System and method for monitoring a free weight system

Abstract

A method for monitoring a free weight system, the free weight system comprising a load bearing member (5) having mounted thereon a sensor (1), wherein the load bearing member (5) additionally has mounted thereon at least one weight (6), each weight having mounted thereon a further sensor (1), wherein the sensors (1) comprise an accelerometer (1B) and a gyroscope (1C) and are associated with a weight value, the method comprising the steps of: receiving motion data sets from the sensor (1) and the at least one further sensor; for each motion data set, correcting acceleration data based on gyroscope data; identifying moving sensors (1) based on corrected acceleration data obtained as corrected motion data sets; in the moving sensors (1), grouping the sensors (1) with at least one of the at least one further sensor (1) based on coexisting corrected acceleration data in the respective corrected motion data sets.

Description

Technical Field

[0001] This invention relates to systems and methods for monitoring free weight systems. In particular, the invention relates to the monitoring of free weight systems, wherein the monitoring involves collecting exercise-related data, such as the current system weight and a given number of repetitions. The collected data may also be reported internally and / or externally. Background Technology

[0002] Free weights are any equipment used for weight training that is not integrated into any larger device and is lifted as a whole by one or two hands, such as barbells or dumbbells or adjustable kettlebells or plate load machines.

[0003] Unlike mechanical weight machines, free weights require more force from the individual using the device for training because they do not constrain the user to a predetermined movement.

[0004] Free weight training is very popular due to its wide range of configuration options. Weights placed on the grip of dumbbells or on barbell bars can come in different shapes, sizes, and weights.

[0005] Another advantage is that training can be done anywhere, not just in a gym.

[0006] However, the aforementioned characteristics of free weights also make them difficult to track in an automated way for exercise monitoring. This is especially true when it comes to so-called retrofit systems that have already been manufactured without any sensors or electronic systems to support such automated monitoring. In such retrofit systems, the monitoring system is added after the user acquires / purchases the free weight system.

[0007] The first specific issue is how to detect which weights have been installed on the bar or grip, preferably without attracting the user's attention. Another issue is how to detect the repetitions of the exercise in the case of a free weight system.

[0008] There are also flatbed loaders, where the load is limited by attaching different weights, similar to the barbell case. Different bars are typically provided for load positioning.

[0009] Therefore, it is advantageous to define a scheme for monitoring free counterweights that will be applicable to all types of such devices and, optionally, allow movement from one free counterweight to another, which would be advantageous from the perspective of a home user.

[0010] The purpose of this invention is to develop an improved system and method for monitoring free counterweight systems. Summary of the Invention

[0011] The present invention aims to provide a method for monitoring a free counterweight system, the free counterweight system comprising a load-bearing member having sensors mounted thereon, wherein the load-bearing member further has at least one counterweight mounted thereon, each counterweight having another sensor mounted thereon, wherein the sensors include accelerometers and gyroscopes and are associated with weight values, the method comprising the steps of: receiving a motion dataset of counterweight information from the sensors and at least one other sensor; for each motion dataset, correcting acceleration data based on gyroscope data; identifying moving sensors based on the corrected acceleration data obtained as the corrected motion dataset; and, among the moving sensors, grouping the sensors and at least one of the at least one other sensor into one or more groups based on the corrected acceleration data coexisting in the respective corrected motion datasets.

[0012] Preferably, the corrected acceleration data is obtained by rotating the acceleration vector of the accelerometer by a rotation recognized by the gyroscope.

[0013] Preferably, the corrected acceleration data is obtained by further subtracting the gravity value.

[0014] Preferably, the sensor further includes a magnetometer, and the step of correcting the acceleration data further includes correcting for drift.

[0015] Preferably, communication with each sensor is achieved using wireless communication at speeds below 1 GHz.

[0016] Preferably, the grouping is also based on constant characteristic parameters associated with the sensors and temporal movement similarity between the sensors.

[0017] Preferably, the grouping is also based on one or more previous groupings.

[0018] Preferably, the method further includes: calculating the weight of a particular group based on the weight information of each sensor associated with each of the one or more groups.

[0019] Another object of the present invention is a computer program comprising program code means for performing all steps of a computer-implemented method according to the present invention when the program is run on a computer.

[0020] Another object of the present invention is a computer-readable medium that stores computer-executable instructions that, when executed on a computer, perform all the steps of a computer-implemented method according to the invention.

[0021] Another object of the present invention is a system configured to perform all the steps of the method according to the present invention. Attached Figure Description

[0022] These and other objects of the invention presented herein are achieved by providing systems and methods for monitoring free counterweight systems. Further details and features, the nature and various advantages of the invention will become clearer from the following detailed description of preferred embodiments illustrated in the accompanying drawings:

[0023] Figure 1A An overall overview diagram of the system according to the present invention is presented;

[0024] Figure 1B A diagram of the sensor according to the present invention is presented;

[0025] Figure 2 A diagram of the system according to the present invention is presented;

[0026] Figure 3 A diagram illustrating the method according to the present invention is presented;

[0027] Figure 4 A diagram illustrating the sensor data processing method according to the present invention is presented;

[0028] Figures 5A to 5B The process for correcting acceleration data using gyroscope data from a sensor is shown; and

[0029] Figure 6 An example is presented showing how 8 counterweights (non-filled shapes) and 2 rods (filled shapes) are clustered into 3 groups.

[0030] Symbols and nomenclature

[0031] Some parts of the following detailed description are presented according to other symbolic representations of data processing procedures, steps, or data bit operations that can be executed on computer memory. Therefore, the computer performs such logical steps, thus requiring physical manipulation of physical quantities.

[0032] Typically, these quantities take the form of electrical or magnetic signals that can be stored, transmitted, combined, compared, and otherwise manipulated in a computer system. For reasons of common use, these signals are referred to as bits, packets, messages, values, elements, symbols, characters, items, numbers, etc.

[0033] Furthermore, all these and similar terms will be associated with appropriate physical quantities and are merely convenient labels applied to those quantities. Terms such as “processing” or “creating” or “transmitting” or “executing” or “determining” or “detecting” or “obtaining” or “selecting” or “calculating” or “generating” refer to the actions and processes of a computer system that manipulates data represented as physical (electronic) quantities within the computer’s registers and memory and converts it into other data represented similarly as physical quantities within memory or registers or other such information storage devices.

[0034] Computer-readable (storage) media, as referred to herein, can generally be non-transient and / or include non-transient devices. In this context, a non-transient storage medium can include devices that can be tangible, meaning that although the device can change its physical state, the device has a specific physical form. Thus, for example, non-transient means a device that remains tangible despite changes in its state.

[0035] As used herein, the term "example" means used as a non-limiting example, instance, or illustration. As used herein, the terms "for example" and "for instance" introduce a list of one or more non-limiting examples, instances, or illustrations. Detailed Implementation

[0036] Figure 1A A general overview of the system according to the invention is presented. The system includes at least one sensor 1, which includes an accelerometer and a gyroscope and optionally a magnetometer, and the sensor 1 also includes a communication module, which is preferably a communication module with a speed of less than 1 GHz.

[0037] The system also includes at least one (passive) NFC tag and a data collection device 3. The user's mobile device 4 (such as a mobile phone, tablet, smartwatch, smart fitness tracker, etc.) may include an application configured to communicate with the data collection device 3.

[0038] Such free counterweights / systems include, for example, bar 5 and corresponding counterweight 6.

[0039] Sensor 1 is mounted on the main body of counterweight 6 (such as a counterweight plate), while another sensor 1 is mounted on each end of bar 5 (or equivalent element, which may generally be referred to as a load-bearing member). For dumbbells, these ends will correspond to the ends of the gripping parts, while for flat load equipment, these ends will correspond to the ends of the corresponding bars on which the counterweight plates are mounted.

[0040] NFC tags 2 are preferably also mounted on these ends (one NFC tag is sufficient in principle, however, for a pole such as 2m long, it is convenient to have an NFC tag 2 at each end of the pole 5). These NFC tags 2 carry data and identifiers that identify the properties of the corresponding sensor 1.

[0041] The embedded data of such an NFC tag 2 may include: an identifier ID required by the data collection device 3 to identify and track the movement of the counterweight, the weight, an optional identifier of an associated item (e.g., a rod, grip part, bar, etc.), and other optional attributes such as the size of the associated item (e.g., the length of the rod, the radius of the associated counterweight, etc.).

[0042] This NFC tag 2 supports simple configuration using the user's mobile device 4, but is not essential for implementing the present invention in principle.

[0043] In other words, preferably there is no collective identifier for a group of sensors.

[0044] Such an NFC tag 2 (in another embodiment, there may be only one tag) allows for the pre-configuration of the free weight system and, upon request, provides this configuration to the corresponding application on the user's mobile device. Therefore, when a new user wants to exercise with this pre-configured free weight, the user simply launches the corresponding application on their mobile device 4 and scans the NFC tag 2. The application then sets up free weight monitoring based on the configuration read from the NFC tag 2.

[0045] like Figure 1B As shown, each sensor 1 has its own power source (e.g., battery 1A) and is configured to read signals from an accelerometer 1B, a gyroscope 1C, and optionally a magnetometer 1D. Such signals allow for the description of the movement of a given object on which the corresponding sensor 1 is mounted.

[0046] The optional magnetometer 1D can be used to invoke a setting mode based on a magnetic field exceeding a given threshold. Typically, a relatively strong magnetic field is not present under normal operating conditions. In this setting mode, the weight value can be given and stored as a configuration parameter.

[0047] Magnetometer 1D can also be used to improve the accuracy of motion tracking (of the corresponding sensor 1 and thus its associated counterweight) by correcting drift, for example, according to a process of “drift correction based on static attitude magnetometer”.

[0048] The counterweight and other setting data are preferably transmitted from the data collection device 3, especially when using communication below 1 GHz (as explained in the rest of the specification). This, in turn, means that the user device can preferably not communicate directly with the sensor 1.

[0049] Each sensor 1 communicates with the data collection device 3 via a communication module 1H, which can be any wireless communication, particularly the previously mentioned sub-1GHz communication.

[0050] Each sensor 1 also sends 1H data describing this movement to an external data collection device 3. The controller 1E manages the operation of the sensor 1 and uses the memory 1F to store any temporary data signals when needed. The memory 1F can also store any configuration data or software data executed by the aforementioned controller 1E.

[0051] Communication between different modules of the system can be achieved through a suitable communication bus 1G.

[0052] Data describing movement (or, in other words, motion data) can be provided to the external data collection device 3 as raw data, or in a processed form to reduce the amount of data to be transmitted in a timely manner. This also depends on the capabilities of the controller 1E and the sampling rate applied by the sensor 1.

[0053] The data collection device 3 records and tracks data from multiple sensors 1 and groups them according to the characteristics of the detected movements. Additionally, the data collection device 3 associates the sensors 1 with the bar 5 (which is also associated with a given user during exercise).

[0054] Alternatively, two sensors positioned at the end of bar 5 allow for the detection of irregularities during training, such as gripping the bar in a non-parallel manner (relative to a predetermined horizontal alignment).

[0055] Figure 2 A diagram of the data collection device 3 according to the present invention is presented. The collected data can be further processed to identify exercise repetitions and multiple weights during exercise.

[0056] Preferably, the data collection device 3 records all received signals from the sensors. When the received data is valid, the data collection device 3 attempts to correlate these sensors with other sensors. This makes the system more flexible, as adding components only requires configuring the new sensor 1 for the counterweight.

[0057] This system can be implemented using dedicated components or custom FPGA or ASIC circuitry. The system includes a data bus 201 communicatively coupled to memory 204. Additionally, other components of the system are communicatively coupled to system bus 201, allowing them to be managed by a suitable controller 205.

[0058] The memory 204 may store one or more computer programs executed by the controller 205 to perform the steps of the method according to the invention. The memory 204 may also store any temporary data as results and permanently stored results.

[0059] The data collection device 3 may be powered by the battery 203 and includes a suitable communication device 206, such as Bluetooth, Wi-Fi, ANT+, etc. (preferably, a low-power protocol).

[0060] In one embodiment of the present invention, communication between the data collection device 3 and the sensor 1 can be achieved via a communication medium different from that used for communication between the data collection device 3 and the user's mobile device 4.

[0061] In this embodiment, optional sub-1 GHz communication 202 is used, which allows for a wider range of communication with lower latency and allows for a larger number of sensors. At the same time, energy consumption can be reduced, which is important for devices installed in the appropriate configuration (e.g., smaller batteries can be used).

[0062] The change in communication frequency range and independence from the Bluetooth communication stack offer the aforementioned advantages, but typically result in reduced throughput, and of course, require a dedicated transmitter / receiver.

[0063] When the number of sensors 1 may be large, the data collection device 3 may include multiple communication modules 202, 206 below 1 GHz to support a larger bandwidth.

[0064] Figure 3 A diagram illustrating the method according to the invention is presented. This is a method from the user's perspective. The method begins with step 301, which involves launching a dedicated application on the mobile device 4. Next, at step 302, the user approaches the selected gripping portion, stick portion, or lever 5, and the identifier is read using the NFC tag 2 and the mobile device 4 with the executed application.

[0065] Subsequently, at step 303, the application of mobile device 4 connects to data collection device 3 (which is identified in NFC data tag 2) and thereby notifies the establishment of pairing between a given user (mobile device 4) and a given lever 5.

[0066] Furthermore, at step 304, selected counterweights are installed on rod 5, and each selected counterweight has a sensor 1 installed thereon.

[0067] At step 305, the user exercises, causing the bar and the counterweight mounted thereon to move in space. At step 306, the data collection device 3 performs a classification of the corresponding counterweight (sensor 1) to a set of currently used sensors 1 based on the detected movement characteristics of sensor 1 relative to bar 5.

[0068] Naturally, multiple sensors 1 can be associated / clustered with multiple bars 5, with each bar associated / clustered with a different set of sensors 1. For example, two people each exercise with two dumbbells. Each of the two people configures their movement device 4 to be associated with the corresponding two bars / grip portions of the dumbbells and receives reports on the counterweight that moves only with those two associated bars. Figure 6 Further information on this issue is provided.

[0069] Finally, at step 307, the data collection device 3 transmits data about the exercise to the user's mobile device 4, such as the count of repetitions since the start of the exercise and weight information. The data is associated in pairs with a given bar 5 (or equivalent).

[0070] In other words, the weight of a particular cluster is calculated based on the weight information of each sensor (1) associated with each group / cluster.

[0071] Figure 4 A diagram illustrating a method for processing sensor data according to the present invention is presented. This method is performed by the aforementioned data collection device 3.

[0072] At step 401, pairing is performed with at least two sensors 1 of the counterweight and at least one sensor of the rod 5. The sensors 1 can automatically identify themselves as sensors 1 assigned to the rod 5 or the counterweight; however, alternatively, such assignment can be performed at the data collection device, thereby allowing all sensors 1 to be identical.

[0073] As described above, more than one sensor 1 can be assigned to a single rod 5, and more than one rod can be configured in the system. For example, the first rod B1 has one sensor S1, and the second rod B2 has two sensors S2 and S3 assigned, preferably positioned at the ends of the rod B2. Each rod sensor 1 can also report the weight of the rod 5. In the case where there is more than one sensor on the rod 5, only one of them can report the weight of the rod 5, or each sensor 1 can report a portion of the total weight of the rod 5, for example, in the case of two sensors 1, each sensor reports 0.5 * weight.

[0074] Next, at step 402, a counterweight is assigned to each sensor 1. Sensors 1 are not fixedly assigned to counterweights, as they can be mounted on different counterweights.

[0075] At step 403, after the setup process, the system can detect / collect acceleration and gyroscope data from the sensors (motion or movement dataset for each sensor). This can be done after a clear start signal from the user's mobile device 4.

[0076] At step 404, a process is performed to correct the acceleration data based on the gyroscope data, which will be described in more detail with reference to FIG5. The corrected acceleration data is stored in the corrected motion dataset.

[0077] At step 405, based on the calibrated readings, the sensors are assigned to one of two groups (i.e., a group of moving sensors and a group of non-moving sensors). This can also be referred to as identifying the moving sensors among the sensors from which data is received.

[0078] Furthermore, at step 406, based on one of the multiple rods selected for a given exercise, the moving sensors are assigned to each rod in use. The system knows which sensors are sensor 1 for rod 5 and which sensor 1 are counterweight sensors. Grouping is based on the corrected acceleration data that coexists centrally in the individual corrected motion datasets, as referenced... Figure 5A , Figure 5B and Figure 6 As explained in more detail.

[0079] Sensors (and the items associated with them) can be grouped / clustered based on current relevant movement data, historical data, and / or characteristic parameters such as weight, associated object type, size, etc. Historical data describes the previous movements of the sensors (also grouped). This can also be used to determine the current grouping.

[0080] Grouping can be based on distance parameters between points representing sensors in a multidimensional space (e.g., 3D based on movement and other dimensions such as time, weight, etc.) (e.g., Euclidean distance or using weights representing the importance of a given parameter to a potential grouping). The dimensions correspond to the component dimension of time movement and the measurable characteristic parameters, which are constant. Therefore, sensors fixed to a counterweight / object sharing the same movement will be represented by points in space that are relatively close to each other and far away from sensors moving differently or stationary sensors. Such groups of points in space can be determined using known methods such as k-means, HCA, or density-based clustering.

[0081] Finally, at step 407, the system uses a set of sensors assigned to the selected bar to report repetitions. The repetition count can be further configured to account for movement exceeding a given threshold distance; this can be predefined by the user based on the type of free weight and the type of exercise.

[0082] Figures 5A to 5BThe process of correcting acceleration data using gyroscope data from sensor 1 is shown.

[0083] A problem has been identified whereby the counterweights can rotate on the corresponding bar 5 during exercise. This can lead to incorrect counterweight counts. This problem has been solved by using a gyroscope along with an accelerometer in each sensor 1 mounted on the counterweights.

[0084] Using a gyroscope allows for the detection of the rotation of the counterweight (along with its associated sensor 1) on rod 5, and the conversion of signals from the corresponding accelerometers through the rotation of the coordinate system, thus ensuring that the acceleration caused by gravity is always kept on the same axis. This allows for the clustering of the counterweights.

[0085] Figure 5A Exemplary graphs of the X and Y axes of the accelerometers for sensors S1 and S2 are presented, wherein sensor S1 does not rotate during the movement of the exercise, while sensor S2 rotates 90 degrees during the same exercise, thereby swapping the X and Y axes and holding them in that final position.

[0086] When calibration is applied based on signals from the corresponding gyroscopes, the X-axis and Y-axis curves of the accelerometers of sensors S1 and S2 have Figure 5B As shown in the figure.

[0087] The above correction can be achieved by rotating the accelerometer's acceleration vector by a rotation recognized by the corresponding gyroscope and by optionally subtracting the gravity value from the reading.

[0088] Preferably, the acceleration vector [x, y, z] is given without considering gravity, so that the direction, velocity, position, and gravity vector [x, y, z] can be determined. Based on the gravity vector [x, y, z], the orientation of the object can be determined relative to a reference surface (e.g., a floor).

[0089] Alternatively, without subtracting the gravity value, the data can be standardized so that the gravitational acceleration vector is aligned with one of the three axes x, y, or z.

[0090] Furthermore, the following specific equation can be used. "Angle" is the angle calculated using a sensor fusion method—in this case, it is a complementary filter:

[0091]

[0092] Where α is the tilt, β is the pitch, γ is the yaw, and the A coefficient is determined experimentally based on the characteristics of the gyroscope and accelerometer (e.g., measurement accuracy, maximum measurement error, average measurement error, etc.).

[0093] Assume the data from the accelerometer is in column vector form:

[0094]

[0095] Where x / y / z are the acceleration values ​​on each axis, then the acceleration vector of the 'Angle' rotation in the previous steps... It can be represented as follows :

[0096]

[0097] The resulting vector Obtained as Vector and default gravity vector The difference between them, the value of which can be determined during calibration:

[0098]

[0099] Due to this signal conversion, although the sensors of the counterweights mounted on the rod rotate during the same movement, they will eventually look the same, which will in turn promote their clustering.

[0100] In the data collection device 3, clustering of data obtained from each sensor 1 is performed. In this process, different criteria may be considered, such as at least one of the following: 3D accelerometer data after conversion based on gyroscope data, the level of dynamics of accelerometer data, time, past clustering data, total load of the symmetrical rod, and the correlation between counterweight movement and rod movement.

[0101] Figure 6 This paper presents an example of clustering eight weights (non-filled shapes) and two bars (filled shapes) into three groups by considering raw accelerometer data on the Z and Y axes at a given time t. The figure shows two bars with different movements and their respective weights GO and G1, and a separate set of weights G2, which includes weights not used during the exercise.

[0102] In this arrangement, repeats are preferably determined using only samples from sensors positioned on the pole. This repeat count can be based on a predetermined movement threshold.

[0103] At least a portion of the method according to the invention can be implemented by a computer. Therefore, the invention can take the form of a completely hardware implementation, a completely software implementation (including firmware, resident software, microcode, etc.), or an implementation combining software and hardware aspects, which may generally be referred to herein as a “circuit,” “module,” or “system.”

[0104] Furthermore, the present invention can take the form of a computer program product embodied in any tangible medium having computer-usable program code embodied therein.

[0105] Those skilled in the art will readily recognize that the methods described above for monitoring a free counterweight system can be executed and / or controlled by one or more computer programs. Such computer programs are typically executed by utilizing computing resources in a computing device. The application program is stored on a non-transient medium. Examples of non-transient media are non-volatile memory, such as flash memory, while examples of volatile memory are RAM. The computer instructions are executed by a processor. These memories are exemplary recording media for storing computer programs comprising computer-executable instructions that perform all steps of a computer-implemented method according to the technical concept presented herein.

[0106] Although the invention presented herein has been described and defined with reference to specific preferred embodiments, such references and examples of implementations in the foregoing description do not imply any limitation on the invention. However, it will be apparent that various modifications and changes can be made therein without departing from the broader scope of the technical concept. The presented preferred embodiments are merely exemplary and do not exhaustively cover the scope of the technical concept presented herein.

[0107] Therefore, the scope of protection is not limited to the preferred embodiments described in the specification, but is limited only by the appended claims.

Claims

1. A method for monitoring a free counterweight system, the free counterweight system comprising a load-bearing member (5) having a sensor (1) mounted thereon, wherein at least one counterweight (6) is additionally mounted thereon on the load-bearing member (5), each counterweight (6) having an additional sensor (1) mounted thereon, wherein the sensor (1) comprises an accelerometer (1B) and a gyroscope (1C) and is associated with a weight value, the method comprising the steps of: Receive weight information motion dataset from the sensor (1) and at least one additional sensor (1); For each of the aforementioned motion datasets, acceleration data is corrected based on gyroscope data; Based on the corrected acceleration data obtained as a corrected motion dataset, the moving sensor is identified (1). In the moving sensor (1), based on the corrected acceleration data that coexists in the individual corrected motion datasets, the sensor (1) is grouped into one or more groups with at least one of the at least one other sensor (1).

2. The method according to claim 1, wherein, The corrected acceleration data is obtained by rotating the acceleration vector of the accelerometer by a rotation recognized by the gyroscope (IC).

3. The method according to claim 2, wherein, The corrected acceleration data was obtained by further subtracting the gravity value.

4. The method according to any one of claims 1 to 3, wherein, The sensor (1) also includes a magnetometer (1D), and the step of correcting the acceleration data also includes correcting drift.

5. The method according to any one of claims 1 to 3, wherein, Communication with each sensor (1) is achieved by using wireless communication below 1 GHz.

6. The method according to any one of claims 1 to 3, wherein, The grouping is also based on constant characteristic parameters associated with sensor (1) and temporal movement similarity between sensors (1).

7. The method according to any one of claims 1 to 3, wherein, The grouping is also based on one or more previous groupings.

8. The method according to any one of claims 1 to 3, further comprising: The weight of a group is calculated based on the weight information of each sensor (1) associated with each of the one or more groups.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements all the steps of the method of claim 1.

10. A computer-readable medium storing computer-executable instructions that, when executed on a computer, perform all the steps of the method according to claim 1 implemented by the computer.

11. A system for monitoring a free counterweight system, the free counterweight system comprising a load-bearing member (5) having a sensor (1) mounted thereon, wherein at least one counterweight (6) is additionally mounted thereon on the load-bearing member (5), each counterweight (6) having an additional sensor (1) mounted thereon, wherein the sensor (1) comprises an accelerometer (1B) and a gyroscope (1C). The system is characterized by: The system includes a controller (205) configured to perform all the steps of the method according to claim 1.

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