TY - GEN
T1 - Hearing Heartbeat from Voice
T2 - 20th ACM Conference on Embedded Networked Sensor Systems, SenSys 2022
AU - Xu, Chenhan
AU - Chen, Tianyu
AU - Li, Huining
AU - Gherardi, Alexander
AU - Weng, Michelle
AU - Li, Zhengxiong
AU - Xu, Wenyao
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2023/1/24
Y1 - 2023/1/24
N2 - Voice user interfaces (VUIs) have been adopted in many IoT and mobile devices in daily life. VUIs provide a good user experience with lower-cost hardware (i.e., microphone) and higher throughput (compared with keyboard and touchscreen). Currently, identity authentication and receiving commands are the two most common interactions through VUIs, leaving physiological information in the voice unexploited. Recognizing this untapped potential, we propose VocalHR to extend VUIs beyond voice commands to heart activity sensing without additional hardware. VocalHR is built upon the voice-heart modulation effect, which is rooted in the cardiac activities' impacts on the behavior of the vocal organ during voice production. VocalHR captures voice features of cardiac activity in multiple voice organs and proposes a deep learning pipeline to transform features into cardiac activities. As this is the first study exploring voice-based heart activity sensing, we conducted extensive experiments on 43 demographically diverse subjects to verify the intrinsic link between voice and heart activities. On average, VocalHR can achieve less than 11.1% normalized sensing error on the heart event timing. Our further evaluation shows VocalHR is robust to different microphone specifications and varying speech rates.
AB - Voice user interfaces (VUIs) have been adopted in many IoT and mobile devices in daily life. VUIs provide a good user experience with lower-cost hardware (i.e., microphone) and higher throughput (compared with keyboard and touchscreen). Currently, identity authentication and receiving commands are the two most common interactions through VUIs, leaving physiological information in the voice unexploited. Recognizing this untapped potential, we propose VocalHR to extend VUIs beyond voice commands to heart activity sensing without additional hardware. VocalHR is built upon the voice-heart modulation effect, which is rooted in the cardiac activities' impacts on the behavior of the vocal organ during voice production. VocalHR captures voice features of cardiac activity in multiple voice organs and proposes a deep learning pipeline to transform features into cardiac activities. As this is the first study exploring voice-based heart activity sensing, we conducted extensive experiments on 43 demographically diverse subjects to verify the intrinsic link between voice and heart activities. On average, VocalHR can achieve less than 11.1% normalized sensing error on the heart event timing. Our further evaluation shows VocalHR is robust to different microphone specifications and varying speech rates.
KW - contactless sensing
KW - healthcare
KW - voice biometrics
KW - voice-user interface
UR - https://www.scopus.com/pages/publications/85147552087
U2 - 10.1145/3560905.3568508
DO - 10.1145/3560905.3568508
M3 - Conference contribution
AN - SCOPUS:85147552087
T3 - SenSys 2022 - Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
SP - 149
EP - 163
BT - SenSys 2022 - Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
PB - Association for Computing Machinery, Inc
Y2 - 6 November 2022 through 9 November 2022
ER -