A Plantar Inclinometer Based Approach to Fall Detection in Open Environments

Jianfei Sun; Zumin Wang; Liming Chen; Baofeng Wang; Changqing Ji; Shuai Tao

doi:10.1007/978-3-319-33353-3_1

A Plantar Inclinometer Based Approach to Fall Detection in Open Environments

Jianfei Sun, Zumin Wang, Liming Chen, Baofeng Wang, Changqing Ji, Shuai Tao

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

8 Citations (Scopus)

Abstract

In this paper, we report a threshold-based method of fall detection using plantar inclinometer sensor, which provides us the information of angle variations during walking, and of angle status after a fall. The angle variations and status are collected in three-dimensional space. We analyzed the normal range of angle variations during walking, and selected the thresholds by testing the distribution of plantar angles of falls. In the experiments, thresholds were selected from plantar angles of fall status in four directions: forward, backward, left and right. Using the selected thresholds, we detected falls of five subjects in different situations for five hundred times and obtained the average detection rate of 85.4 %.

Original language	English
Title of host publication	Emerging Trends and Advanced Technologies for Computational Intelligence
Publisher	Springer Cham
Pages	1-13
Volume	647
ISBN (Print)	978-3-319-33351-9
DOIs	https://doi.org/10.1007/978-3-319-33353-3_1
Publication status	Published (in print/issue) - 7 Jun 2016

Publication series

Name	Emerging Trends and Advanced Technologies for Computational Intelligence
Volume	647
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-33353-3_1

http://link.springer.com/10.1007/978-3-319-33353-3_1

Cite this

Sun, J., Wang, Z., Chen, L., Wang, B., Ji, C., & Tao, S. (2016). A Plantar Inclinometer Based Approach to Fall Detection in Open Environments. In Emerging Trends and Advanced Technologies for Computational Intelligence (Vol. 647, pp. 1-13). Article Chapter 1 (Emerging Trends and Advanced Technologies for Computational Intelligence; Vol. 647). Springer Cham. https://doi.org/10.1007/978-3-319-33353-3_1

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abstract = "In this paper, we report a threshold-based method of fall detection using plantar inclinometer sensor, which provides us the information of angle variations during walking, and of angle status after a fall. The angle variations and status are collected in three-dimensional space. We analyzed the normal range of angle variations during walking, and selected the thresholds by testing the distribution of plantar angles of falls. In the experiments, thresholds were selected from plantar angles of fall status in four directions: forward, backward, left and right. Using the selected thresholds, we detected falls of five subjects in different situations for five hundred times and obtained the average detection rate of 85.4 %.",

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Sun, J, Wang, Z, Chen, L, Wang, B, Ji, C & Tao, S 2016, A Plantar Inclinometer Based Approach to Fall Detection in Open Environments. in Emerging Trends and Advanced Technologies for Computational Intelligence. vol. 647, Chapter 1, Emerging Trends and Advanced Technologies for Computational Intelligence, vol. 647, Springer Cham, pp. 1-13. https://doi.org/10.1007/978-3-319-33353-3_1

A Plantar Inclinometer Based Approach to Fall Detection in Open Environments. / Sun, Jianfei; Wang, Zumin; Chen, Liming et al.
Emerging Trends and Advanced Technologies for Computational Intelligence. Vol. 647 Springer Cham, 2016. p. 1-13 Chapter 1 (Emerging Trends and Advanced Technologies for Computational Intelligence; Vol. 647).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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T1 - A Plantar Inclinometer Based Approach to Fall Detection in Open Environments

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AU - Ji, Changqing

AU - Tao, Shuai

PY - 2016/6/7

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AB - In this paper, we report a threshold-based method of fall detection using plantar inclinometer sensor, which provides us the information of angle variations during walking, and of angle status after a fall. The angle variations and status are collected in three-dimensional space. We analyzed the normal range of angle variations during walking, and selected the thresholds by testing the distribution of plantar angles of falls. In the experiments, thresholds were selected from plantar angles of fall status in four directions: forward, backward, left and right. Using the selected thresholds, we detected falls of five subjects in different situations for five hundred times and obtained the average detection rate of 85.4 %.

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