Behavior analysis through posture recognition is an essential research in robotic systems. Sitting with unhealthy sitting posture for a long time seriously harms human health and may even lead to lumbar disease, cervical disease and myopia. Automatic vision-based detection of unhealthy sitting posture, as an example of posture detection in robotic systems, has become a hot research topic. However, the existing methods only focus on extracting features of human themselves and lack understanding relevancies among objects in the scene, and henceforth fail to recognize some types of unhealthy sitting postures in complicated environments. To alleviate these problems, a scene recognition and semantic analysis approach to unhealthy sitting posture detection in screen-reading is proposed in this paper. The key skeletal points of human body are detected and tracked with a Microsoft Kinect sensor. Meanwhile, a deep learning method, Faster R-CNN, is used in the scene recognition of our method to accurately detect objects and extract relevant features. Then our method performs semantic analysis through Gaussian-Mixture behavioral clustering for scene understanding. The relevant features in the scene and the skeletal features extracted from human are fused into the semantic features to discriminate various types of sitting postures. Experimental results demonstrated that our method accurately and effectively detected various types of unhealthy sitting postures in screen-reading and avoided error detection in complicated environments. Compared with the existing methods, our proposed method detected more types of unhealthy sitting postures including those that the existing methods could not detect. Our method can be potentially applied and integrated as a medical assistance in robotic systems of health care and treatment.

With the dramatic expansion of large-scale videos, traditional centralized face recognition methods cannot meet the demands of time efficiency and expansibility, and thus distributed face recognition models were proposed. However, the number of tasks at the agent side is always dynamic, and unbalanced allocation will lead to time delay and a sharp increase of CPU utilization. To this end, a new distributed face recognition framework based on load balancing and dynamic prediction is proposed in this paper. The framework consists of a server and multiple agents. When performing face recognition, the server is used to recognize faces, and other operations are performed by the agents. Since the changes of the total number of videos and the number of pedestrians affect the task amount, we perform load balancing with an improved genetic algorithm. To ensure the accuracy of task allocation, we use extreme learning machine to predict the change of tasks. The server then performs task allocation based on the predicted results sent by the agents. The experimental results show that the proposed method can effectively solve the problem of unbalanced task allocation at the agent side, and meanwhile alleviate time delay and the sharp increase of CPU utilization.