Abnormal lower limb posture recognition based on spatial gait feature dynamic threshold detection

Lower limb rehabilitation training often involves the use of assistive standing devices. However, elderly individuals frequently experience reduced exercise effectiveness or suffer muscle injuries when utilizing these devices. The ability to recognize abnormal lower limb postures can significantly enhance training efficiency and minimize the risk of injury. To address this, we propose a model based on dynamic threshold detection of spatial gait features to identify such abnormal postures. A human-assisted standing rehabilitation device platform was developed to build a lower limb gait depth dataset. RGB data is employed for keypoint detection, enabling the establishment of a 3D lower limb posture recognition model that extracts gait, time, spatial features, and keypoints. The predicted joint angles, stride length, and step frequency demonstrate errors of 4 %, 8 %, and 1.3 %, respectively, with an average confidence of 0.95 for 3D key points. We employed the WOA-BP neural network to develop a dynamic threshold algorithm based on gait features and propose a model for recognizing abnormal postures. Compared to other models, our model achieves a 96 % accuracy rate in recognizing abnormal postures, with a recall rate of 83 % and an F1 score of 90 %. ROC curve analysis and AUC values reveal that the WOA-BP algorithm performs farthest from the pure chance line, with the highest AUC value of 0.89, indicating its superior performance over other models. Experimental results demonstrate that this model possesses a strong capability in recognizing abnormal lower limb postures, encouraging patients to correct these postures, thereby reducing muscle injuries and improving exercise effectiveness.