Social Distancing Alert with Smartwatches

TL;DR

This paper introduces SoDA, a smartwatch-based system that uses accelerometer and gyroscope data with Vision Transformer models to detect social distancing violations, aiming to reduce COVID-19 transmission.

Contribution

The paper presents a novel activity recognition system using smartwatches and Vision Transformers to effectively identify social distancing violations.

Findings

Achieved 94.7% accuracy in social activity recognition

Reduced negative alerts to 1.8%

Maintained 2.2% missing alerts

Abstract

Social distancing is an efficient public health practice during the COVID-19 pandemic. However, people would violate the social distancing practice unconsciously when they conduct some social activities such as handshaking, hugging, kissing on the face or forehead, etc. In this paper, we present SoDA, a social distancing practice violation alert system based on smartwatches, for preventing COVID-19 virus transmission. SoDA utilizes recordings of accelerometers and gyroscopes to recognize activities that may violate social distancing practice with simple yet effective Vision Transformer models. Extensive experiments over 10 volunteers and 1800+ samples demonstrate that SoDA achieves social activity recognition with the accuracy of 94.7%, 1.8% negative alert, and 2.2% missing alert.