Real-time monitoring of elderly people through computer vision

In recent years, many countries including Japan are facing the problems of increasing old-age population and shortage of labor. This has increased the demands of automating several tasks using robots and artificial intelligence in agriculture, production, and healthcare sectors. With increasing old-age population, an increasing number of people are expected to be admitted in old-age home and rehabilitation centers in the coming years where they receive proper care and attention. In such a scenario, it can be foreseen that it will be increasingly difficult to accurately monitor each patient. This requires an automation of patient's activity detection. To this end, this paper proposes to use computer vision for automatic detection of patient's behavior. The proposed work first detects the pose of the patient through a Convolution Neural Network. Next, the coordinates of the different body parts are detected. These coordinates are input in the decision generation layer which uses the relationship between the coordinates to predict the person's actions. This paper focuses on the detection of important activities like: sudden fall, sitting, eating, sleeping, exercise, and computer usage. Although previous works in behavior detection focused only on detecting a particular activity, the proposed work can detect multiple activities in real-time. We verify the proposed system thorough experiments in real environment with actual sensors. The experimental results shows that the proposed system can accurately detect the activities of the patient in the room. Critical scenarios like sudden fall are detected and an alarm is raised for immediate support. Moreover, the the privacy of the patient is preserved though an ID based method in which only the detected activities are chronologically stored in the database.