<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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 0.5◦.</p></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 0.5◦.

IoT Sensing Networks for Gait Velocity Measurement

Graduate Institute of Networking and Multimedia

Department of Medical Education and Bioethics

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"IoT Sensing Networks for Gait Velocity Measurement"}

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...

IoT Sensing Networks for Gait Velocity Measurement

Pubblicato online: 04 lug 2019

Pagine: 245 - 259

Ricevuto: 01 lug 2018

Accettato: 21 mar 2019

DOI: https://doi.org/10.2478/amcs-2019-0018

Parole chiave
Internet of things, middleware, compression, data fusion, data reduction

© 2019 Jyun-Jhe Chou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

IoT Sensing Networks for Gait Velocity Measurement

Pubblicato online: 04 lug 2019

Pagine: 245 - 259

Ricevuto: 01 lug 2018

Accettato: 21 mar 2019

DOI: https://doi.org/10.2478/amcs-2019-0018

Parole chiaveInternet of things, middleware, compression, data fusion, data reduction

© 2019 Jyun-Jhe Chou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
Internet of things, middleware, compression, data fusion, data reduction