Blast Motion Inc.

Bentley, Michael

Bose, Bhaskar

Bhaskar Bose

Michael Bentley

Michael D Bentley

Ryan Kaps

Piyush Gupta

Juergen Haas

Patrick Cherveny

James R Thornbrue

Brian Estrem

John Goree

Scott Lohr

Joseph Scarbo

Joshua Martin

Tim Haynes

Sheehan Alam

Jacob Howenstein

Michael Gillen

Michael Fitzpatrick

Joe Scarbo

Mazen Abdel-Rahman

Hyoung Sang Park

Ryan Gullotti

Michael Gillian

A system that analyzes motion of a putter and provides feedback to the golfer as a training aid to guide the golfer towards making better putting strokes. The putter may be equipped with an inertial motion sensor that captures data throughout a stroke; data may be transmitted to a processor, such as a mobile device or a server, for analysis, and feedback signals may be sent to the golfer throughout the stroke based on this analysis. For example, audio tones (generated for example by a mobile phone) may change depending on whether the putting stroke has the desired characteristics. The system may support different operating modes that provide feedback on different putting features. Illustrative modes may for example provide feedback on the putter orientation at address, on the timing of the backstroke, and on the changes in putter face orientation through the stroke.

Putting biofeedback system that analyzes motion data

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.

Equipment fitting system that compares swing metrics

A system that captures and analyzes motion data for equipment and that automatically captures the usage context in which the motion occurs. Context items in the environment may have attached beacons that broadcast their identities; a motion capture element on the equipment receives and analyzes these broadcast messages to determine which context items are nearby. Beacons may be grouped into categories such as 'user', “location”, “coach”, and “equipment”; the motion capture element determines the nearest beacon in each category, and these nearest beacons are used as the context for each motion. For example, in a baseball application players and coaches may wear pendants or similar accessories with personal beacons, and other beacons may be installed into bats, bases, batting cages, bullpens, or other locations. The context for a swing may include the player who made the swing, the bat used, a coach that was present, and where the swing occurred.

Motion capture system that recognizes usage context

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.

Motion mirroring system that incorporates virtual environment constraints

A sensor event detection system including a motion capture element and another sensor. The sensor captures values associated with an orientation, position, velocity and acceleration and recognizes an event within the data to determine event data. Uses other values associated with a temperature, humidity, wind and elevation, i.e., environmental and physiological sensors and correlates the data or event data with the other values to determine a type of event or true event or 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.

Disparate sensor event correlation system

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.

Motion capture data fitting system

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.

Swing quality measurement system

A system that measures a swing of equipment (such as a bat or golf club) with inertial sensors, and analyzes sensor data to create a rotational profile. Swing analysis may use a two-lever model, with a body lever from the center of rotation to the hands, and an equipment lever from the hands to the sweet spot of the equipment. The rotational profile may include graphs of rates of change of the angle of the body lever and of the relative angle between the body lever and the equipment lever, and a graph of the centripetal acceleration of the equipment. These three graphs may provide insight into players' relative performance. The timing and sequencing of swing stages may be analyzed by partitioning the swing into four phases: load, accelerate, peak, and transfer. Swing metrics may be calculated from the centripetal acceleration curve and the equipment/body rotation rate curves.

Swing analysis system that calculates a rotational profile

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.

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Carlsbad, CA, United States of America

Bhaskar Bose