A Highly Reliable Wrist-Worn Acceleration-Based Fall Detector

Saleh, Majd; Georgi, Nawras; Abbas, Manuel; Jeannès, Régine Le Bouquin

doi:10.23919/eusipco.2019.8902563

Cited by 8 publications

(12 citation statements)

References 14 publications

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“…In this section, we report on seven works in which the data was collected by the authors [42][43][44][45][46][47][48]. Two additional works [12,13] used datasets as the basis for their work.…”

Section: Fall Detectionmentioning

confidence: 99%

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A Systematic Review of Wearable Sensors for Monitoring Physical Activity

Kristoffersson

Lindén

2022

Sensors

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This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological disease progression. The article provides in-depth information on the retrieved articles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthy participants vs patients, and study conditions. It is well known that motion patterns change with age and the onset of illnesses, and that the risk of falling increases with age. Yet, studies including older persons are rare. Gender distribution was not even provided in several studies, and others included only, or a majority of, men. Another shortcoming is that none of the studies were conducted in real-life conditions. Hence, there is still important work to be done in order to increase the usefulness of wearable sensors in these areas. The article highlights flaws in how studies based on previously collected datasets report on study samples and the data collected, which makes the validity and generalizability of those studies low. Exceptions exist, such as the promising recently reported open dataset FallAllD, wherein a longitudinal study with older adults is ongoing.

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Section: Fall Detectionmentioning

confidence: 99%

“…One work focuses solely on fall detection [12]. Six works discriminate between falls and ADLs [13,[42][43][44][45]49]. Four works focus on near-fall detection [44,[46][47][48].…”

Section: Fall Detectionmentioning

confidence: 99%

A Systematic Review of Wearable Sensors for Monitoring Physical Activity

Kristoffersson

Lindén

2022

Sensors

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“…This is feasible since the average amount of data to be sent is only 42 K Bytes/day (11 windows×13 s×50 Hz×3 axes×2 Bytes) for wrist-worn devices and 13 K Bytes/day for neck-worn devices. We tested this kind of solution in our previous work [11] in which a remote computer receives the raw acceleration data and executes a complex fall detection algorithm; 2) embedding the algorithm in a wearable fall detector that is not necessarily restricted to give a decision in real-time but in near-real-time instead. For instance, the algorithm could give a decision delayed for few seconds.…”

Section: Practical Considerationsmentioning

confidence: 99%

“…Fall detection is still an open research topic to date [2], [3], [4]. Most of fall detection algorithms are based on machine learning (ML) like [5], [6], [7], [8], [9], [10], [11]. MLbased solutions require relatively large datasets for training and performance evaluation.…”

Section: Introductionmentioning

confidence: 99%

A Reliable Fall Detection System Based on Analyzing the Physical Activities of Older Adults Living in Long-Term Care Facilities

Saleh

Abbas

Prud'Homm

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Fall detection systems are designed in view to reduce the serious consequences of falls thanks to the early automatic detection that enables a timely medical intervention. The majority of the state-of-the-art fall detection systems are based on machine learning (ML). For training and performance evaluation, they use some datasets that are collected following predefined simulation protocols i.e. subjects are asked to perform different types of activities and to repeat them several times. Apart from the quality of simulating the activities, protocol-based data collection results in big differences between the distribution of the activities of daily living (ADLs) in these datasets in comparison with the actual distribution in real life. In this work, we first show the effects of this problem on the sensitivity of the ML algorithms and on the interpretability of the reported specificity. Then, we propose a reliable design of an MLbased fall detection system that aims at discriminating falls from the ambiguous ADLs. The latter are extracted from 400 days of recorded activities of older adults experiencing their daily life. The proposed system can be used in neckand wrist-worn fall detectors. In addition, it is invariant to the rotation of the wearable device. The proposed system shows 100% of sensitivity while it generates an average of one false positive every 25 days for the neck-worn device and an average of one false positive every 3 days for the wrist-worn device.

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“…The current research work on fall detection algorithms is mainly divided into three types: wearable device-based methods [3], scene sensor-based methods [4], and computer vision-based methods. Wearable-based fall detection methods are to wear sensor devices including accelerometers, gyroscopes, etc.…”

Section: Introductionmentioning

confidence: 99%

A Computer Vision-Based Dual Network Approach for Indoor Fall Detection

Zhang¹,

Fang²,

Zhu³

2020

IJISRT

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In order to strengthen the monitoring of the elderly and reduce the safety risks caused by falls, a video-based indoor fall detection algorithm using a dual network structure is proposed. Firstly, for the recorded video stream, we apply the fine-tuned YOLACT network to extract the contours of the human body, and then design a simple convolutional neural network to distinguish the categories of different family activities (including bending, standing, sitting and lying), and finally make a fall decision. When a lying position is detected on the floor region, it is considered as a fall. Experiments show that the proposed algorithm can successfully detect fall events in different indoor scenarios, and have a low false detection rate on the constructed data set.

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A Highly Reliable Wrist-Worn Acceleration-Based Fall Detector

Cited by 8 publications

References 14 publications

A Systematic Review of Wearable Sensors for Monitoring Physical Activity

A Systematic Review of Wearable Sensors for Monitoring Physical Activity

A Reliable Fall Detection System Based on Analyzing the Physical Activities of Older Adults Living in Long-Term Care Facilities

A Computer Vision-Based Dual Network Approach for Indoor Fall Detection

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