A survey of fall detection model based on wearable sensor

Li, Congcong; Teng, Guifa; Zhang, Yuting

doi:10.1109/hsi47298.2019.8942593

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…The criteria for evaluating fall detection algorithms' effectiveness consists of three main metrics: accuracy, sensitivity, and specificity [7]. Accuracy refers to the proportion of all actions that are correctly detected as falls or no falls.…”

Section: Comparison With Other Classification Methodsmentioning

confidence: 99%

Fall detection algorithm based on lightweight 3D residual network

Peng

2023

International Conference on Electronic Information Engineering and Computer Science (EIECS 2022)

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Aiming at the many parameters and high computational complexity of video-based deep learning fall detection models, we propose a lightweight fall detection algorithm for 3D residual networks. In this approach, we design a low-rank depthseparable convolution structure. When performing deep convolution, the 3-dimensional parameter matrix is decomposed into 1-dimensional and 2-dimensional parameter matrices to reduce the model parameters and thus improve the performance. Meanwhile, the dataset is built by referring to the format of the URFall dataset and capturing videos of human falling and non-falling states from multiple angles using RGB cameras. The experimental results show that the lightweight 3D residual network can achieve 98.23% accuracy in distinguishing falls from non-falls, and the sensitivity and specificity are kept at a high and stable level.

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Section: Comparison With Other Classification Methodsmentioning

confidence: 99%

Fall detection algorithm based on lightweight 3D residual network

Peng

2023

International Conference on Electronic Information Engineering and Computer Science (EIECS 2022)

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“…The placement of wearable sensors plays an essential role in recognizing falls with a high accuracy rate [10]. The important motion data that contribute to fall-event detection are associated with the moving patterns of the chest, elbow, and wrist from the upper body, and the thigh and ankle from the lower part of the body.…”

Section: Smart Ppc Prototypementioning

confidence: 99%

“…A CAS generally integrates a set of vision systems with cameras and other sensors, such as microphones or vibration sensors, placed in a welllit environment to monitor the user within the range of view [9]. Wearable sensors are commonly used to analyze a human's fall activities, based on the motion pattern [10] and physiological status [11]. Even though FDSs have been widely deployed in the healthcare sector, especially for elderly people, the system is targeted at detecting slowfalling movements, which is not suitable for firefighters.…”

Section: Introductionmentioning

confidence: 99%

Smart Wearables with Sensor Fusion for Fall Detection in Firefighting

Chai

Pike

et al. 2021

Sensors

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During the past decade, falling has been one of the top three causes of death amongst firefighters in China. Even though there are many studies on fall-detection systems (FDSs), the majority use a single motion sensor. Furthermore, few existing studies have considered the impact sensor placement and positioning have on fall-detection performance; most are targeted toward fall detection of the elderly. Unfortunately, floor cracks and unstable building structures in the fireground increase the difficulty of detecting the fall of a firefighter. In particular, the movement activities of firefighters are more varied; hence, distinguishing fall-like activities from actual falls is a significant challenge. This study proposed a smart wearable FDS for firefighter fall detection by integrating motion sensors into the firefighter’s personal protective clothing on the chest, elbows, wrists, thighs, and ankles. The firefighter’s fall activities are detected by the proposed multisensory recurrent neural network, and the performances of different combinations of inertial measurement units (IMUs) on different body parts were also investigated. The results indicated that the sensor fusion of IMUs from all five proposed body parts achieved performances of 94.10%, 92.25%, and 94.59% in accuracy, sensitivity, and specificity, respectively.

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