Multidimensional Feature-Guided Cross-Population Human Activity Recognition and Prediction.

With the rapid development of wearable devices and intelligent sensing technologies, the demand for human behavior recognition in rehabilitation medicine and human-machine collaboration has been increasing. To address the issue of high variability in gait features caused by individual differences in cross-population gait analysis, and to tackle the insufficient generalization ability of models due to the coupling of pathological features with normal gait, we propose a multi-dimensional spatiotemporal feature-guided SG-LSTM framework, based on a dual-branch architecture comprising symmetric LSTM (S-LSTM) and grouped LSTM (G-LSTM) networks, for cross-population lower-limb activity recognition and prediction. On the one hand, the S-LSTM module with a symmetric input structure is used to explicitly model the spatiotemporal symmetry of lower-limb joints in normal gait. On the other hand, the G-LSTM module with a joint functional grouping strategy and local motion decoupling is employed to explicitly model the abnormal motion coupling of lower-limb joints in pathological gait. Furthermore, a dynamically weighted multi-task loss function is designed to jointly optimize gait trajectory prediction and classification tasks, allowing the framework to simultaneously produce both outputs and enhance the adaptability of the model. Extensive experiments on our self-constructed gait dataset as well as the HuGaDB and WearGait-PD datasets demonstrate that the proposed method not only outperforms several existing approaches in cross-population human behavior prediction and gait recognition, but also holds potential clinical application value, achieving state-of-the-art (SOTA) performance.