Wearable Fall-Detection Using Deep Embedded Clustering Algorithm

Falls in elderly people are common. Detecting near-fall situations can prevent fall-related injuries. Wearable technology has made a significant impact in this direction and fall-detection from wearable sensors has become an important research problem in ambient assisted living. Although a number of machine learning algorithms exist for wearable fall-detection, most of them are based on supervised learning. These algorithms require a huge amount of training data and generating such data is very time-consuming process. This paper employs deep embedded clustering, an unsupervised learning approach, for wearable fall-detection. For experimental purpose, Kaggle fall-detection dataset is considered. Results indicate that deep embedded clustering achieves higher accuracy in attaining fall-detection.