A Comprehensive Survey of Various Approaches on Human Fall Detection for Elderly People

Parmar, Rohit; Trapasiya, Samir

doi:10.1007/s11277-022-09816-6

Cited by 10 publications

(2 citation statements)

References 70 publications

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“…Human fall detection systems typically fall into two categories [28]: those that involve wearable sensors and those that rely on machine vision-based techniques. This section will cover a review of the relevant literature, exploring studies conducted on both categories within human fall detection systems.…”

Section: Related Workmentioning

confidence: 99%

A Robust and Automated Vision-Based Human Fall Detection System Using 3D Multi-Stream CNNs with an Image Fusion Technique

2023

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Unintentional human falls, particularly in older adults, can result in severe injuries and death, and negatively impact quality of life. The World Health Organization (WHO) states that falls are a significant public health issue and the primary cause of injury-related fatalities worldwide. Injuries resulting from falls, such as broken bones, trauma, and internal injuries, can have severe consequences and can lead to a loss of mobility and independence. To address this problem, there have been suggestions to develop strategies to reduce the frequency of falls, in order to decrease healthcare costs and productivity loss. Vision-based fall detection approaches have proven their effectiveness in addressing falls on time, which can help to reduce fall injuries. This paper introduces an automated vision-based system for detecting falls and issuing instant alerts upon detection. The proposed system processes live footage from a monitoring surveillance camera by utilizing a fine-tuned human segmentation model and image fusion technique as pre-processing and classifying a set of live footage with a 3D multi-stream CNN model (4S-3DCNN). The system alerts when the sequence of the Falling of the monitored human, followed by having Fallen, takes place. The effectiveness of the system was assessed using the publicly available Le2i dataset. System validation revealed an impressive result, achieving an accuracy of 99.44%, sensitivity of 99.12%, specificity of 99.12%, and precision of 99.59%. Based on the reported results, the presented system can be a valuable tool for detecting human falls, preventing fall injury complications, and reducing healthcare and productivity loss costs.

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Section: Related Workmentioning

confidence: 99%

A Robust and Automated Vision-Based Human Fall Detection System Using 3D Multi-Stream CNNs with an Image Fusion Technique

2023

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“…According to the World Health Organization (WHO), falls are the second leading cause of accidental or unintentional injury deaths worldwide, with adults over 65 being the most vulnerable group [1]. The statistics are alarming: more than 37.3 million falls requiring medical attention occur annually, and this number is expected to rise with the global increase in the elderly population [2]. Early recognition and timely intervention are crucial in mitigating the consequences of falls, prompting extensive research and development efforts in fall-detection systems [3].…”

Section: Introductionmentioning

confidence: 99%

An Interpretable Modular Deep Learning Framework for Video-Based Fall Detection

Dutt,

Gupta,

Goodwin

et al. 2024

Applied Sciences

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Falls are a major risk factor for older adults, increasing morbidity and healthcare costs. Video-based fall-detection systems offer crucial real-time monitoring and assistance. Yet, their deployment faces challenges such as maintaining privacy, reducing false alarms, and providing understandable outputs for healthcare providers. This paper introduces an innovative automated fall-detection framework that includes a Gaussian blur module for privacy preservation, an OpenPose module for precise pose estimation, a short-time Fourier transform (STFT) module to capture frames with significant motion selectively, and a computationally efficient one-dimensional convolutional neural network (1D-CNN) classification module designed to classify these frames. Additionally, integrating a gradient-weighted class activation mapping (GradCAM) module enhances the system’s explainability by visually highlighting the movement of the key points, resulting in classification decisions. Modular flexibility in our system allows customization to meet specific privacy and monitoring needs, enabling the activation or deactivation of modules according to the operational requirements of different healthcare settings. This combination of STFT and 1D-CNN ensures fast and efficient processing, which is essential in healthcare environments where real-time response and accuracy are vital. We validated our approach across multiple datasets, including the Multiple Cameras Fall Dataset (MCFD), the UR fall dataset, and the NTU RGB+D Dataset, which demonstrates high accuracy in detecting falls and provides the interpretability of results.

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A spatio-temporal graph neural network for fall prediction with inertial sensors

Wang,

Li,

Liao

et al. 2024

Knowledge-Based Systems

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A Comprehensive Survey of Various Approaches on Human Fall Detection for Elderly People

Cited by 10 publications

References 70 publications

A Robust and Automated Vision-Based Human Fall Detection System Using 3D Multi-Stream CNNs with an Image Fusion Technique

A Robust and Automated Vision-Based Human Fall Detection System Using 3D Multi-Stream CNNs with an Image Fusion Technique

An Interpretable Modular Deep Learning Framework for Video-Based Fall Detection

A spatio-temporal graph neural network for fall prediction with inertial sensors

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