Abstract
This paper presents an adaptive learning rate of recursive sinusoidal function neural network (ALR-RSFNN) with Lyapunov stability for identification elders fall signal. The older human signal analysis has been a research topic in health care fields that algorithms are implemented in wearable device real time to detect fall situation. However, the code size of the microcontroller in wearable device is limited, and the neural network learning rate choice is important which influencs neural network convergence performance. The recursive sinusoidal function neural network uses sine wave modulation input function to reduce train times in traditional Gaussian function vertex and width. Moreover, we utilize adaptive learning rate to guarantee network stability. In the experimental results, the ALR-RSFNN identify human fall signal accurately and reliably. In addition, we use wearable device combined BLE (Bluetooth low energy) to feedback output response real time.
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Acknowledgment
This work was supported in part by the “Intelligent Recognition Industry Service Center” of Higher Education Sprout Project, Ministry of Education, Taiwan.
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Chu, CT., Ho, CC. (2019). A Lyapunov Stability Based Adaptive Learning Rate of Recursive Sinusoidal Function Neural Network for Identification of Elders Fall Signal. In: Chang, CY., Lin, CC., Lin, HH. (eds) New Trends in Computer Technologies and Applications. ICS 2018. Communications in Computer and Information Science, vol 1013. Springer, Singapore. https://doi.org/10.1007/978-981-13-9190-3_52
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DOI: https://doi.org/10.1007/978-981-13-9190-3_52
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