A Comparative Analysis of Smartphone and Standard Tools for Touch Perception Assessment Across Multiple Body Sites

Tactile perception plays an important role in activities of daily living, and it can be impaired in individuals with certain medical conditions. The most common tools used to assess tactile sensation, the Semmes-Weinstein monofilaments and the 128 Hz tuning fork, have poor repeatability and resolution. Long term, we aim to provide a repeatable, high-resolution testing platform that can be used to assess vibrotactile perception through smartphones without the need for an experimenter to be present to conduct the test. We present a smartphone-based vibration perception measurement platform and compare its performance to measurements from standard monofilament and tuning fork tests. We conducted a user study with 36 healthy adults in which we tested each tool on the hand, wrist, and foot, to assess how well our smartphone-based vibration perception thresholds (VPTs) detect known trends obtained from standard tests. The smartphone platform detected statistically significant changes in VPT between the index finger and foot and also between the feet of younger adults and older adults. Our smartphone-based VPT had a moderate correlation to tuning fork-based VPT. Our overarching objective is to develop an accessible smartphone-based platform that can eventually be used to measure disease progression and regression.Comment: Accepted for publication in IEEE Transactions on Haptics 202

Monitoring Mental Health with a Multimodal Sensor System and Low-power Specialized Hardware

This work presents a system that utilizes smartphone and wearable data to understand human behavior and facilitate mental health monitoring. Although mental disorders are exceedingly prevalent, their diagnostic methods are much more antiquated than their physical ailment counterparts. Mental health diagnosticians employ subjective surveys, professional observations, and patient recall to capture a patient’s changing behavior, but these approaches are severely limited. Mobile devices introduce a unique opportunity to quantify and unobtrusively record data on human behavior. Predictive classifiers can interpret the data and yield meaningful behavior classifications and predict mental health status. With the inclusion of specialized hardware, a secure and energy-efficient system can be developed to identify worrying behavior, predict mental health metrics, and encourage users to seek medical help. In this work, machine learning models associated with worrisome mental health behaviors are developed with existing datasets, optimized for performance, and ported to hardware accelerators. Energy metrics for the models are estimated and used as design considerations for a realizable end-to-end system