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Mental health and the management of chronic diseases are two further areas where it is effective. But it\u2019s still very difficult to objectively assess how well it trains. SVM and CNN are two examples of more conventional approaches to problems like gait identification and motion classification, respectively. LSTM and LSTM-RNN for sequential evaluation and capturing systems built around inertial sensor devices are also included. Furthermore, the results of clinical trial meta-analyses have been very instructive. But these methods still have limitations, such as being too reliant on hardware, not being very flexible, and not being interpretable. An architecture for a neural network that is enabled by the Internet of Things is displayed in this paper. Its intended use is to provide quick, scalable, and interpretable feedback on the efficacy of training. Internet of Things (IoT) wearables capture data. This information is also gathered from standard datasets like the Stroke Rehabilitation Qigong Dataset and the Tai Chi IMU Movement Dataset. The signal is improved during preprocessing by using adaptive filtering. To further isolate important characteristics, it employs principal component analysis (PCA). An Attention-enabled Spatial-Temporal Graph Convolutional Network is used for joint-specific skeletal sequence evaluation in the modeling process. A CNN-Transformer hybrid is employed to record the changing behavior of multimodal sensors. Federated Averaging, also known as FedAvg, is implemented across dispersed participants to ensure education that is both decentralized and respects individuals\u2019 privacy. Making the ST-GCN-Att interpretable is done by using Gradient-weighted Class Activation Mapping (Grad-CAM). The result is the ability to see the temporal intervals and crucial skeleton joints that impact the forecasts. Measures for precision, F1-score, delay, and energy usage are used to verify the level of performance. This system integrates deep spatio-temporal learning with IoT sensing, making it superior to existing techniques. It unifies federated privacy and feedback that is easy to understand into one process. We have created a system that is both scalable and clinically reliable. It improves exercise-based therapeutic training in terms of individualization, openness, and real-time assessment of health.<\/jats:p>","DOI":"10.1007\/s43926-026-00304-y","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T14:46:46Z","timestamp":1772549206000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Monitoring and health evaluation of fitness Qigong training effect based on internet of things and neural network"],"prefix":"10.1007","volume":"6","author":[{"given":"Chunhua","family":"He","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,3]]},"reference":[{"key":"304_CR1","doi-asserted-by":"publisher","first-page":"100016","DOI":"10.1016\/j.bbii.2023.100016","volume":"3","author":"J Liu","year":"2023","unstructured":"Liu J, Shi H, Lee TMC. 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