Arizona State University

Dignity Health

Other

Mozest Goldberg

Troy Lee McDaniel

Stephen L Macknik

Susana Martinez-Conde

Richard Besserman

Richard E Dale

Sethuraman Panchanathan

Hemanth Kumar Demakethepalli Venkateswara

Vishnu Prateek Kakaraparthi

Susana Martinez-conde

A computer-implemented system and associated methods are disclosed including a device for personalized activity monitoring using the hands. The device is worn about a wrist and captures images along the wrist including movement of the hands to monitor predetermined hand movements relative to an object of interest.

Systems and methods including a device for personalized activity monitoring involving the hands

A system includes a wearable device including a camera. The system further includes at least one processor that can identify objects from video data generated by the camera and monitor how an individual wearing the wearable device manipulates the objects according to predetermined micro-activities of interest to infer an action by the individual.

Hand-directed system for identifying activities

Systems and methods for detecting deceptive intent of a subject include observing eye movements of the subject and correlating the observed movements to known baseline neurophysiological indicators of deception. A detection system may record eye movement data from the subject, compare the eye movement data to a data model comprising threshold eye movement data samples, and from the comparison make a determination whether or not the subject is lying. The detection system may create an alert if deception is detected. The eye movements detected include saccadic and intersaccadic parameters such as intersaccadic drift velocity. Measurements may be collected in situ with a field testing device, such as a non-invasive, non-contact device attached to the subject's computing device and configured to non-obtrusively record the eye movement data.

Systems and methods for non-intrusive deception detection

Systems and methods for detecting onset, presence, and progression of particular states, including intoxication, include observing eye movements of a subject and correlating the observed movements to known baseline neurophysiological indicators of intoxication. A detection system may record eye movement data from a user, compare the eye movement data to a data model comprising threshold eye movement data samples, and from the comparison make a determination whether or not intoxication or impairment is present. The detection system may alert the user to take corrective action if onset or presence of a dangerous condition is detected. The eye movements detected include saccadic and intersaccadic parameters such as intersaccadic drift velocity. Measurements may be collected in situ with a field testing device. An interactive application may be provided on a user device to provoke the desired eye movements during recording.

Systems and methods for non-intrusive drug impairment detection

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Chandler, AZ, United States of America

Troy Lee McDaniel