LoEar

Author:

Wang Lei1,Li Wei2,Sun Ke3,Zhang Fusang4,Gu Tao5,Xu Chenren6,Zhang Daqing7

Affiliation:

1. Key Laboratory of High Confidence Software Technologies (Ministry of Education), School of Computer Science, Peking University, China

2. School of Computer Science, Peking University, China

3. University of California San Diego, USA

4. Institute of Software, Chinese Academy of Sciences and University of Chinese Academy of Science, China

5. Macquarie University, Australia

6. School of Computer Science, School of Electronics Engineering and Computer Science, Peking University, Beijing, China

7. Key Laboratory of High Confidence Software Technologies (Ministry of Education), School of Computer Science, School of Electronics Engineering and Computer Science, Peking University, China and Telecom SudParis, France

Abstract

Acoustic sensing has been explored in numerous applications leveraging the wide deployment of acoustic-enabled devices. However, most of the existing acoustic sensing systems work in a very short range only due to fast attenuation of ultrasonic signals, hindering their real-world deployment. In this paper, we present a novel acoustic sensing system using only a single microphone and speaker, named LoEar, to detect vital signs (respiration and heartbeat) with a significantly increased sensing range. We first develop a model, namely Carrierforming, to enhance the signal-to-noise ratio (SNR) via coherent superposition across multiple subcarriers on the target path. We then propose a novel technique called Continuous-MUSIC (Continuous-MUltiple SIgnal Classification) to detect a dynamic reflections, containing subtle motion, and further identify the target user based on the frequency distribution to enable Carrierforming. Finally, we adopt an adaptive Infinite Impulse Response (IIR) comb notch filter to recover the heartbeat pattern from the Channel Frequency Response (CFR) measurements which are dominated by respiration and further develop a peak-based scheme to estimate respiration rate and heart rate. We conduct extensive experiments to evaluate our system, and results show that our system outperforms the state-of-the-art using commercial devices, i.e., the range of respiration sensing is increased from 2 m to 7 m, and the range of heartbeat sensing is increased from 1.2 m to 6.5 m.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction

Reference48 articles.

1. Chao Cai , Zhe Chen , Henglin Pu , Liyuan Ye , Menglan Hu , and Jun Luo . Acute : Acoustic thermometer empowered by a single smartphone . In Proceedings of ACM SenSys , 2020 . Chao Cai, Zhe Chen, Henglin Pu, Liyuan Ye, Menglan Hu, and Jun Luo. Acute: Acoustic thermometer empowered by a single smartphone. In Proceedings of ACM SenSys, 2020.

2. Contactless Sleep Apnea Detection on Smartphones

3. Contactless Infant Monitoring using White Noise

4. C-FMCW Based Contactless Respiration Detection Using Acoustic Signal

5. Xingzhe Song , Boyuan Yang , Ge Yang , Ruirong Chen , Erick Forno , Wei Chen , and Wei Gao . Spirosonic : monitoring human lung function via acoustic sensing on commodity smartphones . In Proceedings of ACM MobiCom , 2020 . Xingzhe Song, Boyuan Yang, Ge Yang, Ruirong Chen, Erick Forno, Wei Chen, and Wei Gao. Spirosonic: monitoring human lung function via acoustic sensing on commodity smartphones. In Proceedings of ACM MobiCom, 2020.

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