Implementation of wearable sensors for fall detection into smart household

Biros, Ondrej; Karchňák, Ján; Šimšík, Dušan; Hosŏvský, Alexander

doi:10.1109/sami.2014.6822406

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…Figure 1 shows a block diagram of a human fall detection system that uses deep learning. Various sensors, including wearable smartwatches, body sensors, and mounted IP cameras, can be utilized to gather data about the patient [29]. The obtained data are conveyed to a local server for pre-processing, which may involve filtering out redundant information, consolidating frames, and eliminating noise.…”

Section: Our Proposed Methodsmentioning

confidence: 99%

“…As our main focus is on vision-based sensors, in this section we briefly discuss some major contributions of wearable sensors in human fall detection systems. The systems use a variety of sensors, including accelerometers, pressure sensors, gyroscopes, tilt switches, and magnetometers, to record data about the user's body movements that may be used to identify and prevent falls [29]. Additionally, these sensors can be utilized in diverse environments and are dependent on datasets to achieve accurate fall detection [30].…”

Section: Wearable Sensorsmentioning

confidence: 99%

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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: Our Proposed Methodsmentioning

confidence: 99%

Section: Wearable Sensorsmentioning

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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show abstract

“…The system used machine learning methods and threshold methods. A computationally efficient system was proposed in [ 23 ]. In the system, machine learning was used to analyze the behavior of lower limb muscles to detect pre-falls.…”

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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“…Within the last years, there are many fall detectors that have been developed. Wearable sensors using accelerometer and gyroscope are used to detect fall, while considering acceleration information, tilt, and angular velocity [3]. Smart…”

Section: Introductionmentioning

confidence: 99%

u-FASt: Ubiquitous fall detection and alert system for elderly people in smart home environment

Rakhman

Kurnianingsih

Nugroho

et al. 2014

2014 Makassar International Conference on Electrical Engineering and Informatics (MICEEI)

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Elderly people with long-term care and some disabilities are more susceptible to falls. Fall can cause accidental or unintentional injury, even deaths. Fall detection monitoring is needed amongst elderly, particularly for elderly people who like living independently. Physical limitations and disabilities of elderly in doing their daily activities, which susceptible to fall, become the reason why fall detection is required. This paper presents a prototype of ubiquitous fall detection and alert system in smart home environment (u-FASt) using smartphone. This proposed system of u-FASt has three features, namely: (1) Sensing data gathered from accelerometer and gyroscope that embedded in the smartphone; (2) Real time alerting system consists of mobile alarm, SMS notification, and automatic calling; (3) History of falls that consists of time of fall, body position of fall, and location of fall. The result of the experiments showed that we could get 95.33% for accuracy level.

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Implementation of wearable sensors for fall detection into smart household

Cited by 6 publications

References 4 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

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

u-FASt: Ubiquitous fall detection and alert system for elderly people in smart home environment

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