SBIR Phase I: Personalized Wearable Device that Learns, Adapts to Users, and Provides Enhanced Metrics and Assessments

Rithmio, Inc., Chicago IL

Investigators

Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project lies in addressing a long-standing roadblock to widespread consumer adoption of wearable tech devices for Smart Health use-cases. The primary barrier is that the current devices are woefully inaccurate when it comes to converting body-worn sensor data into activity/gesture data. A second barrier is that without reliable gesture data, there is no mechanism for providing meaningful feedback to users. To address these barriers, we propose an innovative approach to gesture/activity recognition from body-worn sensor data. The software can rapidly learn and adapt to user's motion idiosyncrasies, in a computationally lightweight but powerful system. The commercialization plan is to license the software to equipment manufacturers for them to embed it in their motion sensing products. Products and use-cases that have been impossible to analyze previously can now have tracking enabled, thus driving value for the end-user and expanding economic opportunities for the manufacturers. The proposed software will empower individuals to manage their fitness and rehabilitation regimens; sports trainers and physicians will have unprecedented tools for monitoring outpatient treatment; and the field will have troves of behavior data to mine for research implications and additional societal outcomes. The proposed project will lead to software that can be integrated with wearable tech devices, either embedded into silicon or as a stand-alone mobile app. The software will be capable of 1) accurately recognizing multiple activities/gestures despite individual variances and other noise factors, 2) providing reliable and valid attributes (metrics) of those activities, specifically range of motion, path efficiency, and power, and 3) providing meaningful data for user feedback and research purposes. Despite the plethora of related devices and technologies now available, current technology is lacking as to the number of gestures identified, accuracy of identification, support for on-device learning, and the ability to provide valid and reliable gesture metrics. The proposed technology will address current limitations in and market demand for functionality, accuracy, and robustness. The overall technical question is whether the software can be developed to adequately compensate for sensor drift and for other types of uncertainties in motion data, at the level of complexity posed by increasingly sophisticated physical behaviors and output (metric) requirements. The anticipated technical results include novel algorithms, fundamental performance bounds, software prototypes, and testing and validation studies. Phase I demonstrations, if successful, will lead to full-scale software development of these and additional functions.