| Literature DB >> 34372477 |
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