Abstract
Falls of older adults is a significant concern for themselves and caregivers as most of the times a fall leads to serious physical injuries. In the age of the Internet of things (IoT), connected smart homes and monitoring services have opened up opportunities for quality of life for the older adults. Detecting falls with wearable IoT devices can provide peace of mind for older adults and caregivers. Accelerometer based fall detection is investigated in this paper. Feed Forward Neural Network (FFNN) and Long Short Term Memory (LSTM) based Deep Learning network is applied to detect fall. LSTM network provides good accuracy based on the experiment. This experiment provides a promising indication that IoT-based fall monitoring can assure post-fall procedures to older adults and caregivers and this can increase the safety level and well-being of the older adults.
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Ahamed, F., Shahrestani, S., Cheung, H. (2020). Intelligent Fall Detection with Wearable IoT. In: Barolli, L., Hussain, F., Ikeda, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2019. Advances in Intelligent Systems and Computing, vol 993. Springer, Cham. https://doi.org/10.1007/978-3-030-22354-0_35
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DOI: https://doi.org/10.1007/978-3-030-22354-0_35
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