用智能音箱音频监测居家健身，自动识别动作和用户。
HearFit+: Personalized Fitness Monitoring via Audio Signals on Smart Speakers
- 通过多普勒效应与短时能量分割，从声音中识别健身动作。
- 动作分类准确率96.13%，用户识别率达91%，支持新增动作。
- 适合居家健身者，可实时反馈动作质量提升效果。
健身有助于增强肌肉、提高抗病能力并改善体形。如今，由于时间有限，越来越多的人选择在家或办公室锻炼而非去健身房。然而，缺乏专业指导使得锻炼效果难以保证。为此，我们提出了首个基于智能音箱的个性化健身监测系统HearFit+，探索利用声学感知进行健身监测的可行性。该系统基于多普勒效应设计动作检测方法，并采用短时能量对健身动作进行分段。结合深度学习，HearFit+可同时完成健身动作分类与用户识别，并支持增量学习以轻松添加新动作。我们设计了持续性、强度、流畅性和时长四项评估指标，帮助用户优化训练效果。在12名志愿者参与的实验中，涵盖超过9000次、10类健身动作，系统平均动作分类准确率达96.13%，用户识别准确率为91%。所有参与者均表示HearFit+能有效提升不同环境下的健身效果。
原文摘要 · Abstract (English)
Fitness can help to strengthen muscles, increase resistance to diseases, and improve body shape. Nowadays, a great number of people choose to exercise at home/office rather than at the gym due to lack of time. However, it is difficult for them to get good fitness effects without professional guidance. Motivated by this, we propose the first personalized fitness monitoring system, HearFit+, using smart speakers at home/office. We explore the feasibility of using acoustic sensing to monitor fitness. We design a fitness detection method based on Doppler shift and adopt the short time energy to segment fitness actions. Based on deep learning, HearFit+ can perform fitness classification and user identification at the same time. Combined with incremental learning, users can easily add new actions. We design 4 evaluation metrics (i.e., duration, intensity, continuity, and smoothness) to help users to improve fitness effects. Through extensive experiments including over 9,000 actions of 10 types of fitness from 12 volunteers, HearFit+ can achieve an average accuracy of 96.13% on fitness classification and 91% accuracy for user identification. All volunteers confirm that HearFit+ can help improve the fitness effect in various environments.
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