Fall Detection Algorithm Based on Inertial Sensor and Hierarchical Decision

With the aging of the human body and the reduction in its physiological capacities, falls have become a huge threat to individuals’ physical and mental health, leading to serious bodily damage to the elderly and financial pressure on their families. As a result, it is vital to design a fall detectio...

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Published in:	Sensors (Basel, Switzerland) Vol. 23; no. 1; p. 107
Main Authors:	Zheng, Liang, Zhao, Jie, Dong, Fangjie, Huang, Zhiyong, Zhong, Daidi
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 22.12.2022 MDPI
Subjects:	Accuracy Aged Aging Algorithms Analysis Behavior Classification Datasets Decision making fall detection feature dimensionality reduction Global positioning systems GPS Humans Machine learning Methods Monitoring, Ambulatory - methods Neural networks Older people Sensors Support vector machines Wearable computers XGBoost China XGBoost fall detection feature dimensionality reduction
ISSN:	1424-8220, 1424-8220
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Abstract	With the aging of the human body and the reduction in its physiological capacities, falls have become a huge threat to individuals’ physical and mental health, leading to serious bodily damage to the elderly and financial pressure on their families. As a result, it is vital to design a fall detection algorithm that monitors the state of human activity. This work designs a human fall detection algorithm based on hierarchical decision making. First, this work proposes a dimensionality reduction approach based on feature importance analysis (FIA), which optimizes the feature space via feature importance. This procedure reduces the dimension of features greatly and reduces the time spent by the model in the training phase. Second, this work proposes a hierarchical decision-making algorithm with an XGBoost model. The algorithm is divided into three levels. The first level uses the threshold approach to make a preliminary assessment of the data and only transfers the fall type data to the next level. The second level is an XGBoost-based classification algorithm to analyze again the type of data which remained from the first level. The third level employs a comparison method to determine the direction of the falling. Finally, the fall detection algorithm proposed in this paper has an accuracy of 98.19%, a sensitivity of 97.50%, and a specificity of 98.63%. The classification accuracy of the fall direction reaches 93.44%, and the algorithm can efficiently determine the fall direction.
AbstractList	With the aging of the human body and the reduction in its physiological capacities, falls have become a huge threat to individuals’ physical and mental health, leading to serious bodily damage to the elderly and financial pressure on their families. As a result, it is vital to design a fall detection algorithm that monitors the state of human activity. This work designs a human fall detection algorithm based on hierarchical decision making. First, this work proposes a dimensionality reduction approach based on feature importance analysis (FIA), which optimizes the feature space via feature importance. This procedure reduces the dimension of features greatly and reduces the time spent by the model in the training phase. Second, this work proposes a hierarchical decision-making algorithm with an XGBoost model. The algorithm is divided into three levels. The first level uses the threshold approach to make a preliminary assessment of the data and only transfers the fall type data to the next level. The second level is an XGBoost-based classification algorithm to analyze again the type of data which remained from the first level. The third level employs a comparison method to determine the direction of the falling. Finally, the fall detection algorithm proposed in this paper has an accuracy of 98.19%, a sensitivity of 97.50%, and a specificity of 98.63%. The classification accuracy of the fall direction reaches 93.44%, and the algorithm can efficiently determine the fall direction. With the aging of the human body and the reduction in its physiological capacities, falls have become a huge threat to individuals' physical and mental health, leading to serious bodily damage to the elderly and financial pressure on their families. As a result, it is vital to design a fall detection algorithm that monitors the state of human activity. This work designs a human fall detection algorithm based on hierarchical decision making. First, this work proposes a dimensionality reduction approach based on feature importance analysis (FIA), which optimizes the feature space via feature importance. This procedure reduces the dimension of features greatly and reduces the time spent by the model in the training phase. Second, this work proposes a hierarchical decision-making algorithm with an XGBoost model. The algorithm is divided into three levels. The first level uses the threshold approach to make a preliminary assessment of the data and only transfers the fall type data to the next level. The second level is an XGBoost-based classification algorithm to analyze again the type of data which remained from the first level. The third level employs a comparison method to determine the direction of the falling. Finally, the fall detection algorithm proposed in this paper has an accuracy of 98.19%, a sensitivity of 97.50%, and a specificity of 98.63%. The classification accuracy of the fall direction reaches 93.44%, and the algorithm can efficiently determine the fall direction.With the aging of the human body and the reduction in its physiological capacities, falls have become a huge threat to individuals' physical and mental health, leading to serious bodily damage to the elderly and financial pressure on their families. As a result, it is vital to design a fall detection algorithm that monitors the state of human activity. This work designs a human fall detection algorithm based on hierarchical decision making. First, this work proposes a dimensionality reduction approach based on feature importance analysis (FIA), which optimizes the feature space via feature importance. This procedure reduces the dimension of features greatly and reduces the time spent by the model in the training phase. Second, this work proposes a hierarchical decision-making algorithm with an XGBoost model. The algorithm is divided into three levels. The first level uses the threshold approach to make a preliminary assessment of the data and only transfers the fall type data to the next level. The second level is an XGBoost-based classification algorithm to analyze again the type of data which remained from the first level. The third level employs a comparison method to determine the direction of the falling. Finally, the fall detection algorithm proposed in this paper has an accuracy of 98.19%, a sensitivity of 97.50%, and a specificity of 98.63%. The classification accuracy of the fall direction reaches 93.44%, and the algorithm can efficiently determine the fall direction.
Audience	Academic
Author	Huang, Zhiyong Zheng, Liang Zhao, Jie Dong, Fangjie Zhong, Daidi
AuthorAffiliation	1 Bioengineering College, Chongqing University, Chongqing 400044, China 2 The 15th Research Institute of China Electronics Technology Group Corporation, Beijing 100083, China 4 School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China 3 Wuhan Branch of Beijing Zunguan Technology Co., Ltd., Wuhan 430079, China
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Snippet	With the aging of the human body and the reduction in its physiological capacities, falls have become a huge threat to individuals’ physical and mental health,... With the aging of the human body and the reduction in its physiological capacities, falls have become a huge threat to individuals' physical and mental health,...
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SubjectTerms	Accuracy Aged Aging Algorithms Analysis Behavior Classification Datasets Decision making fall detection feature dimensionality reduction Global positioning systems GPS Humans Machine learning Methods Monitoring, Ambulatory - methods Neural networks Older people Sensors Support vector machines Wearable computers XGBoost
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Title	Fall Detection Algorithm Based on Inertial Sensor and Hierarchical Decision
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