Early rehabilitation with the right intensity contributes to the physical recovery of stroke survivors. In clinical practice, physicians determine whether the training intensity is suitable for rehabilitation based on patients’ narratives, training scores, and evaluation scales, which puts tremendous pressure on medical resources. In this study, a lightweight facial expression recognition algorithm is proposed to diagnose stroke patients’ training motivations automatically. First, the properties of convolution are introduced into the Vision Transformer’s structure, allowing the model to extract both local and global features of facial expressions. Second, the pyramid-shaped feature output mode in Convolutional Neural Networks is also introduced to reduce the model’s parameters and calculation costs significantly. Moreover, a classifier that can better classify facial expressions of stroke patients is designed to improve performance further. We verified the proposed algorithm on the Real-world Affective Faces Database (RAF-DB), the Face Expression Recognition Plus Dataset (FER+), and a private dataset for stroke patients. Experiments show that the backbone network of the proposed algorithm achieves better performance than Pyramid Vision Transformer (PvT) and Convolutional Vision Transformer (CvT) with fewer parameters and Floating-point Operations Per Second (FLOPs). In addition, the algorithm reaches an 89.44% accuracy on the RAF-DB dataset, which is higher than other recent studies. In particular, it obtains an accuracy of 99.81% on the private dataset, with only 4.10M parameters.

Multi-task learning (MTL) is a learning paradigm which can improve generalization performance by transferring knowledge among multiple tasks. Traditional collaborative filtering recommendation methods suffer from cold start, sparsity and scalability problems. The latest research has shown that applying side information of knowledge graph can not only solve the problems above, but also improve the accuracy of recommendation. However, existing multi-task methods for knowledge graph enhanced recommendation expose obvious issues of disclosing the private information of training samples. In order to solve these problems, we put forward a privacy-preserving multi-task framework for knowledge graph enhanced recommendation. In specific, Laplacian noise is added into the recommendation module to guarantee the privacy of sensitive data and knowledge graph is utilized to improve the accuracy of recommendation. Extensive experimental results on three datasets demonstrate that the proposed method can not only preserve the privacy of sensitive training data, but also have little effect on the prediction accuracy of the model.

Stroke patients often experience a substantial deterioration in physical fitness and are susceptible to fatigue during rehabilitation training due to damage inflicted on their central nervous system. In clinical practice, the implementation of computer vision algorithms for intelligent recognition of fatigue states in stroke patients during rehabilitation training can offer timely reminders and interventions based on the level of fatigue occurrence. This approach can effectively alleviate the substantial burden on medical personnel who would otherwise need to continuously monitor and document patients in real-time. A fatigue action recognition algorithm that combines 3D-CNN and Transformer is proposed. To effectively highlight the key areas related to fatigue action and suppress other visual noise in the natural shooting frame, a feature segmentation structure with 3D window and a window merging mechanism with self-training 3D bell weight are designed. By processing the mean of features in the time dimension, the representation coefficient of the action relationship contained in the sequence is obtained, enhancing the discernability between adjacent similar frames. Based on the fatigue driving dataset published by Tsinghua University in Taiwan and the private fatigue action dataset based on stroke patients, the proposed method has achieved a Top-1 accuracy of 75.35% and 70.83%, respectively. These results prove the feasibility and effectiveness of the 3D CNN-driven Transformer architecture.