Nike, Inc.

Nike Innovate C.v.

Patientech LLC

Santoshkumar Balakrishnan

Krishna Sandeep Bhimavarapu

Christopher Andon

Bradley W Wilkins

Manan Goel

Robert M Bruce

Richard Anthony Derenne

John Michael Schmitt

Jeff Hazel

Jordan M Rice

Sam Amis

Corey Dow-Hygelund

Paul James Francis

Justin Fraga

Jenny Campbell

Joshua Elmer Mix

Steve Holmes

Joseph E Mroz

Brad Wilkins

Xianyu Shea

Aaron B Weast

Paul Francis

Sean Daugherty

Edward C Iii Kline

Systems and methods are provided for calculating athletic activity parameters. Multiple housings are position at different locations on a user's body. The housings are configured to be removably engaged with an electronic module that includes a sensor and a processor configured to calculate athletic activity parameters. Each housing is connected to or includes an identification memory that stores information identifying a location of the housing. The electronic module uses the location information to select an algorithm to use when calculating the athletic activity parameters.

Apparel and location information system

Systems and methods for monitoring athletic performances include determining 'flight time,' e.g., the amount of time both feet are off the ground, and optionally “flight time” resulting from different types of activities, such as jogging, running, sprinting, jumping, etc. “Flight time” may help a player or coach better understand the effort the athlete is putting out, compare efforts of two or more players, gauge the athlete's performance change over time, and/or identify conditioning needs and/or areas for improvement. Such systems and methods also may generate and display various athletic performance metrics, such as: instantaneous flight time; average flight time; cumulative flight time during an athletic performance or other time period; instantaneous jump height; average jump height; cumulative jump height during an athletic performance or other time period; and comparisons of any flight time and/or jump height metric(s) of one player against another player and/or against himself/herself, etc.

Flight time

Systems and methods configured to process motion data associated with a user. The systems and methods are configured to receive motion data from a sensor, calculate motion attributes from the data, and classify the motion data using one or more mathematical models. Attributes may be calculated without classifying the motion data into an activity type (such as walking, running, swimming, or any specific or general activity). Attributes may be compared to activity models comprising motion data from several individuals, which may not include the user. Motion data within the models and attributes of the user may be independent of any activity type. Attributes may be compared to select an energy expenditure model from one or more energy expenditure models, which may be selected as a best-match to the one or more motion attributes. An energy expenditure associated with the motion of the user may then be calculated.

Calculating pace and energy expenditure from athletic movement attributes

Systems and methods configured to process motion data associated with a user. The systems and methods are configured to receive motion data from a sensor, calculate motion attributes from the data, and classify the motion data using one or more mathematical models. Attributes may be calculated without classifying the motion data into an activity. Attributes may be compared to mathematical models. Motion data within the models and attributes of the user may be independent of any activity type. Attributes may be compared to select an energy expenditure model from one or more energy expenditure models, or an activity classification model, from the one or more activity classification models. An energy expenditure, or a classification of received data as a linear travel motion, may then be calculated.

Systems and methods configured to process motion data associated with a user. The systems and methods are configured to receive motion data from a sensor, calculate motion attributes from the data, and classify the motion data using one or more mathematical models. Further, the systems and methods are configured to identify and quantify, using the one or more mathematical models, the motion data as linear travel motion associated with the user running or walking.

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Portland, OR, United States of America

Santoshkumar Balakrishnan