Lower extremity motor function assessment and rehabilitation model by incorporating convolutional neural networks

Lower limb motor function assessment and rehabilitation are of great significance in the fields of sports medicine, rehabilitation therapy and intelligent medicine. Traditional assessment methods rely on manual observation and motion capture systems, which have problems such as high cost, subjectivity, and lack of real-time performance. In this paper, we propose a lower limb motor function assessment and rehabilitation model incorporating convolutional neural network (CNN), which automatically extracts key motor features with deep learning technology to achieve accurate assessment and personalized rehabilitation program generation. The model establishes an efficient assessment framework by combining gait analysis, joint angle calculation and muscle activity feature extraction with multimodal motion data input. We design personalized rehabilitation training strategies based on the assessment results and optimize the rehabilitation process using feedback mechanisms. Validation on public datasets and experimental data shows that the model outperforms traditional methods in terms of both assessment accuracy and rehabilitation effect. The results suggest that the CNN-based lower limb motor function assessment and rehabilitation model can provide intelligent and precise auxiliary diagnosis and rehabilitation guidance for patients with motor dysfunction.