Electronic networked intelligent fitness training coach system

Abstract

A networked fitness system provides real time feedback to a user in the proper operation of a stacked weight fitness machine, while aggregating the user data for the benefit of both the user and the fitness facility. The device automatically connects the user's portable electronic device to the stacked weight fitness machine and provides valuable feedback to the user as to proper machine usage in real time. The aggregated fitness data provides fitness suggestions to the user as well as guidance to the fitness facility to minimize user attrition, increase machine utilization, and identify potential equipment issues.

Description

BACKGROUND

[0001] In general, fitness equipment (also referred to as exercise equipment) is used to provide fixed or adjustable amounts of resistance to enhance an exercise routine. For example, a weight machine is a piece of fitness equipment that uses gravity as a source of resistance and simple machines to convey the resistance of gravity to the user of the machine. A weight machine may have a set of rectangular plates with a vertical bar running through the plates, where the vertical bar has holes through which a pin can be inserted to lift the plates above the pin when the bar rises. A weight machine may also be plate-loaded that uses round weight plates that are manually placed onto horizontal loading pegs attached to the machine's lever arms. When the user pushes or pulls the handles, the lever arms pivot against the resistance created by the loaded plates. In this manner, the machine can provide for measured resistance and safe movement over the same range of motion. A fitness facility typically houses a variety of fitness equipment, such as multiple stacked weight machines, and users move between different machines during a session at the facility.DRAWINGS

[0002] The Detailed Description is described with reference to the accompanying figures. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.

[0003] FIG. 1 is a block diagram illustrating an electronic networked intelligent fitness training coach system in accordance with example embodiments of the present disclosure.

[0004] FIG. 2 is a flow diagram illustrating a user experience for an electronic networked intelligent fitness training coach system in accordance with example embodiments of the present disclosure.

[0005] FIG. 3 is a diagrammatic illustration of an electronic networked intelligent fitness training coach system, where the electronic networked intelligent fitness training coach system is implemented using exercise equipment in accordance with example embodiments of the present disclosure.

[0006] FIG. 4A is a front detailed view of a range of motion sensor, such as the range of motion sensor of FIG. 3, that measures movement of a pulley in accordance with example embodiments of the present disclosure.

[0007] FIG. 4B is a side detailed view of a range of motion sensor, such as the range of motion sensor of FIG. 3, that measures movement of a pulley in accordance with example embodiments of the present disclosure.

[0008] FIG. 5A is a side elevation view of an enclosure for housing a microprocessor, communications interface, and a power source in accordance with example embodiments of the present disclosure.

[0009] FIG. 5B is a top view of an enclosure of FIG. 5A.

[0010] FIG. 5C is a top view of an enclosure of FIG. 5A.

[0011] FIGS. 6A and 6B are diagrammatic illustrations of an electronic dashboard for a user application in an electronic networked intelligent fitness training coach system in accordance with example embodiments of the present disclosure.

[0012] FIG. 7A is a diagrammatic illustration of a smartphone mount for an electronic networked intelligent fitness training coach system in accordance with example embodiments of the present disclosure.

[0013] FIG. 7B is a diagrammatic illustration of a smartphone mount for an electronic networked intelligent fitness training coach system in a closed position without a smartphone, in accordance with example embodiments of the present disclosure.

[0014] FIG. 7C is a diagrammatic illustration of a smartphone mount for an electronic networked intelligent fitness training coach system in an open position with a smartphone, in accordance with example embodiments of the present disclosure.

[0015] FIG. 8A is a diagrammatic illustration of a weight pin tracker for a range of motion sensor, with a microprocessor, communications interface, and a power source in accordance with example embodiments of the present disclosure.

[0016] FIG. 8B is a diagrammatic illustration of a weight pin tracker for a range of motion sensor of FIG. 8A with an exercise equipment, in accordance with example embodiments of the present disclosure.

[0017] FIG. 9 is a diagrammatic illustration of a non-contact distance measurement device (e.g., laser measurement device, light detect and ranging (LiDAR) device, ultrasonic measurement device, etc.) mounted to the top of an equipment frame in accordance with example embodiments of the present disclosure.

[0018] FIG. 10 is a diagrammatic illustration of a network for an electronic networked intelligent fitness training coach ecosystem in accordance with example embodiments of the present disclosure.

[0019] FIG. 11 is a diagrammatic illustration of a weight stack machine with top mount range of motion sensor and side (or rear or front) mount weight sensors both directed at the top plate in a weight stack in accordance with example embodiments of the present disclosure.

[0020] FIG. 12 is a diagrammatic illustration of the weight stack machine illustrated in FIG. 11, where a user has lifted the weight stack according to a prescribed range of motion (ROM), and the combination of the ROM and the user's weight stack height (Hs) is sufficient to clear the side (or rear or front) mount weight sensor, which identifies an air gap and is able to identify the weight lifted in accordance with example embodiments of the present disclosure.

[0021] FIG. 13 is a diagrammatic illustration of the weight stack machine illustrated in FIG. 11, where the combination of the user's ROM and weight stack height (Hs) is not sufficient to clear the side (or rear or front) mount weight sensor in accordance with example embodiments of the present disclosure.

[0022] FIG. 14 is a diagrammatic illustration of a weight stack machine with top mount range of motion sensor and multiple side mount (or rear or front) weight sensors, where the top mount sensor and one side mount sensor are both directed at the top plate in a weight stack, and where the other side mount sensor is directed at a lower plate in the weight stack in accordance with example embodiments of the present disclosure.

[0023] FIG. 15 is an optically-transparent anti-glare label for a display of a cardio equipment console in accordance with an example embodiment of the present disclosure.

[0024] FIG. 16 is a display of an cardio equipment console with the optically-transparent anti-glare label of FIG. 15 in accordance with an example embodiment of the present disclosure.DETAILED DESCRIPTION

[0025] Globally, there are more than 150,000 fitness clubs, supporting more than 130 million members. The U.S. alone supports over 35,000 fitness facilities, serving more than 50 million consumers. As fitness continues to be recognized as an integral part of a healthy lifestyle, fitness clubs have become a regular part of many Americans'daily lives. Fundamentally, each club offers a combination of strength and cardio equipment, typically through dedicated equipment for specific exercises.

[0026] Whereas fitness awareness and equipment continue to grow and evolve, the actual consumer experience and fitness efficiency have been severely neglected. Consumers generally arrive at a fitness facility and use a series of strength and cardio equipment with which they have only some or limited familiarity. They are often required to remember seat settings, weights, and their workout regimen. Users are also responsible for their own use of the equipment. Examples of questions a user may have at a fitness club include the following: “Am I going through a full range of motion? ”“Am I going too fast or too slow? ”“Am I pushing hard enough or too hard? ”“Which equipment should I be using to achieve my fitness goals? ”“When should I increase weights or sets to continue progressing? ”

[0027] Historically, fitness clubs have generally offered tracking sheets (e.g., “blue cards”) that can retain settings and workout plans, allowing them to be used to record actual workouts. This solution requires diligence on the part of a user and is simply a static record of workouts. Whereas identifying a user's trends and issues over a series of workouts would be very valuable, this is simply not very viable, given the limited tools available, and is rarely accomplished. Today's fitness goers generally rely on their own memories for seat settings, weights lifted, and progress. Users today tend to focus on a limited set of exercises (as this is what they can remember), which inherently limits the ability to achieve fitness objectives.

[0028] A further disadvantage of current processes is within the fitness club itself. A fitness club is a relatively poor subscription service, where the average membership turnover is typically around half of the club's membership every nine months. This puts many clubs in a very challenging economic condition, with limited resources available to invest in enhanced member benefits.

[0029] Members generally leave a club because they are frustrated, bored, or otherwise unsatisfied with their experience and / or results. The club's main objective is to attract and retain members, but identifying those members who are at risk of quitting is next to impossible without data. If a member actually used a tracking sheet, it is possible (but rarely actually done) to search for at-risk members. Generally, clubs simply provide a facility, stock it with a bunch of equipment, and hope for the best. This typically makes for a challenging financial business model. For many commercial clubs, the solution is to set their subscription model at a price that is low enough for members to continue paying, regardless of whether the program is actually working to help them achieve their fitness goals. For example, if a club charges $25 per month, even though a member may not be making progress, perhaps hopefully next month will provide more time and success, making it worthwhile for the member to continue the subscription.

[0030] Various solutions to the blue card problem have been attempted through the years. Personal trainers are a good option, but are not only very expensive, but also suffer from a lack of detailed historical information on the client. Some limited number of members will use tracking apps on their phones, which provide a record of key facts, but no guidance.

[0031] Technology solutions, such as Technogym and, more recently, eGym, attempt to address these problems; however, they generally require an initial investment of many tens of thousands of dollars, plus an annual recurring subscription cost. Some of these solutions only work with a facility's specialized equipment, and the equipment is upcharged significantly, often by about $10,000 to $15,000 per piece. This results in a six-figure investment for the full cost of a networked system, and most fitness clubs are hard-pressed to justify this investment.

[0032] An additional problem identified by members in a fitness club is efficiently queuing up for equipment. Each fitness club user has their own sequence of equipment to use in a specific fitness plan, and other members'usages can conflict with that plan. This often requires a combination of patience and flexibility that can frustrate a user and lead to a sub-optimal workout. Thus, despite decades of health and fitness industry evolution, the process for engaging fitness club clients and driving fitness success has failed to keep pace.

[0033] Gym owners, users, and equipment manufacturers all face challenges when it comes to accessing and linking data from cardio machines, both in the gym and at home. Most machines operate within closed ecosystems, meaning data is often locked behind brand-specific apps or platforms. For gym owners, this creates operational challenges and limits visibility across a diverse range of equipment. Users struggle to track progress consistently when switching between brands or locations. Meanwhile, manufacturers must choose between building costly integrations or paying to partner with other systems, resulting in both options being expensive and inefficient.

[0034] An ideal solution to these challenges would meet several criteria: the solution would (1) easily network and connect a user to each machine in a workout protocol, (2) provide a user with simple, efficient, real-time guidance and tracking for workouts with recommended behavior changes required, (3) use a combination of the workout data and user-provided goals and / or objectives to guide the user to enhance workouts, (4) provide the fitness facility guidance on where and how to engage with users to minimize attrition, (5) serve as an attraction mechanism for new members, to the benefit of the fitness facility, and (6) be affordable to both gym users and facility owners. For example, an ideal solution would be having a full time, very competent personal trainer with access to all historical performance and real time accurate feedback.

[0035] A solution that supports and engages the consumer, while providing attraction and retention benefits to the club, and minimizing the cost impact to both parties, would be highly desirable. The key attribute of addressing the cost issue while retaining the consumer and customer benefits is the ubiquity of the smartphone. Whereas previous solutions required significant dedicated electronics on each machine, these can now be replaced by the smartphones owned by the vast majority of consumers using fitness facilities. Members who do not own a smartphone may be loaned a dedicated smartphone (or other portable electronic device with similar features) during their workout. The facility may also have a dedicated smartphone or tablet (or other portable electronic device with similar features) permanently mounted on each machine. A solution that retrofits with existing fitness machines would also minimize the cost impact to the facility.

[0036] Thus, in accordance with the present disclosure, an electronic networked intelligent fitness training coach system for weight stack equipment can be used to measure the movement of the weight stack, providing feedback on range of motion, speed, and / or repetition counts, and monitor the user's workout for safe motion. Sensing the movement of the weight stack can be achieved through the use of an encoder (optical or through-hole) on one of the machine pulleys, and / or through the use of an accelerometer on the pin used to select the desired weight to lift, and / or through the user of a laser, optical or ultrasonic sensor. Additionally, if desired, the actual weight lifted could be measured through the use of optical sensors or strain gauges, which can be communicated to the user at each machine.

[0037] In some embodiments, a fitness machine may also have the ability to identify and connect with a user as the user approaches a given machine. This can be achieved with a network solution, such as Bluetooth Low Energy (BLE), a QR code to identify and pair the user to the machine, and so forth. As described, the electronic networked intelligent fitness training coach system enables intelligent fitness training and tracking through Internet of Things (IoT) sensors, artificial intelligence (AI)-based algorithms, and so forth.

[0038] In some embodiments, weight stack equipment used with an electronic networked intelligent fitness training coach system may have one or more of the following attributes: (1) a communications device and / or a QR code to connect with a user as the user approaches the machine, (2) a sensor to identify the user's range of motion (e.g., distance and / or speed), (3) a microprocessor to process data from sensor(s) and share with the communications interface, (4) a power source to provide power to the system, such as a regenerative power system (e.g., solar power) and / or a battery, (5) a software utility that provides guidance to the user and / or guidance to the fitness facility, (6) a physical platform to hold the user's smartphone or other electronic device while the user is exercising on the stacked weight equipment, (7) a physical platform to hold the smartphone in a position facing the user to measure and monitor the posture of the user, (8) one or more sensors to track the weight being lifted by the user, and so on.

[0039] In some embodiments, a utility to track all non-weight stack equipment may also be included in the electronic networked intelligent fitness training coach system. For example, within the smartphone software solution (hereinafter referred to as an “app”), an interface to manually input additional exercises, such as free weights and body weights, can be included. Free weights and body weight exercise can be easily accounted for in the app. For example, the app can guide the user toward appropriate free weights, body weights, or other non-sensored weight stack equipment, suggest personalized weights and numbers of repetitions (referred to herein as “reps”), and so forth. Furthermore, used in conjunction with an accelerometer-fitted fitness tracker (e.g., a Fitbit, an Apple watch, etc.), the reps may be counted and tracked in the app.

[0040] For instance, with cardio equipment (e.g., a treadmill), detailed workout information may be automatically generated, e.g., using one or more artificial intelligence algorithms (hereinafter referred to as “AI”) to read summary results generated by the cardio equipment, from photos taken, and / or manually entered into a user's app. The AI may also calculate the calories burned based on the manually entered exercise activities paired with the user profile (e.g., age, gender, weight, or the like). Recommendations from the app can convey the fitness plan, while the results can be communicated to the app. Other fitness activities can be manually logged into the app, providing a complete fitness profile for the user.

[0041] An additional benefit of this smart equipment solution is leveraging the AI algorithms to manage the flow of users through the fitness equipment. By understanding each user's workout routine, the algorithms can redirect users to avoid conflicts, delays, and / or abandonments of aspects of the fitness routine. For instance, if user ‘A’ is on a leg press and the next machine in the user's workout sequence is the leg extension machine, which is currently occupied by user ‘B’, user ‘A’ typically chooses to change the desired workout sequence to another machine. However, user ‘B’ may be nearly finished with a prescribed workout. The AI algorithm, understanding this data, continues to direct user ‘A’ to the planned next machine (e.g., the leg extension machine in this example). Similarly, if two users, per their workout plan, are both heading toward the same machine, the AI algorithm can redirect one user accordingly.

[0042] In an example scenario, a non-app user is occupying one of the networked machines. The machine in use can register the usage and can anticipate time of use and / or next machine. For example, where the weight stack on the leg press has moved four (4) times, and the adjacent machine is a leg extension, it can be assumed by the algorithm that the non-app user has eight (8) more reps, and will then move to the adjacent, muscle group similar leg extension machine. Thus, an on-app user can be moved away from the leg extension, anticipating the sequence of the non-app user.

[0043] Additionally, AI aspects of a software algorithm may also provide further benefits to a fitness facility. For example, by understanding the frequency and / or duration of equipment usage, an algorithm can propose an optimized equipment portfolio to maintain, increase, or phase out specific gym equipment. Also, by comparing the equipment usage frequency and duration to benchmark data across fitness locations, the algorithm can propose potential usage, plan maintenance, or identify potential equipment issues. By understanding the aggregated sequencing of machines, the equipment layout could change. For example, perhaps the leg curl should be utilized before the leg extension as that may be a more typical sequence, and will minimize movement and crossing of gym members.

[0044] Generally referring now to FIGS. 1 through 13, an electronic networked intelligent fitness training coach system 100 is described. The electronic networked intelligent fitness training coach system 100 includes an exercise equipment 131 that further includes a microprocessor 101, which can be a microprocessor configured to process device handshakes, process data from sensors (e.g., range of motion sensor 104, weight sensor 122, accelerometer 112, measurement component 124, weight pin tracker 125, non-contact distance measurement device 128, weight sensor 130, or the like), send data to user smartphone devices (e.g., smartphone 116) using communications interfaces (e.g., communications interface 102), and so forth. The electronic networked intelligent fitness training coach system 100 also includes a range of motion (ROM) sensor 104. The ROM sensor 104 can be used to identify a range of motion, including distance and / or speed. The ROM sensor 104 can be implemented via, but not limited to, one or more of the following: an encoder (e.g., optical), a non-contact distance sensor (e.g., a laser distance sensor, an ultrasonic distance sensor), and / or an accelerometer (e.g., as more fully discussed with reference to FIGS. 3, 8 and 9). The electronic networked intelligent fitness training coach system 100 can also include a weight sensor 122, which is used to track the weight being lifted by a user.

[0045] The electronic networked intelligent fitness training coach system 100 can further include a power source 103 that provides power to the system. In some embodiments, the power source 103 can be a regenerating power source, for example, a rechargeable battery and a regenerating charger. The electronic networked intelligent fitness training coach system 100 can also include a communications interface 102, which can be a communications device configured to send equipment information and data from sensor(s) to user smartphones. The electronic networked intelligent fitness training coach system 100 can further include user app 121 and customer app 123, which can be a software utility or utilities that provide guidance to the user. In some embodiments, user app 121 and customer app 123 can also provide user and / or machine management capability to a fitness facility.

[0046] The electronic networked intelligent fitness training coach system 100 can also include a smartphone mount 105, which can be a physical platform to hold a user's smartphone (e.g., smartphone 116) while the user is exercising on the exercise equipment 131, e.g., to ensure the user has line of sight to the phone screen and perhaps has a camera facing the user, e.g., for posture review, reading the weight lifted. The smartphone mount 105 can securely hold the smartphone 116 to ensure security during equipment utilization.

[0047] When a user is ready for a workout, the user starts by activating the user app 121 on the smartphone 116 (i.e., step 202). The user approaches an exercise equipment 131 and places the smartphone 116 on a smartphone mount 105 that is coupled to the exercise equipment 131 (i.e., step 204). The proximity of the smartphone 116 and the communications interface 102 and perhaps a machine identifier (e.g., a QR code) allows the user app 121 to start communicating with the exercise equipment 131 via a communication protocol like Bluetooth Low Energy and / or one or more other similar protocols (i.e., step 206). The microprocessor 101 pairs the sensors on the exercise equipment 131 (e.g., a bicep curl machine) to the smartphone 116. The user app 121 recognizes the exercise equipment 131 and opens a dashboard that displays target weights, repetitions, seat settings, workout progress, and so forth (i.e., step 208). The user prepares the weight and seat settings, takes a seat, and starts the workout (i.e., step 210).

[0048] In step 212, as the user lifts the weight or weights (e.g., weight stack 113), the ROM sensor 104 measures the movement of the weight stack 113 and sends the data to the microprocessor 101 (i.e., step 214). The microprocessor 101 processes the data into movement data (e.g., to calculate distance) and time (e.g., to calculate speed) and sends the data to the user app 121 via the communications interface 102 (i.e., step 216). The user app 121 receives the data and displays workout progress on the dashboard (i.e., step 218).

[0049] In step 220, the user app 121 determines, based on the ROM sensor 104, whether the velocity of the weight stack 113 is changing during any given lift and / or during a given set. This information can be used to identify whether the weight may be too much or too little for the user. If the user moves too fast (e.g., if a measured number of counts per period exceeds a target number of counts in the user app 121) and / or if the user overextends (e.g., if a measured count from the ROM sensor 104 exceeds a target count in the user app 121), the user app 121 warns the user to slow down and / or not to overextend, since any jerking motion during an exercise can lead to injury. In step 222, when the user completes the desired number of reps, the user app 121 can congratulate the user on completion and suggest the next workout equipment (i.e., step 224). The user picks up the smartphone 116 from the smartphone mount 105 (i.e., step 226), and the user app 121 disconnects from the communications interface 102 (i.e., step 228).

[0050] In embodiments of the disclosure, the wireless / cordless power source 103 for the electronic networked intelligent fitness training coach system 100 powers the microprocessor 101, communications interface 102, sensors, and so forth. The power source 103 may have a non-rechargeable battery, a rechargeable battery with a charger powered by light, vibration, noise and / or movements, an alternating current (AC) or direct current (DC) connection, and so on. The electronic networked intelligent fitness training coach system 100 may also include the weight sensor 122 to measure the weights lifted. The weight sensor 122 may be implemented using one or more of a strain gauge, a proximity sensor, a load cell, and so forth, to track the weight lifted.

[0051] With reference to FIG. 3, an electronic networked intelligent fitness training coach system 100 is implemented via exercise equipment 131. The microprocessor 101, communications interface 102, rechargeable battery / power source 103, and a light emitting diode (LED) 127 are housed within an enclosure 119 (e.g., a housing). The enclosure 119 may have other electronics inside and / or on its surface to generate power from light (indoor and / or outdoor), noise, vibration, and so on. There may be a wire connecting the circuit in the enclosure 119 to the ROM sensor 104. The ROM sensor 104 can be mounted on a pulley housing 117 to track the movement of the pulley 114. The smartphone mount 105 can be mounted on a frame 132 of the exercise equipment 131, allowing the user to place the smartphone 116 within communicating distance with the communications interface 102. The enclosure 119 can be mounted on the frame 132 of the exercise equipment 131 using adhesive tape and / or mounting hardware, and may display indicia, such as an identifying corporate logo, information identifying the specific enclosure and / or its included components, and so forth.

[0052] Referring now to FIG. 4, the ROM sensor 104 can be an optical encoder 133 (which may include, but is not limited to, an encoder bracket 106, encoder circuit 107, and an encoder wheel 109) to measure at least one of the distance to a weight stack 113, a displaced distance of the weight stack 113, and / or the movement of a weight machine pulley 114. The pulley 114 can be attached to, for example, a weight stack belt 115. The pulley 114 can be supported by, for example, a pulley bolt 118. In an example embodiment, a certified technician or installer attaches an encoder wheel 109 on the pulley 114 and drills a hole 108 (e.g., a hole of about one-inch (1″) diameter or less) on the pulley housing 117. The technician reinstalls the pulley 114 and attaches an encoder bracket 106 on the surface of the pulley housing 117. An encoder circuit 107 houses an optical encoder chip 135 and is placed in the hole 108 to identify and count the lines 134 on the encoder wheel 109 as they pass by the optical encoder chip 135. The data from line count and time is transmitted to the microprocessor 101 to be processed and transmitted to the smartphone 116 via the communications interface 102.

[0053] With reference to FIG. 5, an enclosure 119 for the ROM sensor 104 can include a microprocessor 101, a communications interface 102, a power source 103, and an indicator LED 127. In some embodiments, the enclosure 119 can be a dome housing. Some or all of these components may be mounted on a circuit board. Input / output (I / O) connectors can be used to connect power to the sensors (e.g., the ROM sensor 104), receive data from the sensors, and so forth. There can also be an ambient electronic recharging component 110 mounted on the enclosure 119 to charge the rechargeable battery / power source 103. For example, a solar panel can be mounted on the surface of the enclosure 119 to provide electricity from light. The indicator LED 127 may light to inform the user of the location of the fitness equipment. The indicator LED 127 may also light up to inform the user that the fitness equipment is reserved and / or temporarily unavailable. The enclosure can be fully sealed to avoid cleaning chemicals, humidity, and / or sweat from penetrating through the enclosure 119 to the circuits. The enclosure 119 may also have a flat bottom 136 to provide a surface for the enclosure 119 to adhere to the frame 132 using strong adhesive tape (e.g., 3M VHB tape), or perhaps a physical clamp, screws, and / or other hardware to hold the enclosure 119 to the frame 132. Various electrical components within the enclosure 119 can be in electrical communication with each other using electrical wires 120 (e.g., including data transmission wires).

[0054] Referring now to FIGS. 6A and 6B, a smartphone 116 with user app 121 displaying example dashboard layouts are described. The user app 121 can identify the next equipment for a workout sequence, and can cause LED 127 to light and / or can cause other visual or audio communication on an exercise equipment 131 to inform the user of the location of the next piece of equipment for the workout. Further, when the smartphone 116 and the user app 121 connects to the exercise equipment 131, the user app 121 can identify a type of the exercise equipment 131 and display the user settings and configuration for that equipment (e.g., as described with reference to FIG. 6A). If there is no existing setting and configuration, the user may set up themselves or request a trained technician and / or trainer to help the user prepare the equipment correctly. Otherwise, if the information is available and shown, the user prepares the equipment according to the settings shown on the screen (e.g., weight settings, seat settings, and so forth). In embodiment, once the user has completed their last set on the exercise equipment 131, the user reserves on the user app 121 the next piece of exercise equipment (i.e., another exercise equipment 131) to be used. In this embodiment, in response to the reservation, an LED 127 on the other exercise equipment 131 changes from green to red for a predetermined period of time to indicate to other users that the other exercise equipment 131 is reserved.

[0055] Once complete, the user starts the workout. When the user lifts the weights, the movement of the weight stack 113 is measured by the ROM sensor 104, and the movement is transmitted to the user app 121 via the communications interface 102. The user app 121 can display the data as progress movement on the dashboard (e.g., via a graphical progress ring with a percentage complete, as illustrated with reference to FIG. 6B). The user app 121 can also display the number of reps completed. If the user overextends the range of motion (ROM), the user app 121 can display a warning message indicating that the user has overextended the movement and needs to keep within a target range. If the user moves too fast, the user app 121 can display a warning message asking the user to slow down. In some embodiments, the acceptable range of motion and / or rate of movement can be adjusted based on criteria specific to the user or the workout. For example, the acceptable rate of movement can be decreased where slower reps (“negatives”) have been added to achieve specific health benefits. Once complete, the user has the option to transmit the workout data (e.g., number of reps, weights, calories burned, etc.) to a wearable fitness tracking device (e.g., a Fitbit, an Apple Watch, etc.). The user app 121 may also display suggestions of improvements for a workout, a marketplace of “designer” workout routines for purchase, and so forth.

[0056] With reference to FIG. 7, a smartphone mount 105 can be provided for the user to place a smartphone 116 on the strength equipment. In some embodiments, the smartphone mount 105 can be made of plastic and / or metal and can be mounted on the machine. A tray 137 for the smartphone 116 can be closed when not in use to prevent inadvertent contact when the machine is in use. The tray may have a groove 138 to place the smartphone 116 horizontally and / or a spring-attached back to hold the smartphone 116 in place. In an embodiment, when the user is at the exercise equipment 131, the user flips down the tray 137 and a back holder automatically flips open. The user places the smartphone 116 in the groove 138 on the tray 137 and starts the workout. Once completed, the user removes the smartphone 116 and flips up the tray 137 to avoid inadvertent contact. A trained technician and / or installer can install the smartphone mount 105 to ensure the mount is at an appropriate height, viewing angle, distance, and so on. The smartphone mount 105 may articulate for the user to adjust the viewing angle to ensure the user has a line of sight to the screen. The user can verify that a camera on the smartphone 116 can have a line of sight to the user (e.g., to monitor user posture, read the weight lifted).

[0057] Referring now to FIGS. 8A and 8B, another arrangement with a weight pin tracker 125 that mounts the microprocessor 101, the communications interface 102, the power source 103, the recharging component 110, and a vertical measurement component 124 in the head of a weight pin 111 is described. The vertical measurement component 124 can be implemented using an accelerometer 112. In some embodiments, the vertical acceleration of the weight can be correlated to distance using double integration, a conversion algorithm that can be executed by the microprocessor 101. The vertical distance may also be measured with other non-contact vertical measurement components 124, including, but not necessarily limited to: (infrared (IR)) distance sensors, ultrasonic sensors, and so forth. In some embodiments, the vertical movement of a weight stack can be used to charge the battery of the power source 103, e.g., using one or more ambient electronic recharging components 110, such as a piezoelectric device that uses lead zirconate titanate (PZT) and converts movements / vibrations to electric currents (e.g., an energy harvester). In this type of implementation, the electronic networked intelligent fitness training coach system does not necessarily include ROM and / or weight sensors as previously described.

[0058] With reference to FIG. 9, a further arrangement that uses a non-contact distance measurement device 128 (e.g., a laser measurement device, an infrared measurement device, an ultrasonic measurement device, etc.) can have one or more non-contact measurement devices 128 mounted on the top of the frame 132 of the exercise equipment 131. The location of a non-contact distance measurement device 128 on the frame 132 allows the measurement device to have a direct line of sight to the top of the weight stack 113 and to measure the vertical movement of the weight stack 113 by measuring the distance between the non-contact distance measurement device 128 and the top of the weight stack 113. In some embodiments, white tape, paper, film, and / or a white or otherwise generally reflective coating may be placed on the top of the weight stack 113 to facilitate better visibility for the non-contact distance measurement device 128. The microprocessor 101, communications interface 102, power source 103, and / or LED 127 can be housed in the enclosure 119. The enclosure 119 may have other electronics inside and / or on its surface to generate power from ambient light, noise, vibration, and so forth. In some embodiments, there can be wire(s) and / or other electrical connector(s) connecting a circuit or circuits in the enclosure to the non-contact distance measurement device 128. A smartphone mount 105 can be mounted on the frame 132 of the exercise equipment 131, allowing a user to place a smartphone 116 within communicating distance for the communications interface 102. The enclosure 119 can be mounted on the frame 132 using adhesive tape, a physical frame clamp, screws, and so forth, and may display a corporate logo and / or other indicia.

[0059] Referring now to FIG. 10, a network 139 for an electronic networked intelligent fitness training coach system 100 is described. When the fitness equipment communicates with a user app 121 in a smartphone 116, the smartphone 116 connects with a cloud-based application 129, e.g., via cellular data, a Wi-Fi network, a proprietary wifi network (e.g., Miwi) and so forth. The cloud-based application 129 can transmit relevant data to the smartphone app 121 based on user profile, location, equipment connected, and so forth. The cloud-based application 129 may also have artificial intelligence (AI) capabilities to provide recommendations to a user on workout options and / or to send data to a customer app 123 via a wired / wireless connection to inform the customer (e.g., gym owner / operator) of the user's workout profile. There may be user profile data (e.g., seat settings, weights, repetitions) stored locally in a user app 121 for quick access (i.e., not requiring an Internet connection). This information can be transmitted to the cloud-based application 129 once the smartphone 116 has Internet access. There can also be a network operating center (NOC) 126 connected to the cloud-based application 129 to monitor and / or analyze user data. In some embodiments, the NOC 126 and / or the cloud-based application 129 may have algorithms and / or use AI to conduct analysis from the data received. Such analysis may be used to provide recommendations to the user for progressive (e.g., progressive resistance training, linear variable resistance, etc.) or regressive (e.g., scaling back exercises to address user injury or form). For example, the AI algorithms can use a combination of real-time and historical data received from the ROM sensor 104, the user profile, and other data sets to provide recommendations for increasing or decreasing resistance or load (e.g., increasing / decreasing reps in each set, increasing / decreasing number of sets in the workout, increasing / decreasing weight, increasing / decreasing rest time between sets, etc.). Such analysis may also include understanding how fitness equipment is used, configuring a desirable site layout for fitness equipment accessibility and / or convenience, monitoring the status of fitness equipment (e.g., identifying erratic data indicating, for instance, a need for technical repair), analyzing user health and wellness, recommending additional wellness services to users, communicating with users to guide and motivate them, and so forth.

[0060] With reference to FIG. 11, a stacked weight machine (i.e., exercise equipment 131) using two optical sensors both targeting the top plate in the weight stack 113 is described. In this example, an additional weight tracking sensor 122 is used in conjunction with a top mounted weight stack tracking sensor (i.e., range of motion sensor 104) to accurately measure the weight being lifted. In embodiments, a top mounted ROM sensor 104 is mounted above the weight stack 113, looking downward to track the motion of the weight stack 113. The ROM sensor 104 is used to measure the user's range of motion, count reps, and / or to understand if the user is properly lifting the weight. In some embodiments, an additional weight sensor 122 can be used to determine the weight being lifted in the vast majority of lift scenarios. For example, the weight sensor 122 can be used to determine how many weights in the stack are being lifted by comparing when the weight sensor 122 stops detecting an adjacent weight to whether the weight stack is still moving as measured by the ROM sensor 104. The number of weights can be counted to determine the amount of overall weight lifted. The system may also query the user to manually input the weight when the weight sensor 122 is not triggered during a set (e.g., the user is lifting a very heavy weight).

[0061] As shown, the exercise equipment 131 may be used for a lat pulldown or forearm pull down (e.g., as shown), and the techniques, systems, and apparatus described herein can also be used with various stacked weight machines and machine arrangements. As described, the ROM sensor 104 can be oriented vertically to track the motion of the weight stack 113, and the weight sensor 122 can be positioned horizontally and oriented toward the middle of the top plate.

[0062] Referring now to FIG. 12, the exercise equipment 131 described with reference to FIG. 11 is shown in use. It should be noted that each plate of the stacked weight 113 on a stacked weight machine (i.e., exercise equipment 131) is typically the same weight, and the thickness of each plate is typically constant for a particular machine. It should also be noted that where the weights and / or thicknesses of the plates differ on a stacked weight machine, variances can be identified and recognized by the electronic networked intelligent fitness training coach system 100, e.g., by storing predetermined information about a weight stack 113 to facilitate a determination of how many weights and / or groups of weights in the stack having particular characteristics are lifted, e.g., where each weight and / or group or set of weights may have different weight and / or thickness characteristics, so that the overall weight lifted can be determined by the electronic networked intelligent fitness training coach system 100.

[0063] During installation and setup of an electronic networked intelligent fitness training coach system 100, each weight machine (i.e., exercise equipment 131) can be characterized such that the weight and height per plate of the weight stack 113 is stored by the system 100 and can be used to determine the overall weight lifted. Further, each user can set up and configure each machine (i.e., exercise equipment 131); a simple process that can be done by the user and / or with the help of a personal trainer / coach. For example, in some embodiments, the user and / or a personal trainer / coach can perform the following sequence of actions: given the user's specific geometry, a proper seat setting or settings may be identified; the user lifts the weight stack 113 or a portion thereof to identify a lift height (HL); the user moves through the ROM; and an initial weight is identified. In the example shown, the user has placed a pin in the fourth weight plate (as illustrated in FIG. 12), meaning the user is lifting four times the weight of one weight plate. As the user has pegged the fourth plate, the height of the stack (Hs) equals four times the height of a single plate. It should be noted that the user typically lifts the weight stack to a certain height (e.g., lift height HL) before beginning the exercise through the range of motion (ROM). This is so the lifted stack does not impact upon the remaining plates at the bottom of the ROM during repetitions. In an embodiment, the ROM is determined when the user performs a set of the exercise motion (in some instances, at a lower weight selection) and the user app 121, based on a measured aggregate of the user's exercise motion, determines an average ROM. In some embodiments, the average ROM excludes 10% of the extremes of the exercise motion for tracking purposes.

[0064] Scenario 1: The lift height (HL) is greater than (>) the stack height (Hs). ROM sensor 104 monitors the vertical movement of the weight stack 113. Weight sensor 122 views the middle of the top weight plate horizontally. Weight sensor 122 is triggered when it no longer sees a plate. For the present example, each plate is one inch (1″) thick and weighs ten pounds (10 lbs.). If, via ROM sensor 104, the electronic networked intelligent fitness training coach system 100 determines the weight stack 113 has been lifted an inch, and weight sensor 122 is still blocked by the weight stack, then more than one plate (i.e. more than ten pounds (10 lbs.)) is being lifted. In the above example, where four plates have been pegged (i.e., the user is lifting forty pounds (40 lbs.)), weight sensor 122 can indicate a gap after the weight stack has been lifted four inches (4″). Because the lift height is greater than the stack height, the weight being lifted is known before the user begins moving the stack through the ROM.

[0065] Scenario 2 (as shown in FIG. 13): The lift height (HL) plus the ROM is greater than (>) the stack height. In this scenario, the user lifts the weight stack 113 to the start of the ROM, but the weight sensor 122 is still blocked by the weight stack. As the user begins lifting the stack through the ROM, as long as the weight sensor 122 can detect an air gap (i.e., the bottom of the lifted stack can be identified), the weight lifted can be properly identified. In an example, the lift height (HL) is three inches (3″), the lifted stack height is four inches (4″), and the ROM is six inches (6″). The user lifts the stack three inches (3″) to begin the ROM. At this height, the weight sensor 122 is still blocked. But once the stack is lifted through the ROM, as soon as the stack moves one more inch upward, the weight sensor 122 can detect an air gap, and the system 100 can determine that at four inches (4″) of travel the weight sensor 122 was triggered, and hence forty pounds (40 lbs.) were lifted.

[0066] Scenario 3: In this example, the weight sensor 122 never triggers because the stack height (Hs) is greater than the lift height (HL) plus the ROM. This may be an unlikely scenario for users of the system 100. For example, this scenario would indicate a very heavy weight that would likely be used by a very experienced fitness member who would more likely be using free weights and / or other similar equipment. Also, for many machines, the lift height (HL) can be very large, so this would be limited to a few machines. However, if or when this scenario is experienced by the electronic networked intelligent fitness training coach system 100, the system 100 can identify this scenario and simply ask the user enter the weight being lifted.

[0067] With reference to FIG. 14, an example is described where the overall weight lifted may be determined regardless of the number of weights in the stack. In this example, an additional horizontal weight sensor 130 is included (and possibly more, depending on lift height, etc.) In this example, more than one sensor can be used (1) when tracking all plates on the weight stack where the total weight stack height (e.g., number of weight stacks times thickness per weight stack) is greater than (>) the ROM, (2) when the ROM is low (e.g., for neck exercise equipment) and the total weight stack height (e.g., number of weight stacks times thickness per weight stack) is greater than (>) the ROM, and so forth. In these examples, the position for a weight sensor 130 can be at least approximately the length of the ROM below the weight sensor 122, rounded to the nearest middle of the weight stack. For example, if the ROM is three and two-tenths inches (3.2″), a weight sensor 130 position can be three and one-half inches (3.5″) below the top of the weight stack. Likewise, another weight sensor 130 position can be seven inches (7″) below the top of the weight stack. In an embodiment, a single weight sensor 130 is positioned to measure the bottom weight (such as shown in FIG. 14) is used to determine that the entire weight stack is lifted by the user.

[0068] In the following discussion, real-time workout coaching using sensor data on exercise equipment 131 is described. The electronic networked intelligent fitness training coach system 100 can be used to capture data from sensors on exercise equipment 131 and transmit the data via wired and / or wireless (e.g., Bluetooth) transmission to one or more user devices to display workout coaching in real time, evaluate the exercise range of motion, count repetitions, and so forth.

[0069] As described herein, an electronic networked intelligent fitness training coach system 100 can use distance sensor data and weight stack sensing data to inform the user in real time via an app on a portable mobile device, such as a phone, when one or more of the following conditions are present: (A) an incorrect weight is selected for a particular user at a particular machine, (B) workout effectiveness for a particular user is compromised based on lifting and / or dropping weights too quickly, (C) a user has overextended or underextended the ROM (i.e., exceeding the start and / or end point of the user's targeted ROM), (D) an evaluation of the quality of a set is desired by a particular user and can be determined by considering the speed of the weight stack during each subsequent repetition (note: each set may contain multiple reps), helping to establish whether the user is using too much or too little weight, or perhaps the user may be physically impaired and should not be lifting as much weight as is typical for that particular user, (E) a user has allowed the weights to slam at a particular stacked weight machine, (F) a repetition count is desired by a particular user and is determined based on completion from start to end point of the ROM, and so forth.

[0070] Rather than a workout tracker (i.e., counting reps for users), the electronic networked intelligent fitness training coach system is unique as a “coaching partner” that monitors in real-time and corrects the user during a workout to ensure the user is doing the workout correctly.

[0071] In accordance with embodiments of the disclosure, a user device (e.g., mobile phone) mounting bracket for an electronic networked intelligent fitness training coach system is described. A mounting bracket with articulating arms can help place a display screen of the user device in line of sight of the user during a workout and perhaps ensure a camera or other imaging device from the phone is facing the user during the workout for purposes such as posture monitoring and reading the weight lifted from the machine. The mounting bracket places the user device within the line of sight of the user. For some equipment, attaching the mounting bracket directly to a frame of the fitness equipment can properly achieve line of sight with the user. For more difficult placements, a more complex solution can be provided.

[0072] The mounting bracket for the user device can include a mounting plate, one or more ball joints (or the like), an arm, a universal clamp, and so forth. The mounting plate may be attached to the frame using adhesive tape, clamp or set screw. A first ball joint can allow the arm to swivel in a particular direction. A second ball joint can allow the universal clamp to face the user directly. In some embodiments, the first ball joint and / or the second ball joint may be fixed or otherwise locked into position with fasteners (e.g., set screws) to ensure that a user does not move the position of the mount after it has been set. The universal clamp can be used to hold mobile devices in place regardless of the type or form factor of the device. As described, in this arrangement the mobile device is very visible to the user during the exercise.

[0073] In accordance with embodiments of the disclosure, a user device (e.g., mobile phone) mounting pedestal for an electronic networked intelligent fitness training coach system can be placed at a comfortable line of sight during workout and not create obstruction in the gym. A mounting pedestal with adjusting height, low profile adhesive feet, and swivel holder can be used to place the display screen of the mobile device in a comfortable line of sight for a user during a workout. A mounting pedestal can include low profile, wide feet with adhesive bottoms to hold the mounting pedestal in place and prevent tip-over. Various adhesives can be used depending upon surface types (e.g., different adhesives for carpeted floor vs. non-carpeted floor). The mounting pedestal can have a slim body form factor with adjustable, and possibly lockable, height. The mounting pedestal can also have a universal electronic device holder that can swivel. In some embodiments, the body of the mounting pedestal can be painted in a bright color (e.g., white) to improve visibility.

[0074] As described herein, an electronic networked intelligent fitness training coach system 100 can use readings from sensors, paired with one or more features in a software application, to help a user identify a target weight for a particular exercise. Typically, a user has a live person or personal trainer to help identify an appropriate target weight and number of repetitions per set. However, in the current challenging environment where personal trainers are scarce are scarce and / or expensive, hardware and software utilities can support the target weight identification. Once a user has set an equipment setting, a setup program proposes a target weight, e.g., based on the user's body weight and height. The user completes one or more repetition(s), and the electronic networked intelligent fitness training coach system 100 informs the user as to whether an appropriate weight was lifted and if the ROM was completed correctly (i.e., no jerks, a controlled drop). If the user has an acceptable ROM, the user continues testing with increased weights until the ROM is not in good form. For example, current literature proposes a target weight of seventy percent (70%) of the final weight. If the user does not have a good ROM, the user continues testing with reduced weights until the ROM is in good form. In an example, a target weight can be seventy percent (70%) of the weight before the final weight.

[0075] As described herein, an electronic networked intelligent fitness training coach system 100 can predict exercise machines to use and exercise equipment settings based on user data (e.g., age, gender, workout goals, height, weight, arm length, leg length) and historical data sets. Typically, a user needs a live person or personal trainer to help set the user up to use the exercise equipment correctly. This may include: seat height setting, seat back setting, arm handle position, leg pad setting, and so forth. However, the electronic networked intelligent fitness training coach system can narrow equipment settings based on the user's body parameters, e.g., gender, height, weight, arm span, leg length, and so on. Using these measurements, the electronic networked intelligent fitness training coach system can identify the most common equipment settings, e.g., based on historical data for the same exercise equipment brand. The user can also leverage a personal trainer to identify the settings and overwrite the settings.

[0076] As described herein, an electronic networked intelligent fitness training coach system can allow a user to set a top and bottom target Range of Motion (ROM) for each piece of equipment. By setting the top and bottom target ROM, the user can receive the benefits of strength training with full range of motion, such as increased gained strength, muscle growth, flexibility, reduced stiffness, and so forth. Setting the target top and bottom target ROM may also reduce the potential for overextension of joints and / or additional stress on joints.

[0077] Typically, a user requires a personal trainer to help set the top and bottom target ROM to use the exercise equipment appropriately. The following process describes a method whereby a user can self-set the ROM: (A) pin to a light weight, helping to reduce stress on the user's joints, (B) follow system recommendations for seat settings, and then set seat settings before proceeding to the next step, (C) if there were no system recommendations on seat settings, set the seat settings to the recommended workout position as shown on the strength equipment, (D) lift the weight and extend to the furthest possible lifting position (e.g., if setting up a leg press, push on the leg platform until legs are straight and knees are locked), holding this position briefly for the system to record (e.g., starting with the lowest weight), (E) return the weight to the furthest possible returning position (e.g., if setting up a leg press, return the weight by bending the knee to the maximum comfortable position). Repeat for multiple times (e.g., 5 times) to get a proper reading of the minimum and maximum ROM.

[0078] As described, the electronic networked intelligent fitness training coach system can take both maximum positions and apply a reduction (e.g., a reduction percentage to be determined by brand and / or equipment type) to calculate the recommended top and bottom target range of motion. As an example, if the reduction for a particular brand of lat pulldown is machine is twenty percent (20%), and if the top maximum position is ten inches (10″), the top range of motion can be set at eight inches (8″).

[0079] Another impediment to the use of weight stack equipment is the issue of queuing. A user may have a sequence of machines they are planning to use, but there may be another user interrupting that sequence. As each of the machines have sensors, the system has the ability to understand which machines are being utilized at any point and direct the user to an alternate machine while a given machine is being used, regardless if the other users on machines are using the app, LED lights and / or other indicators can be used on the machines to direct a user to a given machine.

[0080] In some embodiments, a user may prefer not to see a screen for coaching, or it may not be possible to position the screen in front of a particular user. In these situations, there can be an audio capability, where workout instructions are transmitted to the user over a user's wireless headset (e.g., ear buds). The audio instructions may include the following: (A) a motivational voice to encourage a user to complete a repetition, (B) an acknowledgement that the user has reached the ROM limit, (C) a warning if the user exceeds the ROM limit, (D) a warning if the user lifts or drops weights too fast, (E) a warning if a weight lift is incorrect, (F) a reminder on the remaining reps and sets to complete, (G) a notification on completion of a workout, and so forth.

[0081] Such immersive audio coaching provides audio guidance for optimal motivational experience real-time audio feedback on the user's workout. In embodiments, the audio coaching provides motivation to the user to help the user complete their reps. For example, provide unique messages as the user is halfway through as detected and counted by the ROM motion sensor 104 such as “You can do it! You're almost there!” In embodiments, the audio coaching provides a warning to the user, such as when the ROM motion 104 sensor detects high acceleration that may suggests jerking motion, and deliver messages like, “Watch your lift!”, or if the user has jerky motion a few times consecutively, the audio coaching may provide feedback such as, “I suggest that we end our workout today to avoid further injuries.”

[0082] A key advantage of the electronic networked intelligent fitness training coach systems 100 described herein is the interaction of the sensors with the user app 121, particularly utilizing the feedback of the current workout with the historical performance to identify and suggest workout modifications. The top mount sensor can identify and track the velocity of the weight stack during a rep, set, and workout. If the user experiences a notable decrease in velocity during a session and / or during a given lift / set / workout, it is possible that the weight is more than the user should be lifting. Further, if the user is having to accelerate aggressively at the beginning of a lift, again this could indicate too much weight. If, for example the user is not seeing any decrease in weight stack velocity during a set or workout, more weight may be justified. The variance in velocity of the weight stack could be telling. Perhaps a member tends to slow down toward the end of a rep. If that dynamic becomes more pronounced, it could be a sign of too much weight, or, alternatively, the member may be ready for an increase in weight.

[0083] As described, the electronic networked intelligent fitness training coach system 100 allows a user to perform self-setup on a new piece of equipment. To set up a member on a machine, three elements can be established: seat position, starting weight, and range of motion (ROM). For example, seat position is generally a function of the height and limb length of the user. Generally, strength equipment designs put the average setting of the machine toward the average size of their users. A smaller user would expect a seat setting to be toward one end of the range, and a taller user would expect the opposite.

[0084] To establish a starting weight, a fitness database may be used as a starting point. Data such as gender, age, and height may be sufficient to estimate a starting weight. The initial starting weight may be sixty percent (60%) of the predicted weight in some examples. If a 40-year-old male of average height is predicted to be able to lift one hundred pounds (100 lbs.), the starting weight may be sixty pounds (60 lbs.) (note that known physical impediments may lower that weight further). In some embodiments, the weight stack sensor can determine whether this weight is too much or too little. With patience, the recommended weight can settle to the proper weight as the member has a few sessions with the equipment.

[0085] Additionally, the range of motion may be readily set. In embodiments, once the target weight is established, the user app 121 can have the user set the weight at fifty percent (50%) of the target weight (in the above, example this would be thirty pounds (30 lbs.)). The user is then instructed to raise the weight to the top of their range and then lower the weight to the bottom of their range. This should be quite easy with the reduced weight. The app can then take about ten percent (10%) of the ROM off the top and bottom to determine the setting. In some embodiments, a fitness club may choose to offer to help set up a user / member at the beginning of their fitness journey. But sometimes this will not be the case, and when a user / member chooses to add a machine, the user can do this without needing to search for a trainer. This is particularly helpful if the user app 121 suggests adding a new machine(s) to the workout portfolio. Avoiding having to find a trainer to set up a single machine would be a significant benefit for both the user and the facility.

[0086] Additionally, the user can also choose to start training with low to zero effort using a fully automated configuration. In embodiments, the user app 121 provides automatic configuration on the weight machines based on the users profile. For example, the user app 121 can utilize the database of other users with similar profiles based on the user's age, gender, height, and weight to suggest a typical range value for the exercise based on the measurement from the ROM sensor 104. In an embodiment, the range value r0 is the difference between the top range and bottom range values. The electronic networked intelligent fitness training coach system 100 then determines the distance d0 from the ROM sensor 104 to the top of the weight stack 113 when the weight stack 113 is at rest. In embodiments, electronic networked intelligent fitness training coach system 100 includes a buffer distance b0 to ensure the user does not slam the weight stack 113. For example, the buffer distance can be 1″. Thus, with these values, the user's top range of motion is determined by the equation d0−b0−r0, and the user's bottom range of motion is determined by the equation d0−b0. In this arrangement, the electronic networked intelligent fitness training coach system 100 uses sensors mounted on exercise equipment 131 to monitor how users perform each workout and applies AI to determine when it's safe and effective to increase intensity, such as adding more weight. By analyzing physical metrics like range of motion, lifting speed, weight control, and consistency in completing reps and sets, the system identifies “clean reps” or “quality reps,” which are repetitions executed without issues. A high ratio of clean or high-quality reps indicates readiness to progress to greater weight loads. The system 100 also enhances safety by detecting risky movements like sudden jerks, warning the user in real time, and even pausing workouts if injury is suspected. In doing so, it acts like a live personal trainer, optimizing results while protecting users and reducing liability for gym owners.

[0087] In embodiments, the system 100 uses measurements corresponding to physical attributes collected from sensors placed on exercise equipment 131 and combine the measurements with algorithms and AI to make recommendations for the users. For example, the system 100 determines when the user is ready to increase the weight lifted, the system 100 uses data from the physical sensor to detect the number of issues identified in a workout session. Issues identified may include the following data collected from sensors, but not limited to: lifting or dropping too quickly, lifting target weight or above target weight, exceeding target range of motion, completing a rep without multiple attempts, completing all target sets and repetitions. Based on the sensor data above, the system 100 will determine if the user is ready based on the number of times the user executed the “clean reps” consecutively. “Clean reps” is calculated with the numerator being the count of repetitions that do not have any issues listed above, and the denominator being the total count of repetitions for that workout session. Clean reps are thereby used as a metric to characterize member success and the impetus to suggest an upgrade (e.g. higher weight).

[0088] In embodiments, as the top mounted sensor (i.e., ROM sensor 104) is monitoring the movement of the weight stack 113, if the sensor 104 identifies, for example, sharp, aggressive motion at the beginning of a rep, and particularly if that occurs with each rep, the user is likely jerking the weight stack 113 which exposes the user to injury and suggests the user is attempting to lift too much weight. As a live personal trainer would do, the system 100 warns the user to avoid jerking the weight stack 113 and likely lower the weight. Furthermore, as a connected AI personal trainer, the system 100 can shut the user out of the workouts for the day if it senses injury and even potentially inform the gym owner to avoid any potential liability.

[0089] In embodiments, the electronic networked intelligent fitness training coach system 100 can include cardio information in a user's overall workout portfolio. The system 100 captures (e.g., using a camera) data from cardio equipment. A user uses the exercise equipment 131, and upon completion, captures a picture of the console. The system 100 then uses an AI tool to then analyze the picture, pulling out relevant data, such as the type of equipment (e.g., a stepper, a treadmill, etc.), the distance travelled, the time of the workout, calories burned, and so forth. This data can then be transferred to the user app 121, giving the user additional information to work with to optimize the workout experience. This allows users to seamlessly log their cardio exercises across different equipment types and brands. Today, to enable a user to extract and log their data from their cardio exercise workout, the user typically does the following: see the console and write down or manually enter the information in a mobile application, or use a mobile application that is connected to the equipment manufacturer via an API and the equipment is also connected to the internet. The proposed solution allows a user to use the user app 121 to take a photo of the workout summary when it is displayed on the console. The user app 121 adjusts the exposure and shutter speed to capture the text clearly in the photo and submit the photo to an artificial intelligence (“AI”) processing API with a specific prompt to extract and log their relevant fields for the user. The AI tool identifies the specific display, records the data, and parses into the user app 121 appropriately. Doing so, the user can log across most cardio exercise equipment types and brands.

[0090] Referring now to FIGS. 15 and 16, in further embodiments, the electronic networked intelligent fitness training coach system 100 employs an optically-transparent anti-glare label 140 applied to a display 141 of the cardio equipment console 142 proximate to one or more numerical readouts (e.g., readouts 143A, 143B, and 143C). The label 140 comprises a clear, potentially borderless polymer substrate 144 (e.g., PET or polycarbonate) with a micro-etched matte and / or anti-reflective coating that reduces reflections and bloom under harsh gym lighting while maintaining high transparency for human viewing. The label 140 includes a machine-readable identifier 145 (for example, a colored line, a number, a symbol, or a combination) positioned on the console's numeric readouts. When a user captures an image of the console with the user app 121, the app detects the identifier 145 and applies AI to identify and read the cardio results. The label 140 is removable and repositionable, using a low-tack, solvent-resistant adhesive compatible with common console plastics and cleaning agents. During operation, after the user completes the workout and captures an image of the console display 141, the user app 121 with AI locates the identifier 145 and parse values for the identified equipment type (e.g., time, distance, calories) and transfer to the user app 121 for logging. The described label 140 improves accuracy and speed of data capture across brands and lighting environments, while the identifier 145 unambiguously signals which numbers to read, reducing model complexity and training requirements.

[0091] In embodiments, the electronic networked intelligent fitness training coach system 100 can use the camera function on a smartphone 116 to capture the user's form during the exercise and make recommendations. Poor posture can lead to erratic weight stack motion and suboptimal weight life and injury risk. The camera function reviewing the posture, in combination with proper positioning of the phone camera using the smartphone mount 105 and the top mount sensor 104 feedback on weight stack velocity, can identify and flag performance issues associated with poor posture and usage. These issues can then be messaged to the user in real time, e.g., using a short message service (SMS) text message sent to the user device. In this embodiment, electronic networked intelligent fitness training coach system 100 provides real-time monitoring to maintain the user's posture through the use of the camera on the user's phone. Proper positioning of the camera to ensure optimal capture of the user's form requires the arm mount to be fixed in the right position such that when the user places the phone on the mount, the front camera will capture most of the user's body. The technology may utilize the ROM sensor 104 to trigger when to capture the user's form, such as when the user is at the top range of motion or the bottom range of motion. There are a few ways to analyze the form, mainly sending the captured image to a 3rd party generative AI engine to process the image or to utilizes algorithms like superimposing sticks and joints on the human shape to provide a real-time assessment of the posture. For example, electronic networked intelligent fitness training coach system 100 may use use a 3rd party AI engine: the motion ROM sensor 104 informs the system 100 to take a snapshot of the user via the front camera when it triggers the algorithm such as when the user moves too fast or when the user is at the top or bottom range of motion. The snapshot will be sent to a 3rd party Gen AI engine to process for feedback on the user's form. Feedback will be displayed back on the phone or audio for the user to see. In embodiments, for superimposing sticks and joints, when the user is exercising, the motion ROM sensor 104 will move and this movement triggers the code to look for potential posture issues such as the shoulder is not level (change in stick angle that is greater than + / −25 degrees) or the back is slouched (from front-reduction in stick length that represents the human back (from side-change in stick angle that is greater than + / −25 degrees). In embodiments, the user may receive real-time feedback on the form and posture to avoid further injuries, or the user may choose to conduct a form and posture check to make sure that they are working out correctly.

[0092] The system 100 may incorporate one or more cameras positioned either directly on the strength equipment or mounted separately (e.g., on a camera stand) with a field of view directed toward the user and the associated weight lifted (either weight stack or weight plates). Using computer vision algorithms, the system 100 is also capable of measuring the user's range of motion, detecting the weight lifted, and automatically identifies the specific exercise machine in use. This enables the system to provide real-time feedback and enhance data collection without relying solely on equipment-mounted sensors.

[0093] In embodiments, the system 100 further incorporates data from digital health wearable devices, such as smartwatches or fitness trackers, to enhance functionality and user safety. These wearables can be used to capture additional biometric data, including estimated calorie expenditure, which may be integrated into the workout tracking and analysis framework. Additionally, the wearable device may be configured to deliver haptic feedback, such as tactical vibrations that alert the user in real time when their movement exceeds a target range of motion or if they are performing exercises at an unsafe speed. This feedback mechanism serves to reinforce proper form and reduce injury risk during exercise sessions.

[0094] As described, the electronic networked intelligent fitness training coach system 100 can use a virtual or physical machine identifier label, such a QR code and / or one or more other machine-readable codes, together with the wireless connectivity (e.g., Bluetooth low energy (BLE)) to correctly identify which equipment the user is paired to. This may eliminate some degree of complexity of the system, e.g., simplifying pairing complexity by having the user point the camera at the QR code to complete the pairing process.

[0095] In an operational example, a user enters a fitness facility and opens the “app.” The user accepts the correct location of the fitness facility as determined by the “app” location tracker or the gym code entered by the user on setup.

[0096] In embodiments, the electronic networked intelligent fitness training coach system 100 generates a daily workout plan optimized for the user's recent activities, challenges, and goals. In this embodiment, the electronic networked intelligent fitness training coach system 100 provides a dynamic daily workout plan based on the user's recent activities and performance. For instance, using the sensor data to calculate the “clean reps” and combining with the number of times the user performed the exercise, the system 100 will send the data and target sessions per week with a specific prompt to an artificial intelligence (“AI”) processing API to determine what exercises that need to be performed today. At the end of each workout, the performance of the member is reviewed by the system and changes to the balance of the week plan are made. So, for example, if the member skips legs on a given day, that could be added into the next workout plan.

[0097] In embodiments, the user app 121 directs the user to the first exercise in their protocol, potentially using lights attached on top of an exercise machine. Assuming the first exercise is a stacked weight device, the user simply walks over to the device and is identified by the network protocol (BLE, for instance). The user places their smartphone 116 or other portable electronic communications device on a smartphone holder 105 (hardware) mounted on the machine and begins the workout (e.g., hits “Start” or “Go” on the user app 121). The user is instructed by the user app 121 (1) what weight to choose, (2) the seat setting(s), and (3) what the expected workout is (e.g., 2×12 reps). The user then begins the workout, and the sensors identify the range of motion of the workout. The reps are counted, while the user is instructed whether they are properly using the equipment with regards to range of motion (i.e., distance) and speed. A video clip of proper use of the machine may also be included. If a weight tracking utility is included, the actual weight lifted is captured, and, should the user use a different weight than planned, that difference can be communicated to the user.

[0098] After that machine work is complete, the user app 121 instructs the user which machine to use next. The user removes the smartphone 116 from the smartphone holder mount 105 and moves on to the next machine. Should that machine be unavailable, or the user simply chooses to move to a different machine, the user app 121 and network protocol adjust accordingly. In this embodiment, the electronic networked intelligent fitness training coach system 100 optimizes gym management to optimize the flow in a gym, as there are typically more gym users than machine availability. Additionally, users may not be aware of alternative equipment availability in the gym. In an embodiment, exercise equipment 131 is considered occupied when the ROM sensor is engaged, and the exercise equipment 131 is considered available when the ROM sensor 104 is not engaged after a certain time period (e.g., 1 minute). In an embodiment, when an exercise equipment 131 is occupied, the system 100 will suggest another available exercise equipment 131 that is next on the user's workout plan. In embodiments, the hardware on the system 100 may show a red light when the exercise equipment 131 is occupied. With equipment utilization and optimization, gym owners will also be able to understand the frequency of usage of individual strength equipment (whether used by members using the user app 121 or not). This can be used to understand equipment replacement frequency, location optimization, and whether more or less of a given type of equipment is required.

[0099] Should free weights be in the workout, the user app 121 similarly directs the user to the free weights. The user app 121 may not be able to discern the specific workout, but can guide the user similarly on proper form (perhaps through a video) and proper weight and reps. If the user is using an accelerometer-enabled fitness utility (e.g., a Fitbit, an Apple watch, etc.), the counting of reps may be simplified and tracked. In embodiments, free weights can include motion sensors that capture measurements, which is captured and analyzed by the user app 121.

[0100] In embodiments, if the user incorporates a cardio workout (e.g., treadmill, stair stepper, etc.), that workout could either be included manually by the user, or if the cardio device has enabled a handshake with the app, the detailed information is shared. The user app 121 also shares user profile (e.g., age, weight, workout intensity such as slope, speed) with the cardio workout equipment to reduce setup time and effort for the user.

[0101] In embodiments, post-workout, an entire dataset is available. The user is offered feedback on their workouts, tracking progress over time, and can also be offered suggestions on potential improvements. In this embodiment, the electronic networked intelligent fitness training coach system 100 generates a personalized end of workout assessment that assesses how the user performed today based on, but not limited to, feedback from the sensor data (for example: % clean reps), completion of the target sets, and benchmarking against similar values from previous workouts. The system 100 then sends the data set with a specific prompt to an artificial intelligence (“AI”) processing API to create a personalized post-workout summary.

[0102] In embodiments, if the user's goal is weight reduction, for instance, the data may suggest changes to their cardio program, or the integration of additional, specific strength exercises. The user can also choose to link the workout dataset with other fitness tracking apps (e.g., with apps on a Fitbit, an Apple Watch, etc.). The user app 121 may also offer access directly from the assessment (e.g., a “Get Help” button) to a “marketplace” of designer / customized workout routines that the user can purchase and apply to their workout routine.

[0103] In embodiments, the fitness club, if enabled to see this information, gains very valuable information. There are many specific indications of a member potentially vulnerable to abandoning their membership (e.g., a member who rarely adjusts their weights or rarely works out). Running queries against a membership database can identify at-risk members and allow focused engagement with those members by facility staff. The systems, techniques, and apparatus described herein can help to retain members and commensurately improve the financial viability of a fitness facility. Running queries against equipment usage frequency and duration may also allow a club to customize workout flow, equipment portfolio, and / or identify potential equipment failures.

[0104] Further, the inclusion of the electronic networked intelligent fitness training coach systems 100 described herein may be used as an attraction feature for a club. Users are expecting convenience and simplicity, and tracking a fitness routine on a blue card, or in one's mind, is simply not desirable to many members.

[0105] For gym users, they can seamlessly log workouts and stay on track no matter which gym they visit by utilizing the electronic networked intelligent fitness training coach system 100; they can simply enter or scan a gym code to visit. Their workout plan automatically adjusts to match the equipment available at that specific facility, and if certain machines aren't present, the system 100 substitutes with equivalent unsensored stack weights and free weight exercises. For facility owners, this ensures a consistent, high-quality workout experience for members, promotes engagement across locations, and reduces member frustration from unavailable equipment by offering smart alternatives. This is especially beneficial for chained facilities such as hotels and commercial gyms. In embodiments, there is a gym code or QR code available at the gym that is part of the Fit-X network. When the user enters or scans the code, the user's current workout plan will be adjusted to the gym's available equipment. In embodiments, the user's current weight will be matched to the closest weight available for that gym equipment. The user's Range of Motion for that specific machine (e.g., Lat Pulldown) will be matched to the new machine's values using the following formula: each strength equipment by exercise type (e.g., Lat Pulldown), brand (e.g., Life Fitness) and model (e.g., Axiom) has an “at rest” (r0) value. This value will be measured by the weight sensor when the user starts the workout. For each exercise type (e.g., Lat Pulldown), the user has a bottom range of motion value (rb) and top range of motion value (rt) where “at rest” value=0. When a user goes to a different gym with an equipment of the same exercise type, the system 100 will recognize that the user's target range of motion as follows: target Bottom Range of Motion=r0+rb; and target Top Range of Motion=r0+rt. In embodiments, if equipment for that specific exercise type in the user's workout plan is not available in the new gym, the system 100 will replace the equipment exercise with a free weight exercise.

[0106] An electronic networked intelligent fitness training coach system, including some or all of its components, can operate under computer control. For example, a processor can be included with or in a system to control the components and functions of systems described herein using software, firmware, hardware (e.g., fixed logic circuitry), manual processing, or a combination thereof. The terms “controller,”“functionality,”“service,” and “logic” as used herein generally represent software, firmware, hardware, or a combination of software, firmware, or hardware in conjunction with controlling the systems. In the case of a software implementation, the module, functionality, or logic represents program code that performs specified tasks when executed on a processor (e.g., central processing unit (CPU) or CPUs). The program code can be stored in one or more computer-readable memory devices (e.g., internal memory and / or one or more tangible media), and so on. The structures, functions, approaches, and techniques described herein can be implemented on a variety of commercial computing platforms having a variety of processors.

[0107] The processor provides processing functionality for the system and can include any number of processors, micro-controllers, or other processing systems, and resident or external memory for storing data and other information accessed or generated by the system. The processor can execute one or more software programs that implement techniques described herein. The processor is not limited by the materials from which it is formed or the processing mechanisms employed therein and, as such, can be implemented via semiconductor(s) and / or transistors (e.g., using electronic integrated circuit (IC) components), and so forth.

[0108] The system includes a memory. The memory is an example of tangible, computer-readable storage medium that provides storage functionality to store various data associated with operation of the system, such as software programs and / or code segments, or other data to instruct the processor, and possibly other components of the system, to perform the functionality described herein. Thus, the memory can store data, such as a program of instructions for operating the system (including its components), and so forth. It should be noted that while a single memory is described, a wide variety of types and combinations of memory (e.g., tangible, non-transitory memory) can be employed. The memory can be integral with the processor, can comprise stand-alone memory, or can be a combination of both.

[0109] The memory can include, but is not necessarily limited to: removable and non-removable memory components, such as random-access memory (RAM), read-only memory (ROM), flash memory (e.g., a secure digital (SD) memory card, a mini-SD memory card, and / or a micro-SD memory card), magnetic memory, optical memory, universal serial bus (USB) memory devices, hard disk memory, external memory, and so forth. In implementations, the system and / or the memory can include removable integrated circuit card (ICC) memory, such as memory provided by a subscriber identity module (SIM) card, a universal subscriber identity module (USIM) card, a universal integrated circuit card (UICC), and so on.

[0110] The system includes a communications interface. The communications interface is operatively configured to communicate with components of the system. For example, the communications interface can be configured to transmit data for storage in the system, retrieve data from storage in the system, and so forth. The communications interface is also communicatively coupled with the processor to facilitate data transfer between components of the system and the processor (e.g., for communicating inputs to the processor received from a device communicatively coupled with the system). It should be noted that while the communications interface is described as a component of a system, one or more components of the communications interface can be implemented as external components communicatively coupled to the system via a wired and / or wireless connection. The system can also comprise and / or connect to one or more input / output (I / O) devices (e.g., via the communications interface), including, but not necessarily limited to: a display, a mouse, a touchpad, a keyboard, and so on.

[0111] The communications interface and / or the processor can be configured to communicate with a variety of different networks, including, but not necessarily limited to: a wide-area cellular telephone network, such as a 3G cellular network, a 4G cellular network, or a global system for mobile communications (GSM) network; a wireless computer communications network, such as a WiFi network (e.g., a wireless local area network (WLAN) operated using IEEE 802.11 network standards); an internet; the Internet; a wide area network (WAN); a local area network (LAN); a personal area network (PAN) (e.g., a wireless personal area network (WPAN) operated using IEEE 802.15 network standards); a public telephone network; an extranet; an intranet; and so on. However, this list is provided by way of example only and is not meant to limit the present disclosure. Further, the communications interface can be configured to communicate with a single network or multiple networks across different access points.

[0112] Generally, any of the functions described herein can be implemented using hardware (e.g., fixed logic circuitry such as integrated circuits), software, firmware, manual processing, or a combination thereof. Thus, the blocks discussed in the above disclosure generally represent hardware (e.g., fixed logic circuitry such as integrated circuits), software, firmware, or a combination thereof. In the instance of a hardware configuration, the various blocks discussed in the above disclosure may be implemented as integrated circuits along with other functionality. Such integrated circuits may include all of the functions of a given block, system, or circuit, or a portion of the functions of the block, system, or circuit. Further, elements of the blocks, systems, or circuits may be implemented across multiple integrated circuits. Such integrated circuits may comprise various integrated circuits, including, but not necessarily limited to: a monolithic integrated circuit, a flip chip integrated circuit, a multichip module integrated circuit, and / or a mixed signal integrated circuit. In the instance of a software implementation, the various blocks discussed in the above disclosure represent executable instructions (e.g., program code) that perform specified tasks when executed on a processor. These executable instructions can be stored in one or more tangible computer readable media. In some such instances, the entire system, block, or circuit may be implemented using its software or firmware equivalent. In other instances, one part of a given system, block, or circuit may be implemented in software or firmware, while other parts are implemented in hardware.

[0113] Although the subject matter has been described in language specific to structural features and / or process operations, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A networked fitness system comprising:at least one sensing device to detect a movement of at least one weight of a weight stack included with a fitness machine;a communications interface to interface between the at least one sensing device and a portable electronic device of a user of the fitness machine; andcontrol programming deployable on the portable electronic device of the user and configured to inform the user of at least one of the following, based upon the detected movement of the at least one weight of the weight stack:at least one usage characteristic for the user of the fitness machine, andat least one progress characteristic for the user with respect to an individualized fitness plan for the user.

2. The networked fitness system as recited in claim 1, wherein the portable electronic device of the user is configured to track at least one of a usage of the fitness machine by the user and a progress on the fitness machine by the user over time.

3. The networked fitness system as recited in claim 1, wherein the portable electronic device of the user is configured to provide at least one improvement recommendation to the user based upon the usage characteristic for the user or the progress characteristic for the user.

4. The networked fitness system as recited in claim 3, wherein the at least one improvement recommendation is from an online source.

5. The networked fitness system as recited in claim 1, wherein the communications interface is configured to interface with a fitness facility in which the fitness machine is located to inform the fitness facility of the at least one usage characteristic for the user or the at least one progress characteristic for the user.

6. The networked fitness system as recited in claim 5, wherein the fitness facility can determine at least one suggested change to the individualized fitness plan of the user based upon the detected movement of the at least one weight of the weight stack.

7. The networked fitness system as recited in claim 5, wherein the fitness facility can track at least one of a usage of the fitness machine by the user and a progress on the fitness machine by the user over time.

8. The networked fitness system as recited in claim 7, wherein the fitness facility can determine at least one of an addition, a subtraction, or a relocation associated with the fitness machine based upon the usage of the fitness machine or the progress on the fitness machine by the user over time.

9. The networked fitness system as recited in claim 1, further comprising a second sensing device to detect a movement of at least a second weight of the weight stack.

10. The networked fitness system as recited in claim 1, further comprising a second sensing device to detect a movement of at least a second fitness machine of the fitness facility by the user, wherein the communications interface is configured to interface between the second sensing device and the portable electronic device of the user.

11. The networked fitness system as recited in claim 10, wherein the portable electronic device of the user is configured to review real time usage of the first fitness machine and the second fitness machine and recommend a sequence of usage of the first fitness machine and the second fitness machine to the user based upon the real time usage.

12. The networked fitness system as recited in claim 1, further comprising a housing for the at least one sensing device, the communications interface, and a power supply, wherein the housing is configured to attach to the fitness machine.

13. The networked fitness system as recited in claim 1, further comprising at least one indicator disposed on or proximate to the fitness machine for indicating the fitness machine to the user.

14. The networked fitness system as recited in claim 1, wherein the control programming is configured to use an image capture device of the portable electronic device to capture cardio workout data and use the cardio workout data to further inform the user on the at least one usage characteristic for the user or the at least one progress characteristic for the user.

15. The networked fitness system as recited in claim 1, further comprising a mount for securing the portable electronic device to the fitness equipment.

16. The networked fitness system as recited in claim 15, wherein the mount can articulate to reposition the portable electronic device.

17. The networked fitness system as recited in claim 1, wherein the at least one sensing device comprises a rotational measuring device for measuring rotation of a pulley connected to the at least one weight.

18. The networked fitness system as recited in claim 17, wherein the rotational measuring device comprises an optical sensor that can sense movement of a disk attached to the pulley via a plurality of holes in the disk.

19. The networked fitness system as recited in claim 17, wherein the rotational measuring device comprises an optical sensor that can sense movement of indicia attached to the pulley.

20. The networked fitness system as recited in claim 19, wherein the indicia comprises a plurality of discrete labels.

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