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

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.

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Publication Details

Journal
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
Published
2024-11-21
DOI
https://doi.org/10.1145/3699752
Citations
12
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
2.99
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article

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

Qikang Liang, Ke Li, François V. Guimbretière, Saif Mahmud et al.
12 citations
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
Context-Aware Activity Recognition Systems
2.99
article

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

Qikang Liang, Ke Li, François V. Guimbretière, Saif Mahmud, V.N. Parikh, Cheng Zhang, Ashwin Ajit, Vipin Gunda, Devansh Agarwal, Ruidong Zhang
article en
12 citations

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.

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous TechnologiesVol. 8(4)
Cornell University (US)
Openalex Percentile: Top 7%
Context-Aware Activity Recognition Systems
2.99
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ActSonic: Recognizing Everyday Activities from Inaudible Acoustic Wave Around the Body — Qikang Liang, Ke Li, et al. · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (2024) | TGRS Research Map | TGRS