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Fitness yoga is now a popular form of national fitness and sportive physical therapy. At present, Microsoft Kinect, a depth sensor, and other applications are widely used to monitor and guide yoga performance, but they are inconvenient to use and still a little expensive. To solve these problems, we propose spatial–temporal self-attention enhanced graph convolutional networks (STSAE-GCNs) that can analyze RGB yoga video data captured by cameras or smartphones. In the STSAE-GCN, we build a spatial–temporal self-attention module (STSAM), which can effectively enhance the spatial–temporal expression ability of the model and improve the performance of the proposed model. The STSAM has the characteristics of plug-and-play so that it can be applied in other skeleton-based action recognition methods and improve their performance. To prove the effectiveness of the proposed model in recognizing fitness yoga actions, we collected 960 fitness yoga action video clips in 10 action classes and built the dataset Yoga10. The recognition accuracy of the model on Yoga10 achieves 93.83%, outperforming the state-of-the-art methods, which proves that this model can better recognize fitness yoga actions and help students learn fitness yoga independently.

e13591 Background: Electronic Health Records (EHR) provide the possibility of leveraging modern machine learning algorithms for disease detection prediction. Since a patient’s medical journey contains a complex pattern and it is difficult to identify interactive feature impacts, we propose to reconstruct patients’ attributes at different time points into dynamic graphs. Then we can use graph-based learning algorithms to enhance the predictive precision and identify leading feature interactions that reveal key services transitions. Methods: Sequential EHR like diagnosis (Dx), procedure (Px) and prescription (Rx) can be transformed into dynamic temporal graphs with nodes representing attributes, services, and medical status and edges representing patient-object interactions. Also, graph based sequential recommendation can use structured historical behavior trajectories as input, aiming to predict patient’s next behavior. Then, the model can investigate patients’ time-sensitive features interactively identifying a potential disease and then output a probability of getting that disease. Dynamic graphs can also help to track patient’s medical treatment/drug use service transitions. Results: We applied our method on a Newly Diagnosed Multiple Myeloma (NDMM) dataset consisting of 1.5 million candidates’ longitudinal medical claims from 2017 to 2020 with diagnosis rate around 0.6%. In this data set, patients’159 aggregated Dx, Rx and Px features, together with 30 demographic attributes are updated monthly. The model was trained on 4 monthly data cohorts from the 12/2017 to 03/2019 period and its performance was tested on 9 monthly cohorts from year 2020. The 1000 patients scored as having highest risk were evaluated for the predictive precision which was around 6.5% This precision compares most favorably relative to the reported 0.76% lifetime risk of getting multiple myeloma in US. We also captured the top, service transitions in NDMM patients having 30% more frequency than those in overall populations. Conclusions: The novel dynamic graph-based learning approach on a large-scale EHR dataset demonstrates the advancement of the disease detection methodology. While the outcome of this model significantly improves the accuracy of predictive precision, it also helps identify more meaningful key drivers attributing to disease. The incremental new patients identified through this novel model will provide a better basis for the evolving identification solution within Biopharma industry.

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