start time and a video stop time associated with the video may be obtained. The synchronized event video may then be created by correlating and aligning the event time between the event start time and the event stop time with the video time between the video start time and the video stop time. One or more embodiments may overlay the event data onto the synchronized event video. One or more embodiments may instruct the camera to transfer the synchronized event video to another computer without transferring portions of the video outside the time interval between the event start time and the event stop time. One or more embodiments may also instruct the camera to discard portions of the video outside the time interval between the event start time and the event stop time.

[0272]

[0299] One or more embodiments may first detect and eliminate false positive events by identifying an event candidate using a first set of criteria, and then use a second set of criteria to determine whether the event candidate is valid. The first set of criteria may include, for example, having a first value from the motion capture data that exceeds a first threshold and having a second value from the motion picture data that exceeds a second threshold within a time window around a sample that includes the first value. The second set of criteria may, for example, compare the event candidate to characteristic signals associated with a typical event, or compare the video of the event candidate to characteristic images associated with the typical event, or both.

[0273]

[0300] An embodiment of the invention may include data from multiple sensors, possibly of different types or modalities, tracking a combination of players, equipment, and projectiles such as balls. The sensor data may be transmitted via a wired or wireless communication interface to an event analysis and tagging system. The event analysis and tagging system may include one or more processors that analyze the sensor data. The sensor data may be synchronized to a common time scale and analyzed for event detection and generation of metrics describing the events.

[0274]

[0301] The sensors may include, by way of example and not limitation, inertial motion sensors, video cameras, light gates, light curtains, and radar. Inertial motion sensors may include, for example, accelerometers, gyroscopes, magnetometers, or more generally, any sensor that measures one or more of position, orientation, linear velocity, angular velocity, linear acceleration, or angular acceleration. A combination of sensor modalities may be used. For example, an embodiment of the system may track a player using a video camera, track the speed of a ball using a radar, and track an equipment such as a bat using an inertial motion sensor. Another embodiment may track the equipment using an inertial motion sensor on the equipment and track a projectile (such as a ball) using another inertial motion sensor integrated into the projectile, for example.

[0275]

[0302] The sensors may be attached to equipment, including but not limited to sports equipment, or may otherwise measure or observe the movement of the equipment. The equipment may be worn, attached, carried, or used by one or more players at an event. Equipment tracked by the sensors of the system may include, by way of example and not limitation, bats, racquets, paddles, golf clubs, bows, guns, slingshots, sabres, hexagons, lacrosse sticks, hockey sticks, field hockey sticks, polo mallets, croquet mallets, pool cues, shuffleboard cues, gloves, shoes, belts, watches, helmets, caps, skis, snowboards, skateboards, surfboards, ice skates, sleds, luges, windsurfboards, hang gliders, roller skates, rollerblades, vehicles, snowmobiles, jet skis, bicycles, tricycles, unicycles, motorcycles, snowmobiles, and rodeo machines.

[0276]

[0303] Some sporting events or other activities involve a projectile, such as a ball. The projectile may be thrown, caught, carried, kicked, or hit by a player. In some activities, a player may use equipment to hit, catch, or carry the projectile. Projectiles tracked by the system's sensors may include, by way of example and not limitation, a ball, a football, a rugby ball, an Australian rules football, a soccer ball, a volleyball, a water polo ball, a polo ball, a basketball, a lacrosse ball, a field hockey ball, a croquet ball, a billiard ball, a horseshoe, a shuffleboard disc, a tennis ball, a table tennis ball, a racquetball, a handball, a bocce ball, a lawn dart, a squash ball, a shuttlecock, a baseball, a softball ball, a golf ball, a bowling ball, a hockey puck, a dodgeball ball, a kickball ball, a Wiffle™ ball, a javelin spear, a shot put shot, a discus, a marble, a bullet, an arrow, a knife, a shuriken, a bola, a grenade, a water balloon, a boomerang, a Frisbee, a log, and a curling stone.

[0277]

[0304] In many sports, players swing equipment in an attempt to hit or otherwise make contact with a projectile. Examples include a bat hitting a baseball, a tennis racket hitting a tennis ball, and a polo mallet hitting a polo ball. An embodiment of the invention may measure a swing event by combining information about the motion of the projectile and the motion of the equipment hitting (or attempting to hit) the projectile. For example, in baseball or softball, one or more embodiments may combine sensor data about the bat motion and sensor data about the ball motion to calculate a reaction time metric, which may be defined, for example, as the elapsed time between the ball leaving the pitcher and the initiation of a forward swing of the bat to hit the ball.

[0278]

[0305] One or more embodiments may analyze swing events by calculating the trajectory of the projectile over time and the trajectory of the equipment over time as the equipment hits or attempts to hit the projectile. The equipment trajectory may, for example, track the location of a preferred ball launch location on the equipment, such as the center of a bat, over time. A metric may be calculated by analyzing a combination of the equipment trajectory and the projectile trajectory. For example, one or more embodiments may first calculate a point on the projectile trajectory that represents an optimal impact point. The projectile may reach this point at a particular time that is the optimal ball launch time. A swing accuracy metric may then be derived from the vector difference between the optimal ball launch location and the location on the equipment trajectory at the optimal ball launch time. A poor swing may be due to either spatial offset (the swing did not go to the correct location) or temporal offset (the swing arrived at the correct location but too early or too late). One or more embodiments may calculate a spatial offset metric derived from the vector difference between the optimal ball launch location and the closest point on the equipment trajectory to the optimal ball launch location. A time lag metric may be derived from the time difference between the optimal impact time and the time when the implement trajectory reaches the approach point closest to the optimal impact location.

[0279]

[0306] An embodiment of the present invention may automatically generate or select another tag for the event, for example, based on an analysis of the sensor data. The event data with the tag may be stored in an event database for subsequent retrieval and analysis. The tag may represent, by way of example and not limitation, an activity type, a player, a timestamp, a stage of an activity, a performance level, or a score result.

[0280]

[0307] One or more embodiments may also analyze media such as text, audio, images, or video from a social media site or other server to generate, modify, or verify event tags. Media analyzed may include, by way of example and not limitation, email messages, voice calls, voicemails, audio recordings, video calls, video messages, video recordings, text messages, chat messages, social media site posts, blog posts, or wild postings. Sources of media to analyze may include, by way of example and not limitation, email servers, social media sites, photo sharing sites, video sharing sites, blogs, wikis, databases, news groups, RSS servers, multimedia repositories, document repositories, and text message servers. Analysis may include searching the text for keywords and key phrases related to the event. Event tags and other event data may be published to social media sites, other servers, or information systems.

[0281]

[0308] In one or more embodiments, sensor or video data may be collected over time, with only certain portions of those periods containing activity of interest. Thus, one or more embodiments may receive signatures of activities of interest and use these signatures to filter the sensor and video data to focus on the activities of interest. For example, in one or more embodiments, a set of highlight frames may be selected from the video that specifically show the activity of interest.

[0282]

[0309] In this illustrative example, one activity of interest is a jump at high speed. The signature of a jump is an acceleration magnitude falling below g / 2 and a velocity magnitude exceeding 50 mph, indicating that the snowboarder is in free fall. The acceleration magnitude received from the sensor is compared over time to an acceleration threshold. The accelerometer is integrated (along with data from other inertial sensors such as gyros) to form velocity data. As the snowboarder takes a small jump, the acceleration magnitude falls below the threshold at frame 4103, but the velocity is not high enough at that time to match the activity signature. The acceleration magnitude again falls below the threshold at a corresponding video frame, this time the velocity also exceeds the required threshold, and therefore the data matches the activity signature. Three highlight video frames are selected as indicative of the jump activity detected by comparing the acceleration motion metric to the threshold. One or more embodiments may select the highlight frames to include all frames captured during the activity time period during the activity of interest. One or more embodiments may add additional frames to the highlight frames that are before or after the activity time period. One or more embodiments may sample only selected frames during an active time period, thereby generating, for example, a smaller set of highlight images than the complete video.

[0283]

[0310] In one or more embodiments that generate highlight frames, consumers of the highlight frames may include, by way of example and not limitation, any video or image viewing device; a video, image, or data repository; any type of computer, e.g., server, desktop, laptop, or tablet; any mobile device, e.g., phone; social media sites; any network; and emergency services. One example of an embodiment that may transmit video highlights to emergency services is a crash detection system, e.g., for a bicycle or motorcycle. This embodiment may, e.g., use an accelerometer to monitor the user to detect crashes and an on-board camera to continuously capture video. Once a crash is detected, information about the location and severity of the crash may be sent directly to emergency services along with a video showing the crash. Any camera local to the event, whether a highlight event, a crash, or any other type of event, may be queried to determine if it has video from that location and time, e.g., using a field of view that encompasses the location of the event. Any other sensors close to the event and time or video covering the event may be queried and sent out to define a group event. Other sensor data, including heart rate and heart rate or sound level, may also indicate an event that qualifies as a highlight event or another type of event, such as a failure. Members of any group associated with the user can subscribe to an event or group event and get the highlights or misses of the day.

[0284]

[0311] In another embodiment, the invention encompasses a kinematic link that includes a chain of independent components of motion that work towards a common goal, the effective and efficient execution of motion. This principle of kinematic link is applicable to all dynamic athletic-type physical motions intended to perform work or transform or transfer energy in any manner by exerting the body with or without the use of tools or sports equipment actuated or manipulated by the hands or feet. For example, in a golf swing motion, which is the process of hitting a golf ball with a golf club, the kinematic link is composed of four main components of motion with three links. These four components and three links combine to represent the complete motion of the body. The components consist of the hips, shoulders, arms, and club. The link includes the musculature found between each body segment. The frame of reference and the point at which this type of motion must be utilized is the ground itself, so a complete analysis of the motion must first consider the feet, then the overall posture, then the hips, then the shoulders, then the arms, then the club, and finally the ball. Weakness at any point of the kinematic link leads to a less than optimal overall performance. Any means of identifying and improving the component parts of a movement will improve overall performance. This assumption is the basis of the present invention.

[0285]

[0312] The present invention, in its broadest sense, may be a global knowledge-based business system and method that provides performance testing and training regimes to persons for whom a competitive activity such as golf is an inherent part of their regular work or recreational activities and for whom repetitive athletic movements are an inherent part of their regular work or recreational activities, to improve the effectiveness of their golf swing or other specific athletic movements.

[0286]

[0313] A typical example of a system and method is worn by a user or subject, uses state of the art technology and equipment to monitor exercise, utilizes and contributes to a vast library of extensive performance data for analysis of test results, provides an informative, graphical display of the results in multiple synchronized formats for user review and / or monitoring by a coach or system operator, and prescribes a user-specific training regime based on the results with exercises selected from a library of standardized exercises using standardized tools and training aids.

[0287]

[0314] The user and coach may access a library of performance data and compare the user's most recent test performance with previous test results of themselves or other test subjects. Tests are repeated after an appropriate amount of offline exercise and / or when desired by the user or coach. Prescribed training details may be recalculated and include weighted consideration of current performance test results in addition to previous test results. Performance reports provide an objective record of the type and degree of performance changes experienced by the user.

[0288]

[0315] The system may be employed during live practice sessions to provide essentially instantaneous or "real-time" visual and / or audible biofeedback or a "replay" presentation of each successive attempt at a particular drill or full motion. Deviations of specific parameters from prescribed drill targets are reported and the user has an immediate opportunity to respond to the feedback and reduce deviations of the specific parameters during the very next attempt at the same drill.

[0289]

[0316] In one embodiment, the present invention is a local system or method of golf swing motion analysis of a golfer intended to improve the golfer's performance with repeated use. The present invention includes the use of multiple body and / or tool mounted sensors, video and / or audio recording input of the test, along with real-time wired or wireless transmission of sensor data to a receiver / computer and database system for data processing and analysis. The results are generated instantly by the system and consist of associated forms of biofeedback including graphical representations of coordinated comparative data output for full motion components, along with animations of the motion generated from the motion data and actual videos of the motion. The three forms of feedback are combined in presentation for easy assimilation and understanding by the golfer and / or instructor, either in an immediate form allowing sequential monitored trials with immediate feedback or in a later feedback mode with extended periods for practice of prescribed drills intended to improve aspects of the motion.

[0290]

[0317] The analysis reduces the total motion to predefined major component motions. The resulting linked data output portion may represent the relative timing and amplitude of the components of the user's own motion. The comparison data output may represent a comparison of the relative timing and amplitude of the components of the user's motion with expert or other standard performance data from the system database or the same components of the user's own previous test performance data. The data processing and feedback may further include prescriptions from a database of standard exercises tailored according to the user's level of performance and available time for training on a motion component basis such as stance, balance, hip motion, and shoulder and arm motion, adjusted according to the user's actual performance data. The exercises may prescribe or assume the use of specialized tools and training techniques from a library of predefined tools and training techniques during the exercise.

[0291]

[0318] As described above, data entry, analysis, and biofeedback reporting is preferably enhanced through the use of video, audio, and other recording devices mounted and focused to capture additional motion data in desired directions from the user under test, and processed to provide additional graphical, video, audio, or other forms of output that can be integrated with the data output for optimal user understanding and assimilation in analysis and reporting.

[0292]

[0319] The systems and methods in local or global embodiments may be applied to other athletic or professional activities in which energy is converted into any type of work through a user's movement to improve performance, avoid injuries, and / or provide rehabilitation programs.

[0293]

[0320] The sensor device of the present invention can be attached to a user's body and / or to an athletic tool or device, such as a golf club, optionally at strategic points. The motion sensor includes a multi-element sensing system and circuitry for detecting and orienting the three-dimensional position and orientation of the sensor and transmitting a real-time output of vector data for further application-specific processing. One embodiment of the multi-element sensing system within the motion sensor includes three gyroscope inertial sensors, three accelerometers, and three magnetometers, such as those manufactured by InterSense Inc. of Bedford, Massachusetts. Motion data is typically updated at a rate of 120 Hertz from a system employing three motion sensors, although systems with fewer and more motion sensors and faster and slower position update rates are within the scope of the present invention.

[0294]

[0321] The vector data from the full set of motion sensors is sufficient to derive and characterize the major components of a golf swing or other athletic motion, as described further below. Information is transmitted in near real-time from each sensor either directly, individually, or via a common transmitter to which some or all of the sensors may be hardwired, to a nearby receiver and then a processing computer for application-specific data processing and analysis, and generation of data and graphical output reports representative of the user's performance, as described further below.

[0295]

[0322] The processing computer can perform relational calculations on the data received from the various sensors, thereby allowing the calculation of various application-related parameters of interest. As an example, a processing computer with golf-specific software can calculate the club face angle or the angle at which a golfer's shoulders turn while swinging a golf club. Such parameters are referred to herein as "performance parameters."

[0296]

[0323] Specifically in a golf swing motion analysis system, inertial sensor data is typically processed into the following parameters related to a golfer's physical performance: hip velocity (degrees per second); hip rotation (negative and positive degrees); shoulder velocity (degrees per second); shoulder rotation (negative and positive degrees); club release (degrees per second); club speed (miles per hour); club face rotation (degrees open / closed); club path (degrees inside or outside of club address position); hip linear movement (cm left or right of neutral address); hip shoulder separation (time difference between maximum hip, shoulder, and club height degrees); hip segment flexion / extension (centimeters moved along the z-axis); and kinematic linkage. These parameters are further extrapolated to generate predicted "ball in flight" result performance parameters: spin (degrees per second); launch angle (degrees); carry distance; roll distance (yards); total distance (yards); offline distance (yards right or left); ball flight characteristics (fade, draw, hook, slice, push, pull, straight); and PTI or power transfer index.

[0297]

[0324] The processing computer can also display information about the swing that allows the golfer or the golfer's instructor to visualize and adjust the swing. For example, in one aspect, the system displays a live video feed of the golfer (obtained through a video feed from a video camera located in close proximity to the golfer and wirelessly or otherwise coupled to the processing computer), an animated simplified version of the same motion generated from the motion data, and statistics reporting the status of various parameters at any given still image. The system can also display the results of various calculations of performance parameters that characterize the swing over time, as described in the previous paragraph, for example, the system can display data regarding the club face angle or the angle at which the shoulders rotate during a particular swing.

[0298]

[0325] A system interface between the processing computer and the golfer, in the form of a control or feedback module mounted on or near the instrumented golfer, can provide instructions to the golfer in preparation for or in response to a particular golf swing attempt. The system interface may instruct the golfer, for example, to address the ball and allow the golfer five seconds to begin the swing. Such instructions, in one embodiment, can be in the form of an audible beep or a synthesized speech or pre-recorded voice command. A colored lamp or backlit LCD or other type of visual signal display can also issue coded or alphanumeric commands. Such functionality is useful for calibrating sensors for absolute position and for ensuring specific and timely inputs necessary for coordinating an orderly sequencing or progression of a test session.

[0299]

[0326] In one response mode, the system may be characterized as operating in a "biofeedback mode" in which the processing computer, through a system interface, assists the golfer in following the prescribed exercises (described in more detail below). In that mode, the processing computer may also display one or more calculated performance parameters and a video image of the golfer to the golfer and / or instructor on its display unit or screen. The calculated diagnostic parameters of interest may be reported on the screen, stored for later analysis, or converted into a success or failure code that can be sent to the golfer and / or the golfer's instructor, or any combination of these actions.

[0300]

[0327] The code sent to the golfer as biofeedback can be in the form of different tones or colors for successful and unsuccessful swings. For example, if the system is programmed and set up to train the golfer with a set of exercises that attempt to rotate the shoulders exactly 40 degrees from vertical, the system, as through the control module, can advise the golfer through a change in tone or light or color in a graphic display screen if the swing differs from the ideal rotation by more than a predefined error. For example, a swing is considered successful only if the rotation angle is between 35 and 45 degrees. The change in tone or light can have several bands or ranges, allowing intermediate results or measurements. For example, a red light can follow a swing where the diagnostic parameters deviate severely from the ideal, a yellow light can follow a swing where the same diagnostic parameters deviate only somewhat from the ideal, and a green light can follow a swing where the same diagnostic parameters deviate from the ideal by less than a pre-assigned error margin. The signal light can be an animated background color. The information conveyed by changing the color of selected illuminated portions of the screen may also be presented at the same, greater, or less detailed levels in other audio, text, numerical, and / or graphical formats, including numbers, bar graphs, line graphs, and text messages. In combination with or instead of this, verbal prompts may be used.

[0301]

[0328] The feedback report may be continuous or highly differentiated, for example, the duration of an audible tone may correspond to the extent to which a diagnostic parameter deviates from ideal, and the golfer is instructed to practice until the tone shortens or disappears. The number of light flashes, light color, sound frequency, volume, tone length, and tone type are some of the features that may be used in the feedback mode. Audio format feedback information may be generated using a synthesized voice output from a speaker or earphones.

[0302]

[0329] The processing computer and system interface may also include monitoring by a golf pro, other athletic professional, or instructor, either directly or remotely, such as through an internet connection, allowing the golf pro, other athletic professional, or instructor to send instructions to the golfer to start, stop, or control the exercise through instructor commands input into the system, or received by the system from a remote location, such as through an internet connection.

[0303]

[0330] After calculation of various golf-related parameters of interest, those diagnostic parameters can be utilized by indexing into a cross-reference table of test results and exercises to automatically prescribe the golfer an appropriate personalized exercise assignment to improve his swing. In one embodiment, each calculated diagnostic parameter is divided into two, three, or more ranges, each range corresponding to a prescribed action for a particular exercise. For example, a particular diagnostic parameter in a first range can lead to a prescription of a particular exercise, the same parameter in a second range can lead to a different prescription, and the same parameter in a third range can lead to no exercise prescription because the golfer does not have a problem with the particular aspect of the swing that the parameter measures. The different prescriptions in each example can be, for example, a specific different number of repetitions of a given exercise, a different priority level given to a given exercise (see next paragraph for priority levels), a different exercise tool or accessory used for a given exercise, or a completely different exercise. Furthermore, the frequency and duration of the exercises can be allocated by the prescription compiler according to the golfer's free time and schedule, as previously inputted into the system by the golfer.

[0304]

[0331] Alternatively, the prescription may be generated from a combination of results from two or more diagnostic patterns. In a variant, the knowledge base may include rules created through expert interviews, automated analysis techniques based on measurements generated by the swing, or principles of fuzzy logic. In one embodiment, the diagnostic parameters generate exercise prescriptions that are assigned a priority level. For example, if a particular golfer's swing generates one diagnostic parameter that is significantly off from ideal, while the other diagnostic parameter only partially deviates from ideal, the first diagnostic parameter will be assigned a higher priority level than the other. In another example, if two diagnostic parameters deviate from ideal, but one is considered more important for a good golf swing, or alternatively, one is considered more important to control to provide a good foundation than the other, one will be assigned a higher priority level than the other.

[0305]

[0332] In one embodiment, each prescribed training exercise may be assigned a priority level from 1 to 9, with some exercises being assigned a common priority level. In that embodiment, the golfer or instructor may indicate, by input to the computer, the amount of time the golfer has available to perform the exercises, and based on that information, the system may recommend which exercises to perform. For example, if the athletic motion analysis system suggests the need for three exercises with priority level 1, five exercises given priority level 2, and four other exercises with higher priority levels, and each exercise needs to be performed for at least 15 minutes for reasonable effectiveness, and the golfer has limited time to exercise, the system may allocate or prescribe accordingly. As a specific example, if the golfer indicates that he has one hour of free time, the allocation may be to perform only the three priority 1 exercises of 20 minutes each. If the golfer has two hours of free time, the system may prescribe to perform all priority 1 exercises and all priority 2 exercises for 15 minutes each. If the golfer has three hours of free time, the system may allocate all exercises for 15 minutes each. The minimum time to perform each of the different exercises is variable, the recommended time to perform any particular exercise may be variable or fixed, and the priority rankings may be altered as desired.

[0306]

[0333] The diagnostic parameters may additionally or alternatively be used to prescribe, select, or fit a golfer with golf equipment, such as a golf club or golf shoes, from among several types or customized versions having particular measured parameters. For example, the length, lie angle, loft, or weight of a particular type of golf club may be selected based on an analysis of the diagnostic parameters calculated for the golfer, preferably in combination with parameters for the golfer, such as height, hand length, and foot size.

[0307]

[0334] In another aspect, parameters calculated at impact, such as the position and orientation of the club face relative to the ball, as well as the club's velocity vector and face angle, can be used to predict the force on the ball and therefore the ball's trajectory. Knowledge of the terrain can determine the flight distance and path of the struck golf ball, or alternatively, the calculation can predict the flight distance assuming the terrain is flat. Such predictions based purely on force calculations can be supplemented with information about the ball's behavior in the atmosphere, such as through testing of a particular type of golf ball, and adjusted for air resistance. In a further variation, wind direction and speed can be taken into account, with such data being entered into the system manually, or through an electronic anemometer, or through a local air data station electronically coupled to the system, or via an internet or wireless connection.

[0308]

[0335] The system may be controlled remotely or locally so that an off-site or on-site instructor may direct the operation of the system or monitor the results. In a purely user-inclusive mode, control inputs for set-up and test operations may be entered, test exercises may be performed, and swing data may be viewed and reviewed by the user using a personal control module and data monitoring system, such as a belt-mounted control / display unit or module.

[0309]

[0336] The methodology and system are applicable to other repetitive athletic and professional exercises for animal or human testing, analysis, reporting, and diagnostic review by coaches, trainers, and / or medical personnel or physical therapists, where data can be collected and, in the case of exercises that can be reduced to their component parts as described above, prescriptions directed to training to improve performance, avoid injury, or to the rehabilitation of various motor functions can likewise be generated, and reports can be presented in synchronized composite displays of animations, multiple data tracks, and video formats.

[0310]

[0337] It is an additional goal that reports of practice drills and prescribed regimes can be taken home as records in any useful format or accessed from home through a browser-based online access point that connects to a local system or host, to a connected knowledge-based enterprise system for later review and practice.

[0311]

[0338] Thus, the present invention in one aspect comprises a method and apparatus for analyzing and improving selected athletic movements of an individual, comprising the steps of using a computer-based motion analysis system having a processing computer, inertial sensors, a video camera that may be of any analog or digital technology, and a computer-driven display screen, and testing the individual performing an athletic movement, using a tool if one is implied, by monitoring the execution of the movement with a number of inertial sensors attached to the individual and optionally a tool, and a video camera or cameras pointed at the individual in motion. The athletic movements may be golf, baseball, malleting, sawing, throwing or using other hand-held tools or sports equipment, a small sample of which may include balls, bats, rackets, clubs, paddles, oars, javelins, hammers, screwdrivers, tucker, darts, horseshoes, and axes, etc. It also extends to running, kicking, jumping, pedaling, and other foot / leg athletic movements using foot-actuated tools and sports equipment including bicycles, balls, and foot-operated levers, and other tools and objects.

[0312]

[0339] If used, the video camera is carefully positioned in advance to ensure precise alignment with the individual under test and useful reference points for motion measurement and analysis. The sensor is carefully positioned with the body-wearable device to ensure that the sensor data accurately reflects the body motion. During the performance of the competitive motion, sensor data is collected from the sensor and video signals are collected from the camera, the sensor data is analyzed by processing the sensor data into motion data representative of predefined and selected performance parameters of predefined and selected components of the competitive motion as achieved by or attributable to specific or distinct body segments such as leg, hip, shoulder, neck, head, arm, and hand aspects of the motion, and the results of the analysis are reported or presented in a form including a real-time computer-generated display of multiple selectable configurations, one of which includes a synchronized composite combination of the video signal as a video display, a three-dimensional animation representing the motion of at least one color-coded body segment created from the motion data, and a time-based graph of the multiple selected performance parameters.

[0313]

[0340] Prior to testing, setting of a motion limit range for a selected component of motion, such as a particular bend or flexion component of motion, may be provided. Animation of motion may incorporate a three-dimensional wire mesh cage or open frame with segments visible that represent the motion limit. The software may provide a selected color change in the display when motion beyond the motion limit occurs as a highly visible instantaneous signal to the individual that the limit has been reached or exceeded. Pre-set gradual levels of indication approaching or exceeding the limit may be used by using multiple color changes, such as green to orange to red.

[0314]

[0341] The analysis may include comparing the athletic data test values ​​for selected parameters to predefined benchmark values ​​for the same parameters, identifying the extent of deviation, and simultaneously presenting the test and benchmark values ​​on a time-based graph. The analysis may include calculating a score for each selected parameter from the test and benchmark values. The analysis may further include combining the scores for the selected parameters according to a predefined formula to generate a single score representing an overall athletic performance value as an athletic index.

[0315]

[0342] The system and software may include a report or presentation with a prescription for a training exercise regime selected from a predefined list of exercises based on the amount of deviation of the individual parameters from the benchmarks, together with or in accordance with the exercises. A predefined list of training tools may be associated with the exercises. The frequency and length of duration of the exercises may be limited by the individual's available training time, as may be entered into the processing computer prior to testing.

[0316]

[0343] The wireless inertial sensor may employ a stacked topology within the sensor enclosure, with the battery near and shaped to fit the bottom of the enclosure, and the sensor element and electronics located within the enclosure above the battery. The sensor may be attached to a body appliance worn by an individual. The sensor and appliance may correspondingly have a keyed mating structure, such that the sensor can be uniformly and repeatably attached in the same orientation and location on the appliance.

[0317]

[0344] Results of the analysis, including the athletic data and video data, may be stored in a local or remote computer database and may be provided for later playback either locally or via a remote computer or computer network connected or connectable to the processing computer.

[0318]

[0345] The camera may be set up using a reference target or frame placed at the test site prior to testing to define at least one point or line of reference for the motion being tested. The software may be configured during the calibration phase to overlay on the video display reference points, lines, or other symbols related to aspects of the motion, such as alignment lines, center lines, balance points, etc., of the test subject's start, end, or mid-motion positions where the motion can be more closely observed. The validity of the lines and symbols overlaid on the video display may depend on correct camera placement prior to testing.

[0319]

[0346] The particular embodiments shown and described are illustrative of the invention and are not intended to limit the scope of the invention in any way otherwise. Indeed, for the sake of brevity, conventional manufacturing, connection, preparation, and other functional aspects of the system may not be described in detail. Moreover, the connecting lines shown in the various figures are intended to represent example functional relationships and / or steps between the various elements. In an actual system, many alternative or additional functional relationships or physical connections may be present.

[0320]

[0347] In the above specification, the invention has been described with reference to certain exemplary embodiments. However, various modifications and changes can be made without departing from the scope of the invention as set forth in the claims. The specification and figures are illustrative rather than limiting, and modifications are intended to be included within the scope of the invention. The scope of the invention should therefore be determined by the claims and their legal equivalents, and not by the mere examples described.

[0321]

[0348] For example, the steps recited in any method or process claim may be performed in any order and are not limited to the particular order presented in the claims. Additionally, the components and / or elements recited in any apparatus claim may be assembled or otherwise operatively configured in various permutations and, accordingly, are not limited to the particular configuration recited in the claims.

[0322]

[0349] Although benefits, other advantages, and solutions to problems have been described above with respect to specific embodiments, no benefit, advantage, solution to a problem, or any element that may give rise to or make more pronounced any particular benefit, advantage, or solution, should be construed as a critical, required, or essential feature or component of any or all claims.

Claims

1. 1. A system comprising: a. a core microcontroller processor including a Bluetooth Low Energy (BLE) radio; b. a first triad low inertia acceleration and rate gyrometric sensor; c. a second triad high inertia acceleration and rate gyrometric sensor; d. A triad magnetometer sensor; e. a storage unit; f. A Qi wireless charging controller; g. A battery monitor; A system comprising:

2. The system is programmed to identify a user swing profile, the identifying the user swing profile comprising: a. calculating an initial position and an initial attitude of the wireless sensor at a timing when the swing starts based on actual acceleration data received from the sensor; b) Detecting an actual impact timing based on the received actual acceleration data, calculating a moving direction of a ball striking surface of a golf club and an impact posture of the ball striking surface at the actual impact timing, and calculating an incidence angle of the ball striking surface at the actual impact timing and a relative angle of the ball striking surface at the actual impact timing based on the moving direction and posture of the ball striking surface at the actual impact timing; c) Based on the received actual acceleration data, further detect repetitive waveforms in the sine wave associated with initial address, peak back, top, impact (peak forward), ideal impact and peak forward simultaneously, recoil, and finish; d. Displaying projection-related data based on the calculated incidence angle and the calculated relative angle of the ball-striking surface at the actual impact timing. The system of claim 1 , comprising:

3. The system of claim 1, wherein the processor is further programmed to obtain a face vector indicating the initial attitude of the ball-striking surface of the wireless inertial sensor at a timing of the start of the swing based on the actual acceleration measurement device received from the wireless inertial sensor.

4. The system according to claim 1 , wherein the processor is further programmed to calculate a movement direction vector of the ball-striking surface at a timing when the swing starts based on the acquired face vector.

5. 2. The system of claim 1, wherein the processor is further programmed to obtain, based on the output received from the wireless inertial sensor, a first relationship between the direction of movement of the ball striking face of the golf club at the start of the swing and the impact attitude of the ball striking face at the start of the actual impact, and a second relationship between the attitude of the ball striking face before the start of the swing and the direction of movement of the ball striking face at the start of the swing.

6. The system of claim 5 , wherein the processor is further programmed to output data indicative of at least one of the first relationship and the second relationship.

7. The system of claim 6 , wherein the processor is further programmed to control a display to display data illustrating a combination of the first relationship and the second relationship in a two-dimensional graph.

8. The system of claim 7 , wherein the processor is further programmed to control the display to display a map including areas divided according to projectile types and the two-dimensional graph.

9. 6. The system of claim 5, wherein the processor is further programmed to control the display to display multiple data relating to multiple swings at multiple times using a two-dimensional graph and to distinguish recent data from other non-recent data in the two-dimensional graph.

10. 2. The system of claim 1, wherein the output received from the wireless inertial sensor used to determine the orientation of the ball striking surface at the time of the actual impact during the swing is received at the time of the actual impact, prior to the time of the actual impact, or after the time of the actual impact.

11. 1. A method comprising: Calculating an initial position of the wireless inertial sensor at a timing of starting a swing based on actual acceleration data received from the wireless inertial sensor, the wireless inertial sensor being attached to a shaft of a golf club and configured to measure acceleration; Detecting an actual impact timing based on the received actual acceleration data; Calculating a moving direction of the ball-striking surface of the golf club and an impact posture of the ball-striking surface at the actual impact timing; calculating an incidence angle of the ball-striking surface at the time of the actual impact and a relative angle of the ball-striking surface at the time of the actual impact based on the movement direction and the attitude of the ball-striking surface at the time of the actual impact; displaying projection-related data on a display based on the calculated incidence angle and the calculated relative angle of the ball-striking surface at the actual impact timing; The method includes:

12. The method of claim 11, further comprising obtaining a face vector indicating the initial attitude of the ball-hitting surface of the wireless inertial sensor at a timing when the swing starts based on actual acceleration data received from the wireless inertial sensor.

13. The method according to claim 12 , wherein a movement direction vector of the ball-striking surface at the timing of starting the swing is projected onto a predetermined plane that intersects with the ball-striking surface in a vertical direction.

14. The method according to claim 11 , further comprising: calculating a movement direction vector of the ball-hitting surface at a timing when the swing starts based on the acquired face vector.

15. 12. The method of claim 11, wherein the output received from the wireless inertial sensor used to determine the orientation of the ball striking surface at the time of the actual impact during the swing is received at the time of the actual impact, prior to the time of the actual impact, or after the time of the actual impact.

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