Dynamical Estimation of Key Cardiac-respiratory Variables by using Commercialized Wearable Sensors

Kai Cao, Lin Ye, Hamzah M. Alqudah, Jan Szymanski, Jing Zhou, and Steven W. Su


VO2, Estimation, Modeling, Inertial sensor, IMU


This paper investigates the estimation of key cardiac-respiratory variables (e.g.,$VO_2$) by using commercialised wearable sensors such as SensorTag and iPhone. The main aim of this study is to use inexpensive and user-friendly wearable sensors rather than expensive and cumbersome equipment (e.g., metabolic analyser). This study also aims to explore the possibility of using only embedded sensors of smart-phone to dynamically estimate oxygen consumption during moderate exercises. The major focus of this research is the modelling of the linear dynamic component. In order to capture the variance of linear dynamic characteristics (e.g., the time constant and steady state gain), we proposed a least square estimation algorithm equipped with automated equilibrium detection function. Finally, the effectiveness of the proposed approaches has been well demonstrated by experimental results.

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