Radar-Based Human Activity Acquisition, Classification and Recognition Towards Elderly Fall Prediction

Falls represent the main risk of injury for elderly people. One-third of adults aged over 65 and half of people over 80 will have at least one fall a year. People at risk should visit a clinical service to detect gait difficulties. Solutions for detecting daily activities are being studied more and more, aiming to develop a complementary method to early detect this type of health risk as effectively as possible. Non-intrusiveness in the person's life for this type of problem is an important criterion, which is why current research is focusing on solutions involving non-conventional imagery such as radar systems. This paper presents an embedded system for classifying daily activities based on the processing of micro-Doppler images. The implementation of the pre-processing chain with a filter enables the acquisition of detailed spectrograms, which proves to be effective in detecting walking. Additionally, by porting it onto the Jetson Orin, it could be possible to accelerate the inference phase of the classification model. We used the ResNet-18 classification method to classify six human activities: Walking, Sitting, Standing, Picking up objects, Drinking water, and Fall events. The results showed that the model is capable of recognising most of the activities on real data.