Abstract: A method of calculating the stress on a joint by determining when a joint angle is outside the normal range of motion for the joint. Applications include analysis of elbow joint stress for baseball pitchers, for example to mitigate the risk of injury to the ulnar collateral ligament (UCL). During a movement such as a baseball pitch, sensor data is collected to track the position, orientation, or motion of body segments; joint angles are derived from this sensor data. Joint angles are then compared to a range of motion, which may be measured for each individual. Joint stress contributions from movements that exceed the range of motion may be combined over time to calculate cumulative joint fatigue. This joint fatigue measure may for example be used for pitchers to quantify recovery periods (days off) and future workloads (such as maximum pitch counts).

Abstract: A method that estimates the 3D trajectory of a projectile, such as a golf ball or baseball, by analyzing a sequence of images from a single-lens, 2D camera. Image analysis may be used to locate the projectile in the camera images, using motion detection and filters for the projectile's expected size and shape. A physics model may be used to calculate the projectile's trajectory as a function of its initial position and velocity (for example, just after impact with a golf club or bat). A camera projection transform may map this trajectory into predicted pixel locations, which may be compared to the observed projectile locations in the camera images. The projectile's trajectory may be estimated by finding initial conditions that minimize differences between observed and predicted pixel locations, using a nonlinear least squares solver for example. The method may be extended to multiple cameras.

Abstract: An aiming system that provides feedback on how closely the aim of an object is aligned with a direction to a target. An inertial sensor on the object provides data on the object's position and orientation; this data is combined with target direction information to determine how to correct the aim. An illustrative application is a golf club aiming system that measures whether the clubface normal is aligned horizontally with the direction to the hole. The system sends feedback signals to the user to help the user adjust the aim. These signals may include for example audible tones or haptic vibrations that vary in frequency and amplitude to instruct the user to adjust the aim. For example, haptic signals may be sent to a smart watch worn by the user; the user may therefore obtain aiming feedback without having to look at a screen.

Abstract: Method for coupling a sensor to a piece of equipment, such as a golf club, baseball bat, or tennis racket, that ensures that the sensor is in a known position and orientation relative to the equipment. Compensates and calibrates for degrees of freedom introduced in manufacturing and installation. The method may include manufacturing a sensor receiver that aligns with equipment in a fixed orientation, and that holds a sensor housing in a fixed orientation relative to the receiver. Remaining uncertainties in sensor position and orientation may be addressed using post-installation calibration. Calibration may include performing specific calibration movements with the equipment and analyzing the sensor data collected during these calibration movements.

Abstract: Motion capture system with a motion capture element that uses two or more sensors to measure a single physical quantity, for example to obtain both wide measurement range and high measurement precision. For example, a system may combine a low-range, high precision accelerometer having a range of ?24 g to +24 g with a high-range accelerometer having a range of ?400 g to +400 g. Data from the multiple sensors is transmitted to a computer that combines the individual sensor estimates into a single estimate for the physical quantity. Various methods may be used to combine individual estimates into a combined estimate, including for example weighting individual estimates by the inverse of the measurement variance of each sensor. Data may be extrapolated beyond the measurement range of a low-range sensor, using polynomial curves for example, and combined with data from a high-range sensor to form a combined estimate.

Abstract: A method for analyzing sensor data from baseball swings (or swings in similar sports) that transforms data into a reference frame defined by the bat orientation and velocity at impact. The trajectory of the sweet spot of the bat is tracked through the swing, and is analyzed to generate metrics describing the swing. A two-lever model of the swing may be used to model the effects of body rotation and wrist rotation. Data may be analyzed to identify relevant events during the swing such as start of downswing, commit (wrist release), on-plane, peak bat speed, and impact. Illustrative swing metrics derived from the sweet spot trajectory, the swing plane reference frame, and the two-lever model include: forward bat speed, on-plane rotation, hinge angle at commit, hinge angle at impact, body rotation ratio, body tilt angle, and swing plane tilt angle.

Abstract: A system that analyzes data from multiple sensors, potentially of different types, that track motions of players, equipment, and projectiles such as balls. Data from different sensors is combined to generate integrated metrics for events and activities. Illustrative sensors may include inertial sensors, cameras, radars, and light gates. As an illustrative example, a video camera may track motion of a pitched baseball, and an inertial sensor may track motion of a bat; the system may use the combined data to analyze the effectiveness of the swing in hitting the pitch. The system may also use sensor data to automatically select or generate tags for an event; tags may represent for example activity types, players, performance levels, or scoring results. The system may analyze social media postings to confirm or augment event tags. Users may filter and analyze saved events based on the assigned tags.

Abstract: Intelligent motion capture element that includes sensor personalities that optimize the sensor for specific movements and/or pieces of equipment and/or clothing and may be retrofitted onto existing equipment or interchanged therebetween and automatically detected for example to switch personalities. May be used for low power applications and accurate data capture for use in healthcare compliance, sporting, gaming, military, virtual reality, industrial, retail loss tracking, security, baby and elderly monitoring and other applications for example obtained from a motion capture element and relayed to a database via a mobile phone. System obtains data from motion capture elements, analyzes data and stores data in database for use in these applications and/or data mining. Enables unique displays associated with the user, such as 3D overlays onto images of the user to visually depict the captured motion data. Enables performance related equipment fitting and purchase.

Abstract: A system that mirrors motion of a physical object by displaying a virtual object moving in a virtual environment. The mirroring display may be used for example for feedback, coaching, or for playing virtual games. Motion of the physical object is measured by motion sensors that may for example include an accelerometer, a gyroscope, and a magnetometer. Sensor data is transmitted to a computer that calculates the position and orientation of the physical object and generates a corresponding position and orientation of the virtual object. The computer may correct or adjust the calculations using sensor data redundancies. The virtual environment may include constraints on the position, orientation, or motion of the virtual object. These constraints may be used to compensate for accumulating errors in position and orientation. The system may for example use proportional error feedback to adjust position and orientation based on sensor redundancies and virtual environment constraints.

Abstract: Motion capture system with a motion capture element that uses two or more sensors to measure a single physical quantity, for example to obtain both wide measurement range and high measurement precision. For example, a system may combine a low-range, high precision accelerometer having a range of ?24 g to +24 g with a high-range accelerometer having a range of ?400 g to +400 g. Data from the multiple sensors is transmitted to a computer that combines the individual sensor estimates into a single estimate for the physical quantity. Various methods may be used to combine individual estimates into a combined estimate, including for example weighting individual estimates by the inverse of the measurement variance of each sensor. Data may be extrapolated beyond the measurement range of a low-range sensor, using polynomial curves for example, and combined with data from a high-range sensor to form a combined estimate.

Abstract: A method for analyzing sensor data from baseball swings (or swings in similar sports) that transforms data into a reference frame defined by the bat orientation and velocity at impact. The trajectory of the sweet spot of the bat is tracked through the swing, and is analyzed to generate metrics describing the swing. A two-lever model of the swing may be used to model the effects of body rotation and wrist rotation. Data may be analyzed to identify relevant events during the swing such as start of downswing, commit (wrist release), on-plane, peak bat speed, and impact. Illustrative swing metrics derived from the sweet spot trajectory, the swing plane reference frame, and the two-lever model include: forward bat speed, on-plane rotation, hinge angle at commit, hinge angle at impact, body rotation ratio, body tilt angle, and swing plane tilt angle.

Abstract: A method of calculating the stress on a joint by determining when a joint angle is outside the normal range of motion for the joint. Applications include analysis of elbow joint stress for baseball pitchers, for example to mitigate the risk of injury to the ulnar collateral ligament (UCL). During a movement such as a baseball pitch, sensor data is collected to track the position, orientation, or motion of body segments; joint angles are derived from this sensor data. Joint angles are then compared to a range of motion, which may be measured for each individual. Joint stress contributions from movements that exceed the range of motion may be combined over time to calculate cumulative joint fatigue. This joint fatigue measure may for example be used for pitchers to quantify recovery periods (days off) and future workloads (such as maximum pitch counts).

Abstract: A system that analyzes data from multiple sensors, potentially of different types, that track motions of players, equipment, and projectiles such as balls. Data from different sensors is combined to generate integrated metrics for events and activities. Illustrative sensors may include inertial sensors, cameras, radars, and light gates. As an illustrative example, a video camera may track motion of a pitched baseball, and an inertial sensor may track motion of a bat; the system may use the combined data to analyze the effectiveness of the swing in hitting the pitch. The system may also use sensor data to automatically select or generate tags for an event; tags may represent for example activity types, players, performance levels, or scoring results. The system may analyze social media postings to confirm or augment event tags. Users may filter and analyze saved events based on the assigned tags.

Abstract: Enables integration of sensor data with other information on servers such as social media sites to detect, confirm and/or publish events. Sensors may measure values such as motion, temperature, humidity, wind, pressure, elevation, light, sound, or heart rate, etc. Sensor data and event tags may be utilized to curate text, images, video, sound and post the results to social networks, for example in a dedicated feed. Event tags generated by the system may represent for example activity types, players, performance levels, or scoring results. The system may analyze social media postings to confirm or augment event tags. Users may filter and analyze saved events based on the assigned tags. The system may create highlight and fail reels filtered by metrics and by tags. Recommendations may be provided to a user based on analysis of sensor data and other information; recommendations may include for example recommended friends, purchases, or activities.

Abstract: Motion capture system with a motion capture element that uses two or more sensors to measure a single physical quantity, for example to obtain both wide measurement range and high measurement precision. For example, a system may combine a low-range, high precision accelerometer having a range of ?24 g to +24 g with a high-range accelerometer having a range of ?400 g to +400 g. Data from the multiple sensors is transmitted to a computer that combines the individual sensor estimates into a single estimate for the physical quantity. Various methods may be used to combine individual estimates into a combined estimate, including for example weighting individual estimates by the inverse of the measurement variance of each sensor. Data may be extrapolated beyond the measurement range of a low-range sensor, using polynomial curves for example, and combined with data from a high-range sensor to form a combined estimate.

Abstract: A sensor event detection and tagging system that analyzes data from multiple sensors to detect an event and to automatically select or generate tags for the event. Sensors may include for example a motion capture sensor and one or more additional sensors that measure values such as temperature, humidity, wind or elevation. Tags and event detection may be performed by a microprocessor associated with or integrated with the sensors, or by a computer that receives data from the microprocessor. Tags may represent for example activity types, players, performance levels, or scoring results. The system may analyze social media postings to confirm or augment event tags. Users may filter and analyze saved events based on the assigned tags. The system may create highlight and fail reels filtered by metrics and by tags.

Abstract: Enables a fitting system for sporting equipment using an application that executes on a mobile phone for example to prompt and accept motion inputs from a given motion capture sensor to measure a user's size, range of motion, speed and then utilizes that same sensor to capture motion data from a piece of equipment, for example to further optimize the fit of, or suggest purchase of a particular piece of sporting equipment. Utilizes correlation or other data mining of motion data for size, range of motion, speed of other users to maximize the fit of a piece of equipment for the user based on other user's performance with particular equipment. For example, this enables a user of a similar size, range of motion and speed to data mine for the best performance equipment, e.g., longest drive, lowest putt scores, highest winning percentage, etc., associated with other users having similar characteristics.

Abstract: A system that analyzes data from sensors and video cameras to generated synchronized event videos and to automatically select or generate tags for an event. Enables creating, transferring, obtaining, and storing concise event videos generally without non-event video. Events stored in the database identifies trends, correlations, models, and patterns in event data. Tags may represent for example activity types, players, performance levels, or scoring results. The system may analyze social media postings to confirm or augment event tags. Users may filter and analyze saved events based on the assigned tags. The system may create highlight and fail reels filtered by metrics and by tags.

Abstract: A method for analyzing sensor data from baseball swings (or swings in similar sports) that transforms data into a reference frame defined by the bat orientation and velocity at impact. The swing plane defined by these two axes provides a natural and robust reference frame for physically relevant measurements of swing characteristics. Illustrative swing metrics derived from swing plane reference frame data include: swing speed, defined as a rotational rate within the swing plane; total swing angle, defined as the angular change within the swing plane; and swing tempo, defined as the percentage of peak swing speed achieved halfway through the swing. Analyzing these metrics from multiple swings across multiple users identifies factors that contribute to peak performance. Metrics may be combined into multidimensional feature vectors that characterize a swing; these feature vectors may be used to group swings into swing styles or to match swings against similar players.

Abstract: A method for analyzing sensor data from baseball swings (or swings in similar sports) that transforms data into a reference frame defined by the bat orientation and velocity at impact. The trajectory of the sweet spot of the bat is tracked through the swing, and is analyzed to generate metrics describing the swing. A two-lever model of the swing may be used to model the effects of body rotation and wrist rotation. Data may be analyzed to identify relevant events during the swing such as start of downswing, commit (wrist release), on-plane, peak bat speed, and impact. Illustrative swing metrics derived from the sweet spot trajectory, the swing plane reference frame, and the two-lever model include: forward bat speed, on-plane rotation, hinge angle at commit, hinge angle at impact, body rotation ratio, body tilt angle, and swing plane tilt angle.