System for Measuring and Reporting Weight-Training Performance Metrics

Abstract

A system for measuring weight-lifting performance is described. The system comprises a collar that may be attached to a weight bar, wherein the collar comprises at least one motion sensor, at least one touch sensor, and a signal processor. The motion sensors comprise a barometric altimeter, a gyroscope, and/or an accelerometer that measure the motion of the weight bar during a lifting activity. Signals from the motion sensors are interpreted by the signal processor as physical activity data, which are then wirelessly transmitted to a multimedia device having a data processor. The data processor is configured to calculate performance metrics from the physical activity data and to display them to a user via a display on the multimedia device. Athletes may use the performance metric outputs from the data processor to monitor the form of their lifting exercises for training purposes, safety considerations, and self-improvement.

Description

BACKGROUND OF THE INVENTION

This disclosure relates generally to fitness equipment and in particular to a system for measuring and reporting performance metrics during weightlifting exercises.

Weightlifting is a popular form of recreational and competitive exercise. In an effort to improve their performance, athletes and persons may track their strength training progress by recording repetitions and mass lifted. To prevent against injury and to optimize the incremental benefits of training, athletes and persons may monitor their form to ensure proper technique. Because it can be difficult for athletes or persons to assess their own lifting form without an external reference, a means of providing immediate feedback during a workout would improve the efficacy of training regimens. Competitive fitness activities are adjudicated via a combination of repetitions, weight lifted, and form achieved, and so it may be beneficial to an athlete to track these parameters via real time metrics. Having access to said physical activity data can allow a person to measure their performance, reduce injury risk, and make informed decisions about their exercise regimen. Sensors may be used to detect, measure, and transmit physical activity data, and processors within multimedia devices or other computing means may be used to receive and interpret said data and to calculate and display performance metrics calculated therefrom.

SUMMARY OF THE INVENTION

Accordingly, the present invention provides a system for measuring and reporting performance metrics during weightlifting exercises. The system may comprise a collar configured to attach to a weight bar, wherein the collar comprises at least one touch sensor, at least one motion sensor, and a signal processor, wherein the signal processor is configured to receive signals from said touch and motion sensors, determine physical activity data using said signals, and deliver said physical activity data to a multimedia device. The multimedia device (hereafter referred to as “the device”) may comprise a mobile or stationary electronic device, such as a smart phone or tablet computer. The device may be remote from the collar and may further comprise a data processor, wherein the data processor may be configured to receive said physical activity data from the signal processor and calculate performance metrics that characterize an activity performed by a user, such as an athlete or person performing weightlifting exercises. The device may be further configured to store, transmit, or display said performance metrics to said user.

During an exercise regimen, a user may attach the collar to either end of a cylindrical weight bar. The collar may attach via at least one clamp and an articulated hinge, or it may slide onto the bar. The touch sensors may be configured to sense if the collar is attached to the weight bar. The motion sensors may be configured to sense physical motion of the weight bar and to take measurements of the characteristics of said motion. Once the touch sensors indicate that the collar is attached to the weight bar, the signal processor may be configured to receive and record signals from the motion sensors and determine physical activity data therefrom. The sensors and the signal processor may be enclosed by a case attached to the clamp. Alternatively some or all of the sensors may be attached to an external face of said case.

The motion sensors may comprise an accelerometer, a gyroscope, and/or a pressure sensor such as a barometric altimeter. The motion sensors may relay signals to the signal processor and the signal processor may be configured to convert the signals into physical activity data. For example, as the user lifts the weight bar with the collar attached to either end, the signal processor may be configured to interpret signals from the accelerometer to measure the acceleration at which the weight bar is lifted, to interpret the signals from the gyroscope to measure the orientation of the weight bar relative to an axis, and to interpret the signals from the barometric altimeter to measure the height of the weight bar from the ground. The signal processor may further be configured to transmit the physical data over a communications network to the multimedia device. The communications network may comprise a wireless connection between the signal processor and the multimedia device.

The multimedia device may comprise a data processor configured to receive physical activity data from the signal processor and to calculate weightlifting performance metrics from the physical activity data. Performance metrics may comprise parameters that characterize the action taken by the user during an activity involving the weight bar, such as a lift. The data processor may be capable of executing software instructions and may employ a software application or App having an interface configured to accept inputs from a user. The software application or App may be configured to display said performance metrics via a visual or graphical representation on the multimedia device. Performance metrics may be calculated based on input from a user or may be automatically or programmatically determined. Those performance metrics which are determined and displayed may correspond to particular activities undertaken with the weight bar by a user.

The data processor in the multimedia device may also be configured to analyze the performance metrics it has calculated and to output an assessment of the form of a lift of the weight bar with the collar attached thereto, also referred to as the “lift form”. Lift form is an important determination for an athlete undertaking weightlifting activities, as proper lifting technique can prevent injury and improve athletic performance. Lift form shall be understood as a quantitative or qualitative assessment of proper lifting technique that takes into consideration a plurality of performance metrics related to a particular activity undertaken by the user lifting the weight bar. Performance metrics may comprise the velocity at which the weight bar is lifted, the orientation of the weight bar, the height of the weight bar during a lift, the movement path of the weight bar, the number of repetitions of said lift, the duration of lifts and holds of the weight bar, and forces exerted by the user on the weight bar, among others. The data processor may analyze performance metrics and output a grade, score, suggestion, evaluation, and/or other means of assessment to provide feedback to the user.

In certain embodiments, the system of the present invention may further comprise at least two cameras configured to view a user and the weight bar during weightlifting exercises and at least one light mounted to an end of the weight bar. The light may comprise a light emitting diode or another device emitting suitably discernible illumination or spectra, such as infrared light. Said cameras may be configured to discern spectra that matches the emission properties of the light and to transmit images of the weight bar and the light, such as static photos or video, to an image processor, which may be configured to operate image recognition software. The image processor may be installed within a camera rig made proximate to at least one of the cameras. As the user lifts the weight bar, images of the user lifting the weight bar may be taken by the cameras and image recognition software loaded onto the image processor may be used to discern the location of said light in the images. The image processor may be further configured to track the movements of said light in the images and to record the positions of the light in the images as position data. The image processor may be configured to transmit the position data over a communication network to the multimedia device having a data processor. The communications network may comprise a wireless connection between the image processor and the multimedia device. The data processor may be further configured to determine a map of the movement path of the weight bar using the position data, to output the movement path as a performance metric, and to compare the movement path to an expected movement path associated with a particular lifting exercise. The multimedia device may be configured to display the movement path of the weight bar using a two- or three-dimensional graphical representation. The data processor may be further configured to provide a quantitative or qualitative assessment of the user's lift form based on the determined performance metrics.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 displays an isometric view of a collar configured to attach to a weight bar.

FIG. 2 displays an exploded view of the collar depicted in FIG. 1.

FIG. 3 displays a zoomed-out view of a collar attached to a weight bar.

FIG. 4 displays a zoomed-in view of a collar attached to a weight bar.

FIG. 5 displays a display screen of a multimedia device configured to display weightlifting performance metrics.

FIG. 6 displays the system architecture of a first embodiment of the present invention.

FIG. 7 displays a flowchart detailing the process by which the system according to a first embodiment of the present invention obtains physical activity data and outputs performance metrics.

FIG. 8 displays the system architecture of an alternative embodiment of the present invention (a system additionally comprising a plurality of cameras) configured to take images of the weight bar during user activity.

FIG. 9 displays a flowchart detailing the process by which the system of an alternative embodiment of the present invention (a system additionally comprising a plurality of cameras) obtains physical activity data and outputs performance metrics.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 and FIG. 2 display different isometric views of a collar 101, the collar being one element of a weight-lifting performance measurement system in accordance with the present invention. FIG. 1 portrays a non-exploded view of the collar while FIG. 2 portrays an exploded view of the collar with subcomponents separated for illustrative purposes. The collar 101 may comprise a clamp 102 and a case mounted to the clamp, wherein the case may have a base 102 and a cover 103. The collar may further comprise a circuit board 104 enclosed by said case, wherein the circuit board may be mounted onto said base 102 and positioned beneath said cover 103. The base 102 and cover 103 may be integrally formed or they may be separable and connectable components. The case may be connected to the clamp via a fastener, such as a screw, or an adhesive and may be formed of a moldable material, such as plastic. The clamp may comprise an articulated hinge or it may be integrally formed from a ring of material. The clamp may further comprise a clasp for securing the collar around a cylindrical weight bar.

Circuit board 103 may comprise at least one motion sensor, at least one touch sensor, and a signal processor, wherein the signal processor comprises electronic components that collect and process signals from the motion sensors and the touch sensors. Said motion sensors may be selected from at least one of a gyroscope, an accelerometer, and a pressure sensor, such as a barometric altimeter. The motion sensors may be sufficiently accurate to detect small changes in their measured physical parameter. For example, the barometric altimeter may be accurate to at least 10 centimeters. Said touch sensors may comprise pads, screens, or buttons that sense changes in capacitance when in close proximity to, or in contact with, a conductor. The motion sensors and/or touch sensors may be mounted upon at least one programmable chip having an electrical connection to the circuit board. For example, the gyroscope and accelerometer may be mounted to the same programmable chip. The circuit board may further comprise a battery, a battery charging circuit for charging said battery, and a power boost converter for amplifying voltage to the battery charging circuit.

As depicted in FIG. 3 and FIG. 4, the collar 101 may be configured to attach to a weight bar 106. The collar may attach to the weight bar via a clamp feature, a sliding friction grip, or another suitable means of rigidly mounting to a cylindrical surface. The collar may be positioned at any point along the length of the weight bar. The collar may also be configured or sized to preferably be positioned on either of the weight-bearing end segments of the weight bar. As the weight bar is lifted, dropped, or otherwise moved by an active user, the motion sensors electrically connected to the circuit board of the collar may transmit signals to the signal processor, which may be configured to receive as inputs the signals from the motion sensors and to convert said signals to physical activity data. Physical activity data may comprise measurements by the sensors of physical parameters induced by activity taken by the user. For example, as the user lifts the weight bar with the collar attached to either end, the signal processor may be configured to interpret signals from the accelerometer to measure the acceleration at which the weight bar is lifted, to interpret the signals from the gyroscope to measure the orientation of the weight bar relative to an axis, and to interpret the signals from the barometric altimeter to measure the height of the weight bar from the ground.

Said physical activity data may then be transmitted by the signal processor to a multimedia device. The multimedia device may comprise a data processor that determines performance metrics using the physical activity data received from the signal processor. As depicted in FIG. 5, sensors 110 relay signals to signal processor 120, which converts the signals to physical activity data and then transmits said physical activity data to a data processor 150 within a multimedia device 140 via a communication network 130. Physical activity data may be transmitted wirelessly, for example, via Bluetooth© or Bluetooth© low energy technology. Performance metrics may comprise parameters that characterize actions taken by the user during an activity involving the weight bar, and those performance metrics which are determined may correspond to particular activities. For example, performance metrics may comprise lift velocity, which is the change in position of the weight bar during the time of the lift, lift force, which is the force the user exerts to raise the weight bar, and lift height, which is the vertical distance the weight bar is lifted from the ground as measured at the apex of the lift. Other performance metrics may include the orientation of the weight bar, such as its tilt or deviation from a horizontal axis, the movement path of the weight bar, i.e. the tracked the number of repetitions of a lift, the duration of a lift, and the duration of a hold during a lift, among others. The data processor may be capable of executing software instructions and may run a software application or App having an interface configured to accept inputs from a user. The software application may utilize algorithms to calculate performance metrics by taking as inputs the physical activity data received from the signal processor. The software application or App may be configured to display said performance metrics via a visual or graphical representation on the multimedia device, such as via a display screen. Performance metrics may be calculated based on input from a user or may be automatically or programmatically determined. Alternatively, or in addition, the data processor may upload said performance metrics to another multimedia device or the internet. Said multimedia device may comprise a personal computer, mobile tablet, smart phone, or activity tracker.

The performance metrics output by the data processor may also comprise lift form. Lift form shall be understood to be a qualitative or quantitative assessment of a performed lift's proper technique, defined by predetermined values for performance metrics and movement paths of the weight bar as tracked by the data processor. Analysis of calculated values for performance metrics by the data processor may result in the indication of proper or improper lift form depending on their comparison to said predetermined values. The predetermined values may be defined by the user and provided to the software application as inputs. Alternatively, the data processor may flag a lift as having proper or improper form based on pre-programmed parameters that correspond to expert opinions, athletic research, or weight training techniques known to those skilled in the art.

An athlete, trainer, or other user may utilize the system in accordance with the present invention to record performance metrics during weight training exercises and display them via a multimedia device. The system may operate in the following manner, the process of which is depicted in FIG. 6. First, the collar may be attached to the end of the weight bar. A touch sensor mounted to the collar and comprising a capacitive touch sensor may then be used to sense if the collar is on the weight bar (step 601). The user may then lift the weight bar while the signal processor in the circuit board receives signals from motion sensors, such as the accelerometer and gyroscope, as well as touch sensors (step 602). Signals from the motion sensors may include acceleration force and tilt measurements from an accelerometer, angular velocity and orientation measurements from a gyroscope, and atmospheric pressure measurements from a barometric altimeter. The signal processor may determine physical activity data from said measurements. For example, the signal processor may determine the height of a lift from the change in atmospheric pressure measurements over time. If the signal processor receives signals from the touch sensor that indicate the collar is on the weight bar, it may transfer physical activity data gleaned from the motion sensors to a data processor in a multimedia device such as a smart phone or mobile tablet (step 603). The data transfer may be made wirelessly via Bluetooth©, low energy Bluetooth© or wireless internet protocols. The device may be equipped with a data processor running an installed software application. The data processor may solve at least one algorithm to determine if a lift has occurred by analyzing the physical activity data (step 604). If the data processor determines that a lift of the weight bar has occurred, it may utilize filters to determine performance metrics (step 605). The filters may comprise, for example, a Kalman filter. If a lift has occurred, the performance metrics may be saved by the software application or transmitted to an external storage device. The multimedia device may then display the performance metrics to a user (step 606).

The amount and type of performance metrics that are derived and displayed may change depending on the type of lift being undertaken. Prior to lifting the weight bar with the collar mounted thereto, the athlete may select, via the software application, the type of lift they are about to undertake. The software application may derive and display performance metrics that are specific to the type of lift performed. For example, the user may select from a list of lifting styles such as the “clean and jerk” or a “squat”.

In the case of a “clean and jerk” lift, the software application may process data from the motion sensors to calculate the athlete's Hip Drive Ratio, which is the measure of the force applied by the user during the “clean” segment of the lift, as a weight bar is lifted from the floor to a position across the athlete's deltoids and clavicles. The software application may further calculate and report said athlete's Drop Time, which is the time it takes to transition from the hip drive phase to the catch phase, as a weight bar is lifted from the ground while the athlete transitions to a vertical squatting position.

In the case of a “squat” lift, the software application may process data from the motion sensors to calculate the athlete's Squat Force, which is the measure of the force that the athlete applies to the bar when while in the bottom of the squat (the position at which the athlete's hips are closest to the floor). The software application may further calculate and report said athlete's Up/Down Ratio, which is the ratio of the time it takes the athlete to transition from the top of the squat (legs extended, hips above the knees), to the bottom (legs bent, hips toward the ground) and the time it takes the athlete to transition from the bottom of the squat to the top.

In an alternative embodiment depicted in FIG. 7, the system for measuring and reporting weight-training performance metrics may comprise a vision system for recording and capturing images and video of a user utilizing collar 101 during physical activity. Under this embodiment the system may further comprise a camera rig 160 proximate to or containing at least two cameras 170 mounted to the same or different retaining fixtures a known distance apart from each other. The cameras may be mounted a fixed distance apart from each other in order to better triangulate the position of the collar 101 in a composite image. A plurality of more than two cameras may be mounted an equal distance apart from each other. Each camera may comprise a cut-off filter for blocking light of a particular wavelength, such as infrared light. The system of the second embodiment may further comprise a light fixture 115, such as an LED, that may be mounted or affixed to the collar or at either end of the weight bar. The light may be configured to emit at a wavelength visible to the at least two cameras.

The cameras may be utilized to take images of a user lifting, rolling, tossing, or otherwise manipulating a weight bar with the collar 101 attached. The cameras may be configured to transmit images or video of the bar, the collar, and the light source to an image processor 180. The image processor may be located on a local computer comprised within camera rig 160 or in a server accessible over the internet. The image processor may be configured to determine position data from the location of recognizable features in the received images. The image processor 180 may communicate over a network 130 with a multimedia device 140 having a data processor 150 and may be configured to transmit said position data to the data processor.

An athlete, trainer, or other use may utilize the system in accordance with the second embodiment to record performance metrics during weight training exercises. Prior to utilizing the system, it may require calibration. The calibration process may ensure that the image processor incorporates accurate coordinates of the cameras and the weight bar in three-dimensional space when determining position data. Calibration of the system in accordance with the second embodiment of the present invention may be performed via the following method:

Step 1: Attach a light source emitting light of a particular wavelength and a collar in accordance with the present invention to an end of a weight bar. This step may be performed by the user. Alternatively, the light may be pre-attached to the collar or to the weight bar prior to exercise activity.

Step 2: Position two cameras a known distance apart. This step may be performed by the user. Alternatively, the cameras can be mounted to a rigid or adjustable fixture that maintains an equal separation distance between the cameras at all times.

Step 3: Roll the weight bar across the floor in view of both cameras. This step may be performed by the user.

Step 4: Take images of the weight bar and the light source as it rolls across the floor using both cameras.

Step 5: Utilize said image processor to establish the camera system orientation based on detected light originating from the light source.

Step 6: Lift the weight bar to a known height. This step may be performed by the user. The known height can encompass a predetermined distance from the ground or may be relative to a user-defined characteristic, such as “shoulder height”.

Step 7: Take images of the weight bar and the light source at said known height using both cameras.

Step 8: Utilize said image processor to establish the distance of the light, and therefore the weight bar, from the ground and from each camera.

An athlete, trainer, or other user may utilize the system in accordance with the alternative embodiment of present invention to record performance metrics during weight training exercises and display them via a multimedia device. The system may operate in the following manner, the process of which is depicted in FIG. 8. Once the system has been calibrated, the user may raise and lower the weight bar in accordance with a weight training exercise. As the weight bar is translated through the air by the user, each of the at least two cameras may take images of the light mounted to the weight bar (step 801). The cameras may transfer the images to an image processor (step 802), which may utilize image recognition software to recognize the location of the light within an image and determine position data of the light and weight bar in relation to a known position (step 803). The image processor may save the position data and then transfer the position data to a multimedia device having a data processor (step 804). The device may be a smart phone, mobile tablet, or personal computer. The transfer may be made wirelessly via Bluetooth© technology, a wireless internet connection, or via a wired connection on a local area network.

Next the data processor may determine the movement path of the weight bar by tracking the change in position data over time (step 805). The data processor may subsequently determine performance metrics related to the activity performed by the user (step 806). For example the data processor may be configured to calculate the acceleration of the bar during a lift by using the position data to determine its change in velocity over time. Other weightlifting performance metrics may be calculated by the data processor using algorithms that correspond to a particular type of lift in the same manner as in the first embodiment. A data processor may be configured to run a software application or App, wherein the software application is configured to calculate and display weightlifting performance metrics using the position data output by the image processor. Performance metrics and force graphs may be output to the display screen of said multimedia device (step 807). Once performance metrics and force graphs have been displayed by the device, they may be uploaded by the device to an internet database via a Bluetooth© technology or a wireless internet connection. Users may track their progress by referring to the database via the user interface of the software application or via a web browser.

The derived performance metrics may be displayed to a user on the display screen 907 of a multimedia device, as depicted in FIG. 9. Performance metrics may also be uploaded to the internet, where they may be saved to a database and accessed via a web browser. Information displayed by the device may include the type of lift being conducted, the total weight being lifted, graphs 108 displaying for example the force exerted by the user during the time of the lift, the status of the software application, and the derived performance metrics 109, among other information. The software application may calculate and provide comparisons between the current lift and archival data of previous lifts. The data processor or the software application run by the data processor may analyze performance metrics and output a grade, score, suggestion, evaluation, and/or other means of assessment to provide feedback to the user. Said grade, score, suggestion, evaluation and/or other means of assessment may be uploaded to a database and/or, displayed by the device.

It is contemplated that various combinations and/or sub-combinations of the specific features and aspects of the above embodiments may be made and still fall within the scope of the invention. Accordingly, it should be understood that various features and aspects of the disclosed embodiments may be combined with or substituted for one another in order to form varying modes of the disclosed invention. Further it is intended that the scope of the present invention herein disclosed by way of examples should not be limited by the particular disclosed embodiments described above.

Claims

1. A system for measuring performance metrics, the system comprising:

a collar configured to attach to a weight bar;

at least one motion sensor mounted to said collar;

at least one touch sensor mounted to said collar;

a signal processor configured to receive signals from said motion-sensor and derive physical activity data using said signals; and

a multimedia device, wherein the multimedia device comprises a data processor configured to receive physical activity data from said signal processor and determine performance metrics using said physical activity data.

2. The system of claim 1, wherein said physical activity data comprises at least one of:

the acceleration of a weight bar;

the orientation of a weight bar; and

the distance of a weight bar from the ground.

3. The system of claim 1, wherein said performance metrics comprise at least one of:

force exerted by a user lifting a weight bar; and

lift form while lifting a weight bar.

4. The system of claim 1, wherein said at least one motion-sensor comprises an accelerometer, and wherein the signal processor is configured to measure the acceleration of a weight bar using the signals from said accelerometer.

5. The system of claim 1, wherein said at least one motion-sensor comprises a gyroscope, and wherein the signal processor is configured to measure the orientation of a weight bar using the signals from said gyroscope.

6. The system of claim 1, wherein said at least one motion sensor further comprises a pressure sensor.

7. The system of claim 6, wherein the pressure sensor comprises a barometric altimeter, and wherein the signal processor is configured to measure the distance of a weight bar from the ground using the signals from said barometric altimeter.

8. The system of claim 1, wherein said at least one touch sensor comprises a capacitive touch sensor.

9. The system of claim 8, wherein said at least one capacitive touch sensor is configured to sense contact between the collar and a weight bar.

10. The system of claim 1, wherein the multimedia device comprises a display.

11. The system of claim 10, wherein the multimedia device is a Smartphone, a computer, or a mobile tablet.

12. The system of claim 11, wherein the multimedia device is configured to store said performance metrics or transmit them to a user.

13. A system for measuring performance metrics, the system comprising:

a collar configured to attach to a weight bar;

at least one motion sensor mounted to said collar;

a touch sensor mounted to said collar;

at least one light mounted to said collar;

a camera rig comprising at least two cameras configured to view said collar;

a signal processor configured to receive signals from said at least one motion-sensor and derive physical activity data using said signals;

an image processor configured to receive images of said at least one light from said at least two cameras and derive position data using said images;

a multimedia device, wherein the multimedia device comprises a data processor configured to receive physical activity data from said signal processor and position data from said image processor and to determine performance metrics using said physical activity data and said position data.

14. The system of claim 13, wherein the light is an infrared light.

15. The system of claim 14, wherein the at least two cameras are configured to view infrared light.

16. The system of claim 13, wherein the at least two cameras are spaced an equal distance apart from each other.

17. The system of claim 13, wherein said performance metrics comprise the movement path of a weight bar.