Blast Motion Inc.

Bhaskar Bose

Michael Bentley

Ryan Kaps

Michael D Bentley

Juergen Haas

Piyush Gupta

Brian Estrem

Patrick Cherveny

James R Thornbrue

Joseph Scarbo

Joshua Martin

Sheehan Alam

Michael Gillen

Mazen Abdel-Rahman

Joe Scarbo

Scott Lohr

Michael Fitzpatrick

Hyoung Sang Park

Michael Gillian

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 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.

Multi-source event correlation system

Multi-sensor event correlation system

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.

Swing analysis method using a sweet spot trajectory

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.

Multi-sensor event system

Growing community of inventors

Mesa, AZ, United States of America

Ryan Kaps