Deep Learning Based Systems Developed for Fall Detection: A Review

Md Milon Islam,Md Repon Islam,Omar Tayan,Muhammad Nomani Kabir,Md Rabiul Islam,Sheikh Nooruddin,Md Saiful Islam

doi:10.1109/access.2020.3021943

Md Milon Islam, Md Repon Islam + Show 5 more

Open Access

https://doi.org/10.1109/access.2020.3021943

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Abstract

Accidental falls are a major source of loss of autonomy, deaths, and injuries among the elderly. Accidental falls also have a remarkable impact on the costs of national health systems. Thus, extensive research and development of fall detection and rescue systems are a necessity. Technologies related to fall detection should be reliable and effective to ensure a proper response. This article provides a comprehensive review on state-of-the-art fall detection technologies considering the most powerful deep learning methodologies. We reviewed the most recent and effective deep learning methods for fall detection and categorized them into three categories: Convolutional Neural Network (CNN) based systems, Long Short-Term Memory (LSTM) based systems, and Auto-encoder based systems. Among the reviewed systems, three dimensional (3D) CNN, CNN with 10-fold cross-validation, LSTM with CNN based systems performed the best in terms of accuracy, sensitivity, specificity, etc. The reviewed systems were compared based on their working principles, used deep learning methods, used datasets, performance metrics, etc. This review is aimed at presenting a summary and comparison of existing state-of-the-art deep learning based fall detection systems to facilitate future development in this field.

Highlights

Falls are a major cause of serious injuries for the elderly population worldwide
The summary of the reviewed Convolutional Neural Network (CNN) based systems is shown in Table 1, organized with the following criteria: the category of the systems, devices used for acquisition of raw data, deep learning methods and other techniques used for the systems, whether a system only detects fall or Activities of Daily Life (ADL), used datasets, etc
The summary of the reviewed Long Short-Term Memory (LSTM) based systems is shown in Table 2, organized with the following criteria: the category of the systems, devices used for acquisition of raw data, deep learning methods and other techniques used for the systems, whether a system only detects fall or ADLs, used

Summary

INTRODUCTION

Falls are a major cause of serious injuries for the elderly population worldwide. Falls impede their comfortable and independent living. Islam et al.: Deep Learning Based Systems Developed for Fall Detection: A Review behind fall-related casualties. The reviewed systems are practical systems that employ deep learning methods for the proper detection and recognition of fall events among Activities of Daily Life (ADL) events. All of the reviewed fall detection systems have some general steps, combined with sensing, data processing, fall event recognition and emergency alert system to rescue the victim. CNN based fall detection systems represent the fall and ADL related data in image form [51], [57]. The reviewed systems, combinations of LSTM and CNN are used for fall detection to overcome the general vision related problems such as image noise, occlusion, incorrect segmentation, perspective, etc. The categorization focuses on how the different principal methods (CNN, LSTM, and Auto-encoder) handle the event data captured by sensors.

LITERATURE ON FALL DETECTION SYSTEMS

Findings

CONCLUSION