An Intelligent and Efficient Rehabilitation Status Evaluation Method: A Case Study on Stroke Patients

Chronic patients' care encounters challenges, including high cost, lack of professionals, and insufficient rehabilitation state evaluation. Computer-supported cooperative work (CSCW), is capable of alleviating these issues, as it allows healthcare physicians (HCP) to quantify the workload and thus to enhance rehabilitation care quality. This study aims to design a deep learning algorithm Pose-AMGRU, a deep learning-based pose recognition algorithm combining Pose-Attention Mechanism and Gated Recurrent Unit (GRU), to monitor the human pose of rehabilitating patients efficiently. It gives instructions for HCP. To further substantiate the acceptance of our computer-supported method, we develop a multi-fusion theoretical model to determine factors that may influence the acceptance of HCP and verify the usefulness of the method above. Experiment results show Pose-AMGRU achieves an accuracy of 98.61% in the KTH dataset and 100% in the rehabilitation action dataset, which outperforms other algorithms. The efficiency running speed of Pose-AMGRU on the GTX1060 graphics card is up to 14.75FPS, which adapts to the home rehabilitation scene. As to acceptance evaluation, we verified the positive relationship between the computer-supported method and acceptance, and our model presents decent generalizability of stroke patients' care at the Second Affiliated Hospital of Zhengzhou University.