Wearable Fall Detection for Elderly with Simple Belt Design Using Android Application

Kusumah, Ilman Himawan; Mauludi, Paiz Ilham; Junfithrana, Anggy Pradiftha; Edwinanto,

doi:10.1109/icced53389.2021.9664840

Cited by 3 publications

(1 citation statement)

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“…Most data collectors choose to place the sensor at the waist or wrist, with a few at the thigh or ankle [ 24 ]. Kusumah [ 25 ] implemented a threshold-based fall detection algorithm using accelerometer and gyroscope sensor signals. However, the actual fall process is more complex.…”

Section: Related Workmentioning

confidence: 99%

Patch-Transformer Network: A Wearable-Sensor-Based Fall Detection Method

Wang

2023

Sensors

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Falls can easily cause major harm to the health of the elderly, and timely detection can avoid further injuries. To detect the occurrence of falls in time, we propose a new method called Patch-Transformer Network (PTN) wearable-sensor-based fall detection algorithm. The neural network includes a convolution layer, a Transformer encoding layer, and a linear classification layer. The convolution layer is used to extract local features and project them into feature matrices. After adding positional coding information, the global features of falls are learned through the multi-head self-attention mechanism in the Transformer encoding layer. Global average pooling (GAP) is used to strengthen the correlation between features and categories. The final classification results are provided by the linear layer. The accuracy of the model obtained on the public available datasets SisFall and UnMib SHAR is 99.86% and 99.14%, respectively. The network model has fewer parameters and lower complexity, with detection times of 0.004 s and 0.001 s on the two datasets. Therefore, our proposed method can timely and accurately detect the occurrence of falls, which is important for protecting the lives of the elderly.

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Section: Related Workmentioning

confidence: 99%