Activities of daily living and falls recognition and classification from the wearable sensors data
Todor Ivaşcu, Kristijan Cincar, Adriana Diniş, Viorel Negru · 2017
The increasing percentage in population of the elderly and of the chronically diseased requires new solutions for tele-medicine and the continuous real-time remote health status monitoring. The present paper presents a comparison study of various machine learning and deep learning techniques for cross-person prediction of both activities of daily living and falling down. The experiments are performed on the smartphone's raw accelerometer data from the publicly available UniMiB SHAR dataset. Different cross-validation methods are tested and the performance of each classifier discussed. Deep learning method outperforms the other classifiers in many configurations, performed on the different subsets.