In the areas of robotics, sports science, virtual reality and healthcare, there has been much interest placed on human motion generation and analysis. With precise human motion modelling, HCI may enable the computer systems to comprehend, categorize, and predict these actions. Deep learning and optical motion capture technologies have opened a window to the extraction of rich spatial-temporal features from human actions for improvement in realistic motion generation and real-time analysis. A common problem faced by modern motion analysis systems is to account for the complexity in joint relations and to maintain temporal continuity, resulting in jerky and unrealistic motions during output. Additionally, some of the models are not very generalizable, particularly because they are sensitive to speed and style changes. Most conventional techniques of anomaly detection rely on manually derived thresholds, limiting their adaptability in dynamic real-world situations. This research aims to combine optical motion capture data with Spatial-Temporal Graph Convolutional Networks (ST-GCN) to overcome these limitations and establish a unified framework for motion generation and analysis. The aim is to provide reliable, scalable, realistic motion synthesis, anomaly detection, and accurate motion recognition solutions. The work is also geared towards improving model accuracy and adaptability through an efficient preprocessing and data augmentation technique. The ST-GCN spatiotemporal method for human motion modelling uses graph representations (nodes and edges) of joint movements, while the data preprocessing stages are cleaning, skeleton normalization, and data augmentation through mirroring and time scaling. The subsequent step is to build a model using a graph-based deep learning technique for motion generation, anomaly detection, and action recognition tasks. The ST-GCN model showed a high level of realism, with KL divergence values of 0.35 for "Jump" motion synthesis, while 98% action recognition accuracy was achieved for tasks like "Run" and "Punch." Outlier anomalies detection through a threshold of 0.7 proved to be sufficient to 'catch' any such instances, showcasing the capabilities of the model in both analysis and generation. These results bear testimony to the effectiveness of the framework towards resolving contemporary constraining issues in motion analysis and generation.
Liu et al. (Fri,) studied this question.