Archinoetics LLC

J Hunter Downs

Fahrettin Olcay Cirit

Richard Moffitt Cowan

Nathan Ronald Kowahl

William James Robinson

Traci H Downs

J Patrick Stautzenberger

Erin Nishimura

Jason Akagi

Brendan F P O'Donnell

Evan D Rapoport

A device worn by a human performing a method using accelerometer data to classify human activities is disclosed. The method uses memory and computation efficiently. A stream of samples is divided into a sequence of sampling periods; each sample has an acceleration value for each axis (e.g., a 3-D accelerometer). Standard deviation of the values for each axis during each sampling period are calculated. A running sum of each axis' values can be maintained sample-by-sample. Each value is sorted into a bin of a histogram, by quantifying a deviance from a respective mean in standard deviations. A standard deviation produced from samples of a previous period can be used. The histogram is compared with histograms associated with particular activities and a classification output can be produced. Classification outputs from multiple sampling periods can be used for voting. A threshold amount of activity can be required to begin activity classification.

Efficient activity classification from motion inputs

Systems, devices and methods for monitoring and evaluating cognitive effectiveness are provided. In one exemplary embodiment, a system for monitoring cognitive effectiveness can include a central network communicatively coupled with a local computing device, which can in turn be communicatively coupled with a portable monitoring device. The portable monitoring device can be located in proximity with a subject and configured to collect data from the subject usable in determining a cognitive effectiveness level. Adaptive methods for determining cognitive performance are also provided.

Systems and methods for monitoring and evaluating individual performance

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Honolulu, HI, United States of America

J Hunter Downs