Fall detection system for older adults

Angal, Yogesh; Jagtap, Aarti

doi:10.1109/icaecct.2016.7942595

Cited by 9 publications

(7 citation statements)

References 4 publications

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“…Diraco et al [6] evaluated the distance of a 3D human centroid from the floor plane to detect falls. Angal et al [32] used the Microsoft Kinect sensor to detect a fall by collecting information on the velocity, acceleration, and width height ratio of a human object. However, the above-mentioned methods may not be accurate when the person lies on the ground.…”

Section: Image-based Methodsmentioning

confidence: 99%

An Image-Based Fall Detection System for the Elderly

Chu

2018

Applied Sciences

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Due to advances in medical technology, the elderly population has continued to grow. Elderly healthcare issues have been widely discussed—especially fall accidents—because a fall can lead to a fracture and have serious consequences. Therefore, the effective detection of fall accidents is important for both elderly people and their caregivers. In this work, we designed an Image-based FAll Detection System (IFADS) for nursing homes, where public areas are usually equipped with surveillance cameras. Unlike existing fall detection algorithms, we mainly focused on falls that occur while sitting down and standing up from a chair, because the two activities together account for a higher proportion of falls than forward walking. IFADS first applies an object detection algorithm to identify people in a video frame. Then, a posture recognition method is used to keep tracking the status of the people by checking the relative positions of the chair and the people. An alarm is triggered when a fall is detected. In order to evaluate the effectiveness of IFADS, we not only simulated different fall scenarios, but also adopted YouTube and Giphy videos that captured real falls. Our experimental results showed that IFADS achieved an average accuracy of 95.96%. Therefore, IFADS can be used by nursing homes to improve the quality of residential care facilities.

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Section: Image-based Methodsmentioning

confidence: 99%

An Image-Based Fall Detection System for the Elderly

Chu

2018

Applied Sciences

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“…The fall detection system in [ 37 ] relied on the Microsoft Kinect camera to extract RGB data and depth data. In real-time, their system read each frame from the Kinect camera.…”

Section: Related Workmentioning

confidence: 99%

Human Fall Detection Using 3D Multi-Stream Convolutional Neural Networks with Fusion

Alanazi

Muhammad

2022

Diagnostics

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Human falls, especially for elderly people, can cause serious injuries that might lead to permanent disability. Approximately 20–30% of the aged people in the United States who experienced fall accidents suffer from head trauma, injuries, or bruises. Fall detection is becoming an important public healthcare problem. Timely and accurate fall incident detection could enable the instant delivery of medical services to the injured. New advances in vision-based technologies, including deep learning, have shown significant results in action recognition, where some focus on the detection of fall actions. In this paper, we propose an automatic human fall detection system using multi-stream convolutional neural networks with fusion. The system is based on a multi-level image-fusion approach of every 16 frames of an input video to highlight movement differences within this range. This results of four consecutive preprocessed images are fed to a new proposed and efficient lightweight multi-stream CNN model that is based on a four-branch architecture (4S-3DCNN) that classifies whether there is an incident of a human fall. The evaluation included the use of more than 6392 generated sequences from the Le2i fall detection dataset, which is a publicly available fall video dataset. The proposed method, using three-fold cross-validation to validate generalization and susceptibility to overfitting, achieved a 99.03%, 99.00%, 99.68%, and 99.00% accuracy, sensitivity, specificity, and precision, respectively. The experimental results prove that the proposed model outperforms state-of-the-art models, including GoogleNet, SqueezeNet, ResNet18, and DarkNet19, for fall incident detection.

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“…Such unintentional falls can lead to serious injuries and even death in the elderly [4]. Approximately 30-50% of falls cause minor injuries, such as bruises or contusion, but 5-10% of falls lead to major injuries, such as fractures and traumatic brain injury [5].…”

Section: Introductionmentioning

confidence: 99%

Relationship between Associated Neuropsychological Factors and Fall Risk Factors in Community-Dwelling Elderly

Jang

Yim

2022

Healthcare

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This study examined whether neuropsychological factors could affect fall risk factors in the community-dwelling elderly via correlation analysis. A total of 393 older adults (76.69 ± 6.01) participated in this study. Cognitive function, depression, fall efficacy, balance confidence, balance, gait, and muscle strength were evaluated, and the correlation between psychological factors and fall risk factors was analyzed. Additionally, a multiple regression analysis was conducted to determine whether or not there was a significant effect between psychological factors and fall risk factors. Analysis showed that the psychological factors examined were all significantly correlated with the fall risk factors. A correlation analysis between cognitive function and fall risk factors showed that the correlation coefficient of the 6-Meter Walk Test was highest; for depression and fall risk factors, the correlation coefficient of gait speed was highest; for fall efficacy and fall risk factors, the correlation coefficient of the 6-Meter Walk Test was highest; and for confidence in balancing and fall risk factors, the correlation coefficient of the 6-Meter Walk Test was highest. This study suggests that psychological factors affect fall risk factors in the community-dwelling elderly, and a multifaceted approach that includes psychological factors would be helpful in providing interventions for falls.

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Fall detection system for older adults

Cited by 9 publications

References 4 publications

An Image-Based Fall Detection System for the Elderly

An Image-Based Fall Detection System for the Elderly

Human Fall Detection Using 3D Multi-Stream Convolutional Neural Networks with Fusion

Relationship between Associated Neuropsychological Factors and Fall Risk Factors in Community-Dwelling Elderly

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