A Machine Learning Classification Paradigm for Automated Human Fall Detection

Main Article Content

Sarthak Turki, V Adithya, Rohit Bharti, Paras Bhat, Pranali G. Chavhan, Namrata N. Wasatkar

Abstract

For elderly people, falls are a severe worry since they can result in serious injuries, loss of independence, and deterioration of general health. In fact, among older persons, falls constitute the main reason for injury-related hospitalisations and fatalities. There is an obvious demand for fall detection systems that can help avoid or lessen the negative effects of falls given the enormous impact of falls on the senior population. Systems for detecting falls are created to notify carers or emergency services when a person has fallen, enabling quicker responses and better results. Elderly people who live alone or have mobility or balance impairments that make them more likely to fall may find these systems to be especially helpful.


The difficulty of categorising various actions as part of a system created to meet the demand for a wearable device to collect data for fall and near-fall analysis is addressed in this study. Three common activities—standing, walking, and lying down—four distinct fall trajectories—forward, backward, left, and right—as well as near-fall circumstances are recognised and detected.


Overall, fall detection systems play a significant role in the care of elderly people by lowering the chance of falls and its unfavourable effects. In order to better meet the demands of this vulnerable group, it's expected that as the older population grows, there will be a greater demand for fall detection systems and ongoing technological developments.

Article Details

How to Cite
Sarthak Turki, et al. (2023). A Machine Learning Classification Paradigm for Automated Human Fall Detection. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 1169–1176. https://doi.org/10.17762/ijritcc.v11i10.8638
Section
Articles
Author Biography

Sarthak Turki, V Adithya, Rohit Bharti, Paras Bhat, Pranali G. Chavhan, Namrata N. Wasatkar

Sarthak Turki1, V Adithya2, Rohit Bharti3, Paras Bhat4, Mrs. Pranali G. Chavhan5, Mrs. Namrata N. Wasatkar6

1Department of Computer Engineering

Vishwakarma Institute of Information Technology, Pune, India

s.turki1965@gmail.com

2Department of Computer Engineering

Vishwakarma Institute of Information Technology, Pune, India

adithyavenghat@gmail.com

3Department of Computer Engineering

Vishwakarma Institute of Information Technology, Pune, India

rohitbharti326452@gmail.com 

4Department of Computer Engineering

Vishwakarma Institute of Information Technology, Pune, India

parasbbhat20@gmail.com

5Department of Computer Engineering

Vishwakarma Institute of Information Technology, Pune, India

pranali.chavhan@viit.ac.in

6Department of Computer Engineering

Vishwakarma Institute of Information Technology, Pune, India

namrata.kharate@viit.ac.in