Experiments of posture estimation on vehicles using wearable acceleration sensors
Yoji Yamato
TL;DR
The paper addresses driver posture estimation in vehicles using wearable acceleration sensors, noting that vehicle motion contaminates wearable signals. It compares two approaches: (i) subtracting vehicle acceleration using synchronized hitoe and smartphone data, and (ii) threshold-based posture detection from hitoe acceleration data. Field tests indicate the subtraction approach suffers from sensor mismatch, while posture detection is feasible with a threshold of $-0.34G$ corresponding to about $20^{\circ}$ tilt, and a sample cloud-enabled app demonstrates real-time posture visualization and data transmission including heart-rate via $EV$ and $CVI$. This work has practical implications for bus and taxi safety by enabling driver-posture and fatigue monitoring through wearables and cloud analytics.
Abstract
In this paper, we study methods to estimate drivers' posture in vehicles using acceleration data of wearable sensor and conduct a field test. Recently, sensor technologies have been progressed. Solutions of safety management to analyze vital data acquired from wearable sensor and judge work status are proposed. To prevent huge accidents, demands for safety management of bus and taxi are high. However, acceleration of vehicles is added to wearable sensor in vehicles, and there is no guarantee to estimate drivers' posture accurately. Therefore, in this paper, we study methods to estimate driving posture using acceleration data acquired from T-shirt type wearable sensor hitoe, conduct field tests and implement a sample application. Y. Yamato, "Experiments of Posture Estimation on Vehicles Using Wearable Acceleration Sensors," The 3rd IEEE International Conference on Big Data Security on Cloud (BigDataSecurity 2017), pp.14-17, DOI: 10.1109/BigDataSecurity.2017.8, May 2017. "(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."