In this paper, we introduce ActSonic, an intelligent, low-power active acoustic sensing system integrated into eyeglasses. ActSonic is designed to recognize 27 different everyday activities (e.g., eating, drinking, toothbrushing). It only needs a pair of miniature speakers and microphones mounted on each hinge of eyeglasses to emit ultrasonic waves to create an acoustic aura around the body. Based on the position and motion of various body parts, the acoustic signals are reflected with unique patterns captured by the microphone and analyzed by a customized self-supervised deep learning framework to infer the performed activities. ActSonic was deployed in a user study with 19 participants across 19 households to evaluate its efficacy. Without requiring any training data from a new user (leave-one-participant-out evaluation), ActSonic was able to detect 27 activities with an inference resolution of 1 second, achieving an average F1-score of 86.6% in an unconstrained setting and 93.4% in a prompted setting.

翻译：本文介绍了ActSonic——一种集成于眼镜框架的智能低功耗主动声学感知系统。该系统设计用于识别27种日常活动（如进食、饮水、刷牙等），仅需在眼镜铰链处安装一对微型扬声器和麦克风，通过发射超声波在人体周围构建声学场域。基于不同身体部位的位置与运动特性，超声波信号经反射后呈现独特模式，由麦克风采集并通过定制的自监督深度学习框架解析活动类型。我们在19户家庭中开展用户研究，部署ActSonic并评估其效能。在无需新用户训练数据（留一参与者交叉验证）的条件下，ActSonic可在非约束环境下以1秒推理分辨率识别27种活动，平均F1分数达86.6%；在提示性环境下达到93.4%。