Fall-prediction algorithm using a neural network for safety enhancement of elderly

Shih Hung Yang, Wenlong Zhang, Yizou Wang, Masayoshi Tomizuka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

Among the elderly, falls are a well-known safety hazard, often resulting in major injury, hospitalization and death. To reduce the injuries caused by falls, it is first necessary to predict a fall as early as possible and then to provide protection for the person who is falling. This paper proposes a fall-prediction algorithm (FPA) that can predict whether the person will fall within one-walking-step. The fall prediction is different from the fall detection, and it is intended to predict a fall before it occurs and provide sufficient time to enable a safety mechanism. The proposed FPA adopts a neural network to perform prediction in which the inputs are accelerations and angular rates of upper trunk and the output presents fall or no fall. A wearable inertial sensor package with a triple axis accelerometer and a triple axis gyroscope is developed to measure the required motion data. Five subjects were asked to wear the inertial sensor package and perform a number of simulated falls. The experimental results show that the FPA could predict a fall 0.4 seconds prior to the beginning of the fall. The time interval is sufficient to inflate an airbag covering the head, trunk, and hip, an intervention that would reduce fall-related injuries among older people.

Original languageEnglish
Title of host publication2013 CACS International Automatic Control Conference, CACS 2013 - Conference Digest
Pages245-249
Number of pages5
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 CACS International Automatic Control Conference, CACS 2013 - Nantou, Taiwan
Duration: 2013 Dec 22013 Dec 4

Publication series

Name2013 CACS International Automatic Control Conference, CACS 2013 - Conference Digest

Other

Other2013 CACS International Automatic Control Conference, CACS 2013
CountryTaiwan
CityNantou
Period13-12-0213-12-04

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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