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 | |
| 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)
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SDG 3 Good Health and Well-being
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