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Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis
Human falls pose a serious threat to the person’s health, especially for the elderly and disease-impacted people. Early detection of involuntary human gait change can indicate a forthcoming fall. Therefore, human body fall warning can help avoid falls and their caused injuries for the skeleton and j...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347941/ https://www.ncbi.nlm.nih.gov/pubmed/34372477 http://dx.doi.org/10.3390/s21155240 |
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author | Bucinskas, Vytautas Dzedzickis, Andrius Rozene, Juste Subaciute-Zemaitiene, Jurga Satkauskas, Igoris Uvarovas, Valentinas Bobina, Rokas Morkvenaite-Vilkonciene, Inga |
author_facet | Bucinskas, Vytautas Dzedzickis, Andrius Rozene, Juste Subaciute-Zemaitiene, Jurga Satkauskas, Igoris Uvarovas, Valentinas Bobina, Rokas Morkvenaite-Vilkonciene, Inga |
author_sort | Bucinskas, Vytautas |
collection | PubMed |
description | Human falls pose a serious threat to the person’s health, especially for the elderly and disease-impacted people. Early detection of involuntary human gait change can indicate a forthcoming fall. Therefore, human body fall warning can help avoid falls and their caused injuries for the skeleton and joints. A simple and easy-to-use fall detection system based on gait analysis can be very helpful, especially if sensors of this system are implemented inside the shoes without causing a sensible discomfort for the user. We created a methodology for the fall prediction using three specially designed Velostat(®)-based wearable feet sensors installed in the shoe lining. Measured pressure distribution of the feet allows the analysis of the gait by evaluating the main parameters: stepping rhythm, size of the step, weight distribution between heel and foot, and timing of the gait phases. The proposed method was evaluated by recording normal gait and simulated abnormal gait of subjects. The obtained results show the efficiency of the proposed method: the accuracy of abnormal gait detection reached up to 94%. In this way, it becomes possible to predict the fall in the early stage or avoid gait discoordination and warn the subject or helping companion person. |
format | Online Article Text |
id | pubmed-8347941 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83479412021-08-08 Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis Bucinskas, Vytautas Dzedzickis, Andrius Rozene, Juste Subaciute-Zemaitiene, Jurga Satkauskas, Igoris Uvarovas, Valentinas Bobina, Rokas Morkvenaite-Vilkonciene, Inga Sensors (Basel) Article Human falls pose a serious threat to the person’s health, especially for the elderly and disease-impacted people. Early detection of involuntary human gait change can indicate a forthcoming fall. Therefore, human body fall warning can help avoid falls and their caused injuries for the skeleton and joints. A simple and easy-to-use fall detection system based on gait analysis can be very helpful, especially if sensors of this system are implemented inside the shoes without causing a sensible discomfort for the user. We created a methodology for the fall prediction using three specially designed Velostat(®)-based wearable feet sensors installed in the shoe lining. Measured pressure distribution of the feet allows the analysis of the gait by evaluating the main parameters: stepping rhythm, size of the step, weight distribution between heel and foot, and timing of the gait phases. The proposed method was evaluated by recording normal gait and simulated abnormal gait of subjects. The obtained results show the efficiency of the proposed method: the accuracy of abnormal gait detection reached up to 94%. In this way, it becomes possible to predict the fall in the early stage or avoid gait discoordination and warn the subject or helping companion person. MDPI 2021-08-03 /pmc/articles/PMC8347941/ /pubmed/34372477 http://dx.doi.org/10.3390/s21155240 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bucinskas, Vytautas Dzedzickis, Andrius Rozene, Juste Subaciute-Zemaitiene, Jurga Satkauskas, Igoris Uvarovas, Valentinas Bobina, Rokas Morkvenaite-Vilkonciene, Inga Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis |
title | Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis |
title_full | Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis |
title_fullStr | Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis |
title_full_unstemmed | Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis |
title_short | Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis |
title_sort | wearable feet pressure sensor for human gait and falling diagnosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347941/ https://www.ncbi.nlm.nih.gov/pubmed/34372477 http://dx.doi.org/10.3390/s21155240 |
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