Pre-impact Alarm System for Fall Detection Using MEMS Sensors and HMM-based SVM Classifier

Liang, Shengyun; Chu, Tianyue; Lin, Dan; Ning, Yunkun; Li, Huiqi; Zhao, Guoru

doi:10.1109/embc.2018.8513119

Cited by 17 publications

(11 citation statements)

References 20 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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“…In addition to the threshold method, machine learning is also a common class of preimpact fall detection algorithms. Common machine learning algorithms include support vector machine (SVM) [14,[23][24][25], decision tree (DT), artificial neural network (ANN) [26], and k-nearest neighbor (K-NN) [27], which all can be used for model construction. Some have preferable performance including convolutional neural network (CNN), a class of support vector machine (1SVM), CNN+1SVM [28], hidden Markov model (HMM) [29], K-NN, and ANN [30].…”

Section: Preimpact Fall Detection Based On Machine Learningmentioning

confidence: 99%

“…ey also proposed that only by observing the feet and hands, the appropriate performance in terms of average detection time and accuracy can be obtained. e authors of [14] used the waist inertial sensor to measure acceleration and angular velocity and used the SVM algorithm based on hidden Markov model (HMM) for fall warning. e accuracy is 94.91%, the sensitivity is 97.22%, and the specificity is 93.75%.…”

Section: Introductionmentioning

confidence: 99%

Preimpact Fall Detection for Elderly Based on Fractional Domain

Liu

Zhang

et al. 2021

Mathematical Problems in Engineering

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The aging population has become a growing worldwide problem. Every year, deaths and injuries caused by elderly people's falls bring huge social costs. To reduce the rate of injury and death caused by falls among the elderly and the following social cost, the elderly must be monitored. In this context, falls detecting has become a hotspot for many research institutions and enterprises at home and abroad. This paper proposes an algorithm framework to prealarm the fall based on fractional domain, using inertial data sensor as motion data collection devices, preprocessing the data by axis synthesis and mean filtering, and using fractional-order Fourier transform to convert the collected data from time domain to fractional domain. Based on the above, a multilayer dichotomy classifier is designed, and each node parameter selection method is given, which constructed a preimpact fall detection system with excellent performance. The experiment result demonstrates that the algorithm proposed in this paper can guarantee better warning effect and classification accuracy with fewer features.

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“…In other words, threshold-based algorithms lack the generalizability and thus are difficult for practical applications. A few studies utilized conventional machine learning methods such as Support Vector Machine and Fisher Discriminant Analysis to predict pre-impact falls (Aziz et al, 2014;Liang et al, 2018;Wu et al, 2019). Tested by small amount of data from very limited types of simulated falls (≤7), they reported good prediction accuracy and reasonable lead time.…”

Section: Introductionmentioning

confidence: 99%