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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Vydáno v:Sensors (Basel, Switzerland) Ročník 23; číslo 1; s. 107
Hlavní autoři: Zheng, Liang, Zhao, Jie, Dong, Fangjie, Huang, Zhiyong, Zhong, Daidi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 22.12.2022
MDPI
Témata:
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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Shrnutí: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23010107