Abstract:
A motion sensor package with an elastomer layer that encases the sensor electronics, including the sensors, a processor, an antenna, and a battery. The elastomer layer may provide shock isolation and water resistance to protect the enclosed electronics. Embodiments may also include an outer housing into which the elastomer encased package is installed. The outer housing may for example comprise two cylindrical sections that screw together to close the outer housing. In one or more embodiments part of the outer housing may be integrated into an item of sports equipment. Embodiments for golf may also include a golf club grip adapter that is inserted into the top of a grip, and which attaches to the outer housing containing the elastomer enclosed sensor package.
Abstract:
Enables detection and tagging of events using sensor data combined with data from servers such as social media sites. Sensors may measure values such as motion, temperature, humidity, wind, pressure, elevation, light, sound, or heart rate. 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.
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:
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 sensor event detection system including a motion capture element with a memory, sensor, microprocessor, first communication interface and another sensor. The sensor captures values associated with an orientation, position, velocity and acceleration of the motion capture element. The first communication interface receives other values associated with a temperature, humidity, wind and elevation, and the other sensor locally captures the other values. The microprocessor collects data that includes sensor values from the sensor, stores the data in the memory, and recognizes an event within the data to determine event data. The microprocessor correlates the data or event data with the other values to determine a false positive event or a type of equipment the motion capture element is coupled with, or a type of activity indicated by the data or event data, and transmits the data or event data associated with the event via the first communication interface.
Abstract:
Enables recognition of events within motion data obtained from portable wireless motion capture elements and video synchronization of the events with video as the events occur or at a later time, based on location and/or time of the event or both. May use integrated camera or external cameras with respect to mobile device to automatically generate generally smaller event videos of the event on the mobile device or server. Also enables analysis or comparison of movement associated with the same user, other user, historical user or group of users. Provides low memory and power utilization and greatly reduces storage for video data that corresponds to events such as a shot, move or swing of a player, a concussion of a player, or other medical related events or events, such as the first steps of a child, or falling events.
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:
An initialization method for an inertial sensor that estimates starting orientation and velocity without requiring the sensor to start at rest or in a well-known location or orientation. Initialization uses patterns of motion encoded as a set of soft constraints that are expected to hold approximately during an initialization period. Penalty metrics are defined to measure the deviation of calculated motion trajectories from the soft constraints. Differential equations of motion for an inertial sensor are solved with the initial conditions as variables; the initial conditions that minimize the penalty metrics are used as estimates for the actual initial conditions of the sensor. Soft constraints and penalty metrics for a specific application are chosen based on the types of motion patterns expected for this application. Illustrative cases include applications with relatively little movement during initialization, and applications with approximately periodic motion during initialization.
Abstract:
Enables recognition of events within motion data including but not limited to motion capture data obtained from portable wireless motion capture elements such as visual markers and sensors, radio frequency identification tags and motion sensors within mobile device computer systems, or calculated based on analyzed movement associated with the same user, other user, historical user or group of users. Provides low power transmission of events. Greatly reduces storage for events such as a shot, move or swing of a player, a concussion of a player, boxer, rider or driver, or a heat stroke, hypothermia, seizure, asthma attack, epileptic attack. Events may be correlated with image(s) as captured from internal/external camera(s) or nanny cam, for example to enable saving video of the event, such as the first steps of a child, violent shaking events, sporting, military or other motion events including concussions, or falling events associated with an elderly person.