Literature DB >> 34372477

Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis.

Vytautas Bucinskas1, Andrius Dzedzickis1, Juste Rozene1, Jurga Subaciute-Zemaitiene1, Igoris Satkauskas2,3, Valentinas Uvarovas2,3, Rokas Bobina2,3, Inga Morkvenaite-Vilkonciene1.   

Abstract

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.

Entities:  

Keywords:  falling diagnosis; feet pressure sensor; human gait

Year:  2021        PMID: 34372477     DOI: 10.3390/s21155240

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  3 in total

Review 1.  Wearable Sensor Systems for Fall Risk Assessment: A Review.

Authors:  Sophini Subramaniam; Abu Ilius Faisal; M Jamal Deen
Journal:  Front Digit Health       Date:  2022-07-14

2.  Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM.

Authors:  Wei Liu; Xu Liu; Yuan Hu; Jie Shi; Xinqiang Chen; Jiansen Zhao; Shengzheng Wang; Qingsong Hu
Journal:  Sensors (Basel)       Date:  2022-07-21       Impact factor: 3.847

3.  The Automatization of the Gait Analysis by the Vicon Video System: A Pilot Study.

Authors:  Victoriya Smirnova; Regina Khamatnurova; Nikita Kharin; Elena Yaikova; Tatiana Baltina; Oskar Sachenkov
Journal:  Sensors (Basel)       Date:  2022-09-21       Impact factor: 3.847

  3 in total

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