Title: Machine learning based wearable sensor module for human fall detection - a fully functional solution

Authors: Juluru Anudeep; Shriram K. Vasudevan; T.S. Murugesh

Addresses: Loopus WearTech, No. 20-3/1-76, Flat No. 5, 3rd Floor, Sai Sidda Apartments, Palagani Prabhakar St., Ayodhya Nagar, Vijayawada, Andhra Pradesh 520003, India ' Huawei Developer Expert, Software Project Manager – MNC, Bengaluru, India ' Department of Electronics and Communication Engineering, Government College of Engineering Srirangam, Tiruchirappalli, 620 012, Tamil Nadu, India

Abstract: This paper addresses a potential concern faced by the majority of aged ones left unaccompanied at home. Any untoward fall owing to poor health conditions or slippery floors that are more prevalent among the elders happens; it usually goes unnoticed, and the aged ones are deprived of the potentially life-saving 'golden hour' treatment. To mitigate such problems faced by the elderly, we have designed a wrist-wearable fall detection system that employs a machine learning model for movement tracking and to detect a fall just in case. During a fall, an automated call is generated to the emergency services as well as to a caretaker through a GSM module. Two datasets are collected, trained, and tested on seven different machine learning models, and the results are presented.

Keywords: machine learning; fall detection; wearable device; medical applications.

DOI: 10.1504/IJMEI.2024.139887

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.4, pp.350 - 362

Received: 22 Jan 2022
Accepted: 03 Apr 2022
Published online: 09 Jul 2024 *

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