A Comparative Study of Fall Detection Device for Elderly People Using Machine Learning

J, Dr. Dileep; G, Janavi V; Pallavi, Kampalli; Krishna, Talapaneni Mohan; Kumar, R. Ranjith

doi:https://doi.org/10.55041/ijcope.v2i9.099

Volume 02, Issue 9

Published on: September 2026

A COMPARATIVE STUDY OF FALL DETECTION DEVICE FOR ELDERLY PEOPLE USING MACHINE LEARNING

Dr. Dileep J Janavi V G Kampalli Pallavi Talapaneni Mohan Krishna R. Ranjith Kumar

Dept. of ECE / K S School of Engineering and Management /

Visvesvaraya Technological University, Bengaluru, India

DOI:https://doi.org/10.55041/ijcope.v2i9.099

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Abstract

Falls are a major cause of injury, disability, and hospitalization among elderly individuals, particularly those living independently without continuous supervision. The absence of timely assistance following a fall can lead to severe medical complications and increased mortality risk. To address this challenge, extensive research has been carried out in the domain of fall detection, employing a variety of approaches ranging from simple threshold-based methods to advanced Machine Learning and Deep Learning techniques. This paper presents a comparative study of existing fall detection methodologies, evaluating their accuracy, computational complexity, power consumption, and suitability for real-time wearable applications. Threshold-based systems, while computationally simple, often suffer from high false-positive rates and lack adaptability to varied movement patterns. Deep Learning-based approaches offer improved accuracy but demand significant computational resources, making them less suitable for low-power embedded devices. Based on insights drawn from this comparative analysis, the paper proposes a smart fall detection system that combines wearable sensor technology, a lightweight Machine Learning model, and IoT-based communication to achieve a practical balance between accuracy, efficiency, and cost-effectiveness. The system acquires motion data through a tri-axial accelerometer and gyroscope, applies preprocessing and feature extraction techniques, and utilizes a lightweight ML model deployed on a low-power microcontroller for real-time fall classification. Upon detecting a fall, the system triggers immediate local alerts and transmits emergency notifications along with location data to caregivers through an IoT platform, along with a reset mechanism to minimize false alarms. The comparative evaluation demonstrates that the proposed approach effectively overcomes the limitations of traditional threshold-based and resource-intensive deep learning methods, offering a scalable and reliable solution for elderly safety and remote health monitoring.

How to Cite this Paper

J, D., G, J. V., Pallavi, K., Krishna, T. M. & Kumar, R. R. (2026). A Comparative Study of Fall Detection Device for Elderly People Using Machine Learning. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(9), 1-9. https://doi.org/10.55041/ijcope.v2i9.099

J, Dileep, et al.. "A Comparative Study of Fall Detection Device for Elderly People Using Machine Learning." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 9, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i9.099.

J, Dileep,Janavi G,Kampalli Pallavi,Talapaneni Krishna, and R. Kumar. "A Comparative Study of Fall Detection Device for Elderly People Using Machine Learning." International Journal of Creative and Open Research in Engineering and Management 02, no. 9 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i9.099.

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•Published on: Sep 16 2026

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