Convolutional neural network for wearable fall detection systems

Main Article Content

Uttapon Khawnuan
Teppakorn Sittiwanchai
Nantakrit Yodpijit

Abstract

Fall accidents are a common cause of critical injuries among older adults. Therefore, fall detection systems have garnered considerable attention in research and industry. Feature extraction is the key for detecting falls, but it is time-consuming and tedious process. Deep learning can autonomously extract features from raw sensor data. In this study, we proposed a fall detection algorithm for wearable devices using a convolutional neural network (CNN) to differentiate falls from activities of daily living. The proposed model achieved over 99% metrics (sensitivity, specificity, precision, accuracy, and F1 score) by evaluating two different public datasets and provided better classification performance compared to other fall detection models in the same dataset. The higher CNN performance was recognized without requiring complex data preparation and manual feature extraction. Results from this study could induce CNN through the application of classification problems in technological environments.

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How to Cite
Khawnuan, U., Sittiwanchai, T., & Yodpijit, N. (2023). Convolutional neural network for wearable fall detection systems. Science, Engineering and Health Studies, 17, 23040002. https://doi.org/10.69598/sehs.17.23040002
Section
Engineering

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