Abstract:
An equipment fitting system that measures swings by a user of different pieces of equipment with inertial sensors, and analyzes sensor data to recommend which piece of equipment is optimal for the user from among those tested. Illustrative applications include fitting of baseball bats and golf clubs. Swing metrics calculated from sensor data may include an acceleration metric, a speed metric, and a momentum metric; these metrics may be combined into a metrics score for each piece of equipment. Other factors may be included in an overall score, such as the user's subjective score for each piece of equipment, and ratings from experts or other consumers. Users may assign the relative importance for the different factors to calculate an overall equipment score.
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:
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 measures a swing of a bat with one or more sensors and analyzes sensor data to create swing quality metrics. Metrics may include for example rotational acceleration, on-plane efficiency, and body-bat connection. Rotational acceleration measures the centripetal acceleration of the bat along the bat's longitudinal axis at a point early in the rotational part of the swing; it is an indicator of the swing's power. On-plane efficiency measures how much of the bat's angular velocity occurs around the swing plane, the plane spanned by the bat and the bat's sweet spot velocity at impact. Body-bat connection measures the angle between the bat and the body tilt axis, which is estimated from the trajectory of the hand position on the bat through the swing; an ideal bat-body connection is near 90 degrees. These three swing quality metrics provide a simple and useful characterization of the swing mechanics.
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:
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 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:
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:
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:
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.