Nike, Inc.

Lonza Inc.

John Michael Schmitt

James Mathew Mullin

Aaron K Goodwin

Corey Dow-Hygelund

Steven H Walker

Jordan M Rice

SantoshKumar Balakrishnan

Adam S Carroll

Joshua Rooke-Ley

Tye L Rattenbury

Aaron B Weast

Bradley W Wilkins

Manan Goel

Santoshkumar Balakrishnan

Jeff Hazel

Brandon John Downey

Jeffrey Francis Breit

Aspects relate to calculating energy expenditure values from an apparatus configured to be worn on an appendage of a user. Steps counts may be quantified, such as by detecting arm swings peaks and bounce peaks in motion data. A search range of acceleration frequencies related to an expected activity may be established. Frequencies of acceleration data within a search range may be analyzed to identify one or more peaks, such as a bounce peak and an arm swing peak. Novel systems and methods may determine whether to utilize the arm swing data, bounce data, and/or other data or portions of data to quantify steps. The number of peaks (and types of peaks) may be used to choose a step frequency and step magnitude. At least a portion of the motion data may be classified into an activity category based upon the quantification of steps.

Energy expenditure

A predictive model is described that can predict parameter concentrations in the future based on initial, measured concentrations and historical data. A plurality of multivariate techniques can be used to construct the predictive model capable of forecasting concentrations over multiple and diverse cell lines. The predictive model is also scalable. In one embodiment, a future lactate concentration trajectory is determined and at least one condition within a bioreactor is changed or modified to maintain lactate concentration within desired ranges.

Process and system for propagating cell cultures while preventing lactate accumulation

A process and system for efficiently producing reference data that can be fed into a predictive model for predicting quality attribute concentrations in cell culture processes. A perfusion bioreactor is operated at pseudo-steady-state conditions and one or more attribute influencing parameters are manipulated and changed over time. As the one or more attribute influencing parameters are manipulated, one or more quality attributes are monitored and measured. In one embodiment, multiple quality attributes are monitored and measured in parallel. The quality attribute information is recorded in conjunction with the changes in the attribute influencing parameters. This information is then fed to the predictive model for propagating cell cultures in commercial processes and maintaining the cell cultures within desired preset limits.

Process for creating reference data for predicting concentrations of quality 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 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.

Activities, actions and events during user performance of physical activity may be detected using various algorithms and templates. Templates may include an arrangement of one or more states that may identify particular event types and timing between events. Templates may be specific to a particular type of activity (e.g., types of sports, drills, events, etc.), user, terrain, time of day and the like.

Action detection and activity classification

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.

Growing community of inventors

Bend, OR, United States of America

John Michael Schmitt