A shared objective of precision health and psychiatry is to precisely measure relevant symptoms and sustain mental health with high precision. Best mental health practices propose the need to identify the early signs of risk factors such as stress. However, mental health research focuses primarily on late-stage symptom assessment via self-report data such as surveys. As a result, clinical practice for mental health mostly consists of reactive treatments. This grant seeks to advance precision mental health by developing an Artificial Intelligence (AI) -enabled platform for continuous and precise measurements of stress. This platform leverages data from a skin-like wearable device that measures cortisol, a stress hormone from sweat, and from sensing techniques based on estimating muscle stiffness changes derived from “fight or flight” stress response, by “repurposing” signals available in billions of existing mobile and computing devices. These data streams will be combined using Machine Learning (ML) algorithms for optimizing data collection, power consumption, and accuracy. Since stress and its effect on mental health deterioration are pervasive, the broader impact of this work could be enormous as well as the basis for new research on precision health in psychiatry.

The overall goal of this project is to develop a multimodal sensing platform leveraging AI/ML algorithms to optimize stress prediction and hardware performance. The first step involves validating the skin-inspired wearable for lab stress measurements, followed by redesigning, and validating as a continuous in-the-wild device. It features a set of physiological sensors for collecting heart rate variability (HRV) and electrodermal activity (EDA) data, signals directly correlated with the autonomous nervous system (ANS) response. Two types of sensors for continuous measurement of cortisol level will be tested for high accuracy and selectivity. However, processing and transmitting sensor data continuously in-the-wild requires significant battery capacity. To address this issue, the second step combines the wearable with passive biomechanical sensors and AI/ML algorithms to optimize for continuous stress detection in-the-wild. Additionally, passive sensors transform computer peripheral data (e.g., mice, trackpads, smartphone screens) into parameters correlated to muscle stiffness linked to the “fight or flight” stress response. For example, the system uses inverse filtering techniques to approximate mass-spring-damper (MSD) models derived from mouse displacements or force models derived from the area under the finger on a trackpad. The system employs several AI/ML algorithms including a) compressive sensing to optimize energy efficiency, b) autoencoder models to correct for artifacts, missing data or sensor failures, c) active learning to discover optimal collection times of stress events and labels, and d) cloud computing powered data collection and processing to make predictions based on the best available data. The intellectual merits of this work include 1) a multimodal stress monitoring wearable that measures cortisol from sweat and other physiological signals, 2) biomechanical sensing algorithms that repurpose movement and touch data into muscle stiffness, and 3) AI/ML algorithms that integrate this data to optimize wearable and smartphone power usage, learn ideal sensing scenarios with high precision, improve privacy, optimize data labeling, and optimize the early prediction of stress.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Stanford University
United States
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