HearFit: Fitness Monitoring on Smart Speakers via Active Acoustic Sensing

被引:23
作者
Xie, Yadong [1 ]
Li, Fan [1 ]
Wu, Yue [1 ]
Wang, Yu [2 ]
机构
[1] Beijing Inst Technol, Sch Comp Sci, Beijing, Peoples R China
[2] Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA USA
来源
IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (IEEE INFOCOM 2021) | 2021年
基金
中国国家自然科学基金; 北京市自然科学基金;
关键词
EXERCISE;
D O I
10.1109/INFOCOM42981.2021.9488811
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Fitness can help to strengthen muscles, increase resistance to diseases and improve body shape. Nowadays, more and more people tend to exercise at home/office, since they lack time to go to the dedicated gym. However, it is difficult for most of them to get good fitness effect due to the lack of professional guidance. Motivated by this, we propose HearFit, the first non-invasive fitness monitoring system based on commercial smart speakers for home/office environments. To achieve this, we turn smart speakers into active sonars. We design a fitness detection method based on Doppler shift and adopt the short time energy to segment fitness actions. We design a high-accuracy LSTM network to determine the type of fitness. Combined with incremental learning, users can easily add new actions. Finally, we evaluate the local (i.e., intensity and duration) and global (i.e., continuity and smoothness) fitness quality of users to help to improve fitness effect and prevent injury. Through extensive experiments including over 7,000 actions of 10 types of fitness with and without dumbbells from 12 participants, HearFit can detect fitness actions with an average accuracy of 96.13 %, and give accurate statistics in various environments.
引用
收藏
页数:10
相关论文
共 41 条
[21]   Your table can be an input panel: Acoustic-based device-free interaction recognition [J].
Chen, Mingshi ;
Yang, Panlong ;
Xiong, Jie ;
Zhang, Maotian ;
Lee, Youngki ;
Xiang, Chaocan ;
Tian, Chang .
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2019, 3 (01)
[22]   RecoFit: Using a Wearable Sensor to Find, Recognize, and Count Repetitive Exercises [J].
Morris, Dan ;
Saponas, T. Scott ;
Guillory, Andrew ;
Kelner, Ilya .
32ND ANNUAL ACM CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2014), 2014, :3225-3234
[23]  
National Public Radio, 2020, SMART AUD REP
[24]  
Nexercise, 2019, SWORK
[25]  
Ning Xiao, 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, P2024, DOI 10.1109/INFOCOM.2018.8485933
[26]  
Rocca S., 2020, GYMBOOK
[27]   Role of exercise in the prevention of cardiovascular disease: results, mechanisms, and new perspectives [J].
Schuler, Gerhard ;
Adams, Volker ;
Goto, Yoichi .
EUROPEAN HEART JOURNAL, 2013, 34 (24) :1790-+
[28]   MiLift: Efficient Smartwatch-Based Workout Tracking Using Automatic Segmentation [J].
Shen, Chenguang ;
Ho, Bo-Jhang ;
Srivastava, Mani .
IEEE TRANSACTIONS ON MOBILE COMPUTING, 2018, 17 (07) :1609-1622
[29]   Directional loudness and binaural summation for wideband and reverberant sounds [J].
Sivonen, Ville Pekka .
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 2007, 121 (05) :2852-2861
[30]   The Health Benefits of Muscular Fitness for Children and Adolescents: A Systematic Review and Meta-Analysis [J].
Smith, Jordan J. ;
Eather, Narelle ;
Morgan, Philip J. ;
Plotnikoff, Ronald C. ;
Faigenbaum, Avery D. ;
Lubans, David R. .
SPORTS MEDICINE, 2014, 44 (09) :1209-1223