ActSonic: Recognizing Everyday Activities from Inaudible Acoustic Wave Around the Body

TL;DR

ActSonic is a low-power, eyeglasses-integrated acoustic sensing system that recognizes 27 daily activities using inaudible ultrasonic waves and deep learning, achieving high accuracy without user-specific training.

Contribution

It introduces a novel ultrasonic sensing system embedded in eyeglasses for activity recognition, utilizing self-supervised deep learning and requiring no user-specific training data.

Findings

Achieved an average F1-score of 86.6% in real-world scenarios.

Successfully recognized 27 different activities.

Operates with low power and minimal hardware.

Abstract

We present ActSonic, an intelligent, low-power active acoustic sensing system integrated into eyeglasses that can recognize 27 different everyday activities (e.g., eating, drinking, toothbrushing) from inaudible acoustic waves around the body. It requires only a pair of miniature speakers and microphones mounted on each hinge of the eyeglasses to emit ultrasonic waves, creating an acoustic aura around the body. The acoustic signals are reflected based on the position and motion of various body parts, captured by the microphones, and analyzed by a customized self-supervised deep learning framework to infer the performed activities on a remote device such as a mobile phone or cloud server. ActSonic was evaluated in user studies with 19 participants across 19 households to track its efficacy in everyday activity recognition. Without requiring any training data from new users (leave-one-participant-out evaluation), ActSonic detected 27 activities, achieving an average F1-score of 86.6% in fully unconstrained scenarios and 93.4% in prompted settings at participants' homes.