Scalable Deep Learning for Stress and Affect Detection on Resource-Constrained Devices

Abstract

Psychological stress in human beings has been on a meteoric rise over the last few years. Chronic stress can have fatal consequences such as heart disease, cancer, suicide and so on. It is thus imperative to detect stress early on to prevent health risks. In this work, we discuss efficient and accurate stress and affect detection using scalable Deep Learning methods, that can be used to monitor stress real-time on resource-constrained devices such as low-cost wearables. By making inferences on-device, we solve the issues of high latency and lack of privacy which are prevalent in cloud-based computation. Using the concept of Early Stopping - Multiple Instance Learning, we build specialized models for stress and affect detection for 3 popular datasets in the domain, that have very low inference times but high accuracy. We introduce a metric ηcomp to measure the computational savings from the use of these models. On average, our models show an absolute increase of 10% in overall accuracy over the benchmarks, computational savings of 95.39%, and an 18x reduction in inference times on a Raspberry Pi 3 Model B. This allows for efficient and accurate real-time monitoring of stress on low-cost resource-constrained devices.