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Paper details
Number 2 - June 2019
Volume 29 - 2019
IoT sensing networks for gait velocity measurement
Jyun-Jhe Chou, Chi-Sheng Shih, Wei-Dean Wang, Kuo-Chin Huang
Abstract
Gait velocity has been considered the sixth vital sign. It can be used not only to estimate the survival rate of the elderly,
but also to predict the tendency of falling. Unfortunately, gait velocity is usually measured on a specially designed walk
path, which has to be done at clinics or health institutes. Wearable tracking services using an accelerometer or an inertial
measurement unit can measure the velocity for a certain time interval, but not all the time, due to the lack of a sustainable
energy source. To tackle the shortcomings of wearable sensors, this work develops a framework to measure gait velocity
using distributed tracking services deployed indoors. Two major challenges are tackled in this paper. The first is to minimize
the sensing errors caused by thermal noise and overlapping sensing regions. The second is to minimize the data volume to
be stored or transmitted. Given numerous errors caused by remote sensing, the framework takes into account the temporal
and spatial relationship among tracking services to calibrate the services systematically. Consequently, gait velocity can be
measured without wearable sensors and with higher accuracy. The developed method is built on top of WuKong, which is
an intelligent IoT middleware, to enable location and temporal-aware data collection. In this work, we present an iterative
method to reduce the data volume collected by thermal sensors. The evaluation results show that the file size is up to 25%
of that of the JPEG format when the RMSE is limited to 0.5º.
Keywords
Internet of things, middleware, compression, data fusion, data reduction