A Class-Imbalanced Deep Learning Fall Detection Algorithm Using Wearable Sensors

Falling represents one of the most serious health risks for elderly people; it may cause irreversible injuries if the individual cannot obtain timely treatment after the fall happens. Therefore, timely and accurate fall detection algorithm research is extremely important. Recently, a number of resea...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 21; no. 19; p. 6511
Main Authors: Zhang, Jing, Li, Jia, Wang, Weibing
Format: Journal Article
Language:English
Published: Basel MDPI AG 29.09.2021
MDPI
Subjects:
Algorithms
class imbalance
Deep learning
fall detection
Machine learning
Medical research
Methods
Neural networks
Older people
Sensors
Support vector machines
Wearable computers
wearable sensor
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Falling represents one of the most serious health risks for elderly people; it may cause irreversible injuries if the individual cannot obtain timely treatment after the fall happens. Therefore, timely and accurate fall detection algorithm research is extremely important. Recently, a number of researchers have focused on fall detection and made many achievements, and most of the relevant algorithm studies are based on ideal class-balanced datasets. However, in real-life applications, the possibilities of Activities of Daily Life (ADL) and fall events are different, so the data collected by wearable sensors suffers from class imbalance. The previously developed algorithms perform poorly on class-imbalanced data. In order to solve this problem, this paper proposes an algorithm that can effectively distinguish falls from a large amount of ADL signals. Compared with the state-of-the-art fall detection algorithms, the proposed method can achieve the highest score in multiple evaluation methods, with a sensitivity of 99.33%, a specificity of 91.86%, an F-Score of 98.44% and an AUC of 98.35%. The results prove that the proposed algorithm is effective on class-imbalanced data and more suitable for real-life application compared to previous works.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s21196511