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- W4313444130 abstract "This research work has developed the methodology to detect the fall of a person during walking, running, and standing. The fall recovery capability & fall diagnosis information can be utilized to avoid the fall and to provide assistant after fall detection of elderly subjects and assess the various health metrics. A total of 20 subjects of [30 ±10] age group were considered for data collection. The fall diagnosis will help in designing an Internet of health things-based (IoHT) alert device or fall assistant device like airbag opening to support elderly subjects. All four directions were considered to observe the fall. It is observed that the vertical acceleration, angular velocity is helping us to detect fall and no-fall boundary. The separate boundary of fall & no-fall is calculated and presented for all three activities stand, walk, and run. Several deep learning models including the hybrid model is considered, but the long short-term memory (LSTM) model has provided the highest accuracy of 92.98% & an F1-score of 90.48% for fall detection. The allowable range of acceleration and angular velocity is provided in the form of 3D plots." @default.
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- W4313444130 date "2023-01-01" @default.
- W4313444130 modified "2023-10-17" @default.
- W4313444130 title "Tracking of Fall Detection Using IMU Sensor: An IoHT Application" @default.
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- W4313444130 doi "https://doi.org/10.1007/978-981-19-5868-7_61" @default.
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