Heterogeneous Sensor Data Fusion for Human Falling Detection
With the continuous improvement of human living standards, population aging has become a global development trend. At present, China has entered an aging society, the health and safety of the elderly have become the focus of social concern. Due to the aging of physiological structure and the decline...
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doaj-ec82adb333c74ff6812d5640bd9d502a2021-03-30T15:24:41ZengIEEEIEEE Access2169-35362021-01-019176101761910.1109/ACCESS.2021.30518999326351Heterogeneous Sensor Data Fusion for Human Falling DetectionDaohua Pan0https://orcid.org/0000-0003-2004-6762Hongwei Liu1https://orcid.org/0000-0002-9215-7173Dongming Qu2School of Computer Science and Technology, Harbin Institute of Technology, Harbin, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, ChinaDepartment of Financial Technology, China Construction Bank, Harbin, ChinaWith the continuous improvement of human living standards, population aging has become a global development trend. At present, China has entered an aging society, the health and safety of the elderly have become the focus of social concern. Due to the aging of physiological structure and the decline of physical function, the probability and frequency of accidental falls in the elderly are very high. Under the above background, the purpose of this study is based on a heterogeneous sensor data fusion algorithm in an intelligent wearable sensor network. This article proposes a heterogeneous sensor data fusion algorithm based on wearable wireless body area network technology, and constructs a high-precision and stable wearable elderly activities of daily living (ADLs) and fall monitoring system. We first select the three-axis acceleration sensor, three-axis magnetic sensor, and three-axis angular velocity sensor to monitor the activities of the elderly. Then, we use Bluetooth to transmit the data collected by heterogeneous sensors to smartphones, and communicate with service centers and users through the mobile phone communication network, Family members interact to form a wireless city network based on wearable technology. Our proposed data fusion approach is based on the Kalman filter algorithm, which can reduce the system noise and improve the stability of the system. The experimental results demonstrate that the fall detection system proposed and implemented in this study can well detect accidental falls in the daily activities of the elderly, the sensitivity and specificity of the fall detection system are 98.7% and 98.5% respectively. The study in this article has a high research value and practical application significance in protecting the healthy life of the elderly.https://ieeexplore.ieee.org/document/9326351/Wearable sensor networkheterogeneous sensorsattitude measurementdata fusion |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Daohua Pan Hongwei Liu Dongming Qu |
spellingShingle |
Daohua Pan Hongwei Liu Dongming Qu Heterogeneous Sensor Data Fusion for Human Falling Detection IEEE Access Wearable sensor network heterogeneous sensors attitude measurement data fusion |
author_facet |
Daohua Pan Hongwei Liu Dongming Qu |
author_sort |
Daohua Pan |
title |
Heterogeneous Sensor Data Fusion for Human Falling Detection |
title_short |
Heterogeneous Sensor Data Fusion for Human Falling Detection |
title_full |
Heterogeneous Sensor Data Fusion for Human Falling Detection |
title_fullStr |
Heterogeneous Sensor Data Fusion for Human Falling Detection |
title_full_unstemmed |
Heterogeneous Sensor Data Fusion for Human Falling Detection |
title_sort |
heterogeneous sensor data fusion for human falling detection |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
With the continuous improvement of human living standards, population aging has become a global development trend. At present, China has entered an aging society, the health and safety of the elderly have become the focus of social concern. Due to the aging of physiological structure and the decline of physical function, the probability and frequency of accidental falls in the elderly are very high. Under the above background, the purpose of this study is based on a heterogeneous sensor data fusion algorithm in an intelligent wearable sensor network. This article proposes a heterogeneous sensor data fusion algorithm based on wearable wireless body area network technology, and constructs a high-precision and stable wearable elderly activities of daily living (ADLs) and fall monitoring system. We first select the three-axis acceleration sensor, three-axis magnetic sensor, and three-axis angular velocity sensor to monitor the activities of the elderly. Then, we use Bluetooth to transmit the data collected by heterogeneous sensors to smartphones, and communicate with service centers and users through the mobile phone communication network, Family members interact to form a wireless city network based on wearable technology. Our proposed data fusion approach is based on the Kalman filter algorithm, which can reduce the system noise and improve the stability of the system. The experimental results demonstrate that the fall detection system proposed and implemented in this study can well detect accidental falls in the daily activities of the elderly, the sensitivity and specificity of the fall detection system are 98.7% and 98.5% respectively. The study in this article has a high research value and practical application significance in protecting the healthy life of the elderly. |
topic |
Wearable sensor network heterogeneous sensors attitude measurement data fusion |
url |
https://ieeexplore.ieee.org/document/9326351/ |
work_keys_str_mv |
AT daohuapan heterogeneoussensordatafusionforhumanfallingdetection AT hongweiliu heterogeneoussensordatafusionforhumanfallingdetection AT dongmingqu heterogeneoussensordatafusionforhumanfallingdetection |
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