Autonomous Fall Detection with Wearable Cameras by Using Relative Entropy Distance Measure

Timely, precise, and reliable detection of fall events is very important for systems monitoring activities of elderly people, especially the ones living independently. In this paper, we propose an autonomous fall detection system by taking a completely different view compared with existing vision-based activity monitoring systems and applying a reverse approach. In our system, in contrast with static sensors installed at fixed locations, the camera is worn by the subject, and thus, monitoring is not limited only to areas where the sensors are located and extends to wherever the subject may travel. Moreover, the camera provides a richer set of data and helps lower the false positive rates compared with accelerometer-only systems. We employ a modified version of the histograms of oriented gradients (HOG) approach together with the gradient local binary patterns (GLBP). It is shown that, with the same training set, the GLBP feature is more descriptive and discriminative than HOG, histograms of template, and semantic local binary patterns. Moreover, we autonomously compute a threshold, for the detection of fall events, from the training data based on relative entropy, which is a member of Ali-Silvey distance measures. Experiments are performed with ten different people and a total of around 300 associated fall events indoors and outdoors. Experimental results show that, with the autonomously computed threshold, the proposed method provides 93.78% and 89.8% accuracy for detecting falls with indoor and outdoor experiments, respectively.