The locomotor-respiratory coupling (LRC) ratio of a person doing exercise is an important parameter to reflect the exercise safety and effectiveness. Existing approaches that can measure LRC either rely on specialized and costly devices or use heavy sensors, bringing much inconvenience to people during exercise. To overcome these limitations, we propose ER-Rhythm using low-cost and lightweight RFID tags attached on the human body to simultaneously extract and couple the exercise and respiration rhythm for LRC estimation. ER-Rhythm captures exercise locomotion rhythm from the signals of the tags on limbs. However, extracting respiration rhythm from the signals of the tags on the chest during exercise is a challenging task because the minute respiration movement can be overwhelmed by the large torso movement. To address this challenge, we first leverage the unique characteristic of human respiratory mechanism to measure the chest movement while breathing, and then perform dedicated signal fusion of multiple tags interrogated by a pair of antennas to remove the torso movement effect. In addition, we take advantage of the multi-path effect of RF signals to reduce the number of needed antennas for respiration pattern extraction to save the system cost. To couple the exercise and respiration rhythm, we adopt a correlation-based approach to facilitate LRC estimation. The experimental results show that LRC can be estimated accurately up to 92% - 95% of the time.
|Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
|Published - Dec 2019
- Exercise and respiration rhythm
ASJC Scopus subject areas
- Computer Networks and Communications
- Hardware and Architecture
- Human-Computer Interaction