Video surveillance for near-fall detection at home

This paper presents the feasibility of a low-cost video surveillance system that can be used at home to assess the fall risk of older adults. This is of paramount importance since fall is the greatest hazard for older adults. To detect early signs of mobility decline in older adults the system simply detects near-falls with machine learning as part of a fall prevention plan. A One-Class SVM was trained to combine spatiotemporal features from normal activities of daily living. The spatiotemporal features were extracted from a simplified skeleton fitted to the body based on a keypoint RCNN algorithm. Then the system was used to estimate normality scores to identify abnormal events. In practice, a near-fall will trigger a notification to document the fall risk probability. Our experimental results demonstrated that the One-Class SVM could successfully distinguish anomalies (near-falls) with a detection accuracy of 90%, specificity of 87.67% and sensitivity of 93.33% on a dataset of 55 videos (> 1 6000 frames) of simulated normal and abnormal activities in a realistic apartment-laboratory.