Precise recognition of human action is a key enabler for the development of many applications, including autonomous robots for medical diagnosis and surveillance of elderly people in home environment. This paper addresses the human action recognition based on variation in body shape. Specifically, we divide the human body into five partitions that correspond to five partial occupancy areas. For each frame, we calculated area ratios and used them as input data for recognition stage. Here, we consider six classes of activities namely: walking, standing, bending, lying, squatting, and sitting. In this paper, we proposed an efficient human action recognition scheme, which takes advantages of the superior discrimination capacity of adaptive boosting algorithm. We validated the effectiveness of this approach by using experimental data from two publicly available databases fall detection databases from the University of Rzeszow’s and the Universidad de Malaga fall detection data sets. We provided comparisons of the proposed approach with the state-of-the-art classifiers based on the neural network, $K$ -nearest neighbor, support vector machine, and naive Bayes and showed that we achieve better results in discriminating human gestures.