Machine Learning Based Early Fall Detection for Elderly People with Neurological Disorder Using Multimodal Data Fusion

Abstract

Fall is deemed to be one of the critical problems for the elderly patient having neurological disorders as it may cause injury or death. It turns to be a public health concern and attracts researchers to detect fall using sensing devices wearable, portable, and imaging. With the availability of low cost pervasive sensing elements, advancement of ubiquitous computing and better understanding of machine learning approaches, researchers have employing various machine learning approaches in detecting fall from the sensor data. In this paper, we have proposed a recurrent neural network (RNN)-based framework for detecting fall/daily activity of a patient having a neurological disorder using Internet of things and then manage the patient by referring to doctor. If an anomaly is detected in the daily activity and notify caregiver/family member if fall is detected. The RNN based fall detection model fused knowledge from both the smartphone/wearable and camera installed on the wall and ceiling. The proposed RNN is trained with open-labeled and UR data-sets and is compared with the support vector machine and random forest for these two data-sets. The performance evaluation shows the proposed method is effecting and outperforms its counterparts.