AI-Driven Health Monitoring: Integrating Transformer and Convolutional Fusion for Stroke Patient Posture Estimation

Rehabilitative exercises are crucial for the motor recovery of stroke patients. Traditional physical therapy involves various techniques and exercise therapies aimed at helping patients regain function and enhance their quality of life. However, effective treatment through this method requires substantial human resources and can be costly. It also tends to be subjective and lacks real-time responsiveness. With the rapid development of AI-generated content technology, to improve the precision and real-time performance of rehabilitative motion posture recognition and to better assist patients in conducting extended rehabilitation exercises independently at home, this paper proposes an AI-driven network architecture for posture estimation in stroke patient rehabilitation that can generate corresponding posture skeleton content by identifying the patient's posture. This generated content feedback can assist in monitoring and treating stroke patients' rehabilitation. The architecture integrates spatial convolutional layers with an improved transformer module, termed ConvTrans (Integration of Convolution and Transformer). In the ConvTrans block, the use of lightweight multi-head self-attention (LMHSA) and inverted residual forward networks (IRFFN) effectively reduces computational costs and enhances processing efficiency. This configuration captures both information pertaining to local and global structure, thereby enhancing the network's representational capabilities. Demonstrating strong performance on three challenging human pose estimation (HPE) datasets, this method offers efficient feedback on posture to aid in the rehabilitation of individuals affected by strokes.