| Summary: | With the rise in the elderly population, the importance of care services for the elderly is also increasing. Among the care services, sudden fall detection is one of the most important services that the elderly need. The hip joints are prone to damage when they fall, and most of such injuries can lead to very severe consequences. In recent times, researches on fall detection have been very active. Fall detection by attaching an acceleration sensor to the waist of a person is popular and the detection rate is very high. However, when the fall is detected from a sensor attached to the wrist, which is more convenient as compared to the waist attachment, the detection accuracy is lower. To overcome the problem, in this article, we propose a system that distinguishes falls from the acceleration sensor attached to the wrist using an artificial neural network–based deep learning method. With the proposed method, we could detect the falls with a 100% accuracy in an experiment.
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