Elderly Fall Detection using Lightweight Convolution Deep Learning Model

Neeraj Varshney

doi:10.17762/turcomat.v12i2.1814

Abstract

Old people, who are living alone at home face serious problem of Falls while moving from one place to another and sometime life threading also. In order to prevent this situation, several fall monitoring systems based on sensor data were proposed. However, there was an issue of misclassification to identify the fall as daily life activities and also routine activity as fall. Towards this end, a deep learning based model is proposed in this paper by using the data of heart rate, BP and sugar level to identify fall along with other daily life activities like walking, running jogging etc. For accurate identification of fall accidents, a publicly accessible data collection and a lightly weighted CNN model are used. The model reports proposed and 98.21 % precision.

Highlights

Behavioral pattern of anybody can be identify through the various activities performed
During the last few decades, many researchers have tried to carry out the fall detection activity
Hakim et al [16] for everyday activity classification, a proposed threshold algorithm focused on fall detection and supervised machine learning

Summary

Introduction

Behavioral pattern of anybody can be identify through the various activities performed. As per the statistics [1], at least 103.5 million people are over age of 60 in India and many of such people are living alone at their home [2].One major concern is to monitor their daily activates, to avoid any kind of casualty with them like fall while moving from here to there or fall while trying to sit or get up or many more such situation [3,4,5]. Fall and ADL both are the part of same bouquet because fall occur while performing the daily activities [13]

Literature review

Proposed model

Dataset

Findings

Conclusion