2D LIDAR-Based Approach for Activity Identification and Fall Detection

Activity detection is a key task in the monitoring of elderly people living alone. This is because it helps locate them and identify any accident that might occur to them. In this paper, we propose a novel approach that uses 2D LIght Detection and Ranging (LIDAR) and Deep Learning to perform activity detection. In a first step, our approach processes and interpolates the data collected using the 2D LIDAR following an algorithm we propose to locate the person and identify the useful data points. In the next steps, the data are transformed into 2 types of representations: a time series type and an image type. The time series data are used to train different Long Short-Term Memory (LSTM) networks to identify the person and to recognize his/her activity, while the image type is used to fine-tune a Convolutional Neural Network (CNN) for fall detection. Throughout our experiments, we show that our approach allows for the identification of people from their gait, and the detection of unsteady gait or unstable walk (i.e., when the person is about to fall or feeling dizzy) as well as the detection of up-to 4 activities: walking, standing, sitting, and falling. The results obtained from our experiment show that the proposed method reaches an accuracy equal to 94.1% for multi-class activity detection, 98.6% for fall detection, 93.2% for person identification (for 3 different people), and 92.5% for unsteady walk detection.