[A review on multi-modal human motion representation recognition and its application in orthopedic rehabilitation training].

Human motion recognition (HAR) is the technological base of intelligent medical treatment, sports training, video monitoring and many other fields, and it has been widely concerned by all walks of life. This paper summarized the progress and significance of HAR research, which includes two processes: action capture and action classification based on deep learning. Firstly, the paper introduced in detail three mainstream methods of action capture: video-based, depth camera-based and inertial sensor-based. The commonly used action data sets were also listed. Secondly, the realization of HAR based on deep learning was described in two aspects, including automatic feature extraction and multi-modal feature fusion. The realization of training monitoring and simulative training with HAR in orthopedic rehabilitation training was also introduced. Finally, it discussed precise motion capture and multi-modal feature fusion of HAR, as well as the key points and difficulties of HAR application in orthopedic rehabilitation training. This article summarized the above contents to quickly guide researchers to understand the current status of HAR research and its application in orthopedic rehabilitation training.

A Comprehensive Survey of Vision-Based Human Action Recognition Methods.

Although widely used in many applications, accurate and efficient human action recognition remains a challenging area of research in the field of computer vision. Most recent surveys have focused on narrow problems such as human action recognition methods using depth data, 3D-skeleton data, still image data, spatiotemporal interest point-based methods, and human walking motion recognition. However, there has been no systematic survey of human action recognition. To this end, we present a thorough review of human action recognition methods and provide a comprehensive overview of recent approaches in human action recognition research, including progress in hand-designed action features in RGB and depth data, current deep learning-based action feature representation methods, advances in human⁻object interaction recognition methods, and the current prominent research topic of action detection methods. Finally, we present several analysis recommendations for researchers. This survey paper provides an essential reference for those interested in further research on human action recognition.

Action representations activated by task-irrelevant information: is it really irrelevant?

Accessing action knowledge is believed to rely on the activation of action representations through the retrieval of functional, manipulative, and spatial information associated with objects. However, it remains unclear whether action representations can be activated in this way when the object information is irrelevant to the current judgment. The present study investigated this question by independently manipulating the correctness of three types of action-related information: the functional relation between the two objects, the grip applied to the objects, and the orientation of the objects. In each of three tasks in Experiment 1, participants evaluated the correctness of only one of the three information types (function, grip or orientation). Similar results were achieved with all three tasks: "correct" judgments were facilitated when the other dimensions were correct; however, "incorrect" judgments were facilitated when the other two dimensions were both correct and also when they were both incorrect. In Experiment 2, when participants attended to an action-irrelevant feature (object color), there was no interaction between function, grip, and orientation. These results clearly indicate that action representations can be activated by retrieval of functional, manipulative, and spatial knowledge about objects, even though this is task-irrelevant information.