Human factors engineering (HFE) in the major of rehabilitation engineering (RE) is a distinctive course offered by the School of Health Science and Engineering at the University of Shanghai for Science and Technology (USST). This course builds on the traditional HFE curriculum by integrating usability engineering, HFE in medical devices, usability design standards, and HFE design standards for interactive systems. Innovative designs and explorations are applied to aspects such as course content, teaching methods, and assessment methods. Through practical implementation, significant improvements in teaching quality and students' comprehensive skills have been achieved. Furthermore, this approach contributes to the establishment of a mature talent cultivation mechanism with distinctive features in the field of RE.
This paper presents the research status of rehabilitation robot in detail. In order to understand the current situation of rehabilitation robots, they are divided into four categories, such as upper limb rehabilitation robots, lower limb rehabilitation robots, massage assistance robots and other rehabilitation robots. The research status and existing problems of rehabilitation robots at home and abroad in recent years are introduced in detail, and the functions of rehabilitation robots are demonstrated. Trained upper limb rehabilitation robots and lower limb rehabilitation robots have been introduced, which help patients undergo rehabilitation training and demonstrate strong recovery functions. Massage auxiliary robots provide continuous and stable massage therapy to the patient's specific muscle tissue, relieving the patient's pain. Thermal moxibustion robots and acupuncture robots provide rehabilitation treatment for patients in the way of traditional Chinese medicine. Finally, the common challenges in the research field of rehabilitation robots are discussed, and the future development trends of various rehabilitation robots are proposed.
In view of the problems of poor human-computer interaction and unstable movement process in the training process of lower limb exoskeleton rehabilitation robot, a lower limb continuous motion prediction model (FOX-LSTM) based on FOX algorithm (FOX) optimized long and short-term memory (LSTM) is proposed. Based on the surface electromyogram (sEMG), the comprehensive features can well represent the lower limb movement intention, complete signal pretreatment, feature extraction and dimension reduction. And by FOX algorithm for LSTM model optimization, the key parameters of the electromyographic signal characteristics match the joint trajectory, improve the joint Angle prediction accuracy. The experimental results prove that the proposed prediction model can predict the angle information of human lower limb joints well and improve the prediction accuracy.
Surface electromyography (sEMG) is often used as an ideal control source to drive an intelligent prosthetic hand. The study aims to distinguish 24 types of finger-wrist joint movements based on 8-channel sEMG signals and 6-channel inertial signals. Considering the dynamic characteristics of signal, we proposed a new method named segmented sliding window (SSW) for feature extraction. Compared with fixed window (FW) and sliding window (SW), the new method performed better. Then fisher score (FS) and sequential forward selection (SFS) were employed for feature selection. And a support vector machine (SVM) incorporating majority voting method was adopted as a classifier. The results from 14 subjects indicated that the combination of SSW and SFS got best performance, whose classification accuracy can achieve to 96.61% only base on sEMG signals within length of 300ms. Classification accuracy could be raised to 97.56 % with inertial signals added, which verified inertial signal is helpful for motion recognition.
The classification models generally have poor generality in the Motor Imagery Brain-Computer Interface systems (MI-BCIs). Besides, the long training process limits the practicality of the system. Thus, shortening the long training process and improving the generalization of the system's model are very important for developing BCI systems. Transfer learning (TL) is currently one of the most commonly used strategies in BCI to reduce the calibration time and improve the adaptability of cross-subject learning systems, which can eliminate the dependence on model reconstruction. To address the lack of adaptability of MI-BCI systems in cross-subject learning, we propose a TL algorithm that combines Riemannian alignment with Distance Preservation to Local Mean (RA-DPLM), which utilizes the Riemannian distance affine invariance, effectively combines Riemannian geometry and TL to align the distributions of data on Riemannian manifolds between the target and source subjects to reduce the training time for cross-subject experiments. Meanwhile, the proposed method combined with the dimensionality reduction algorithm further reduces the calibration time for cross-subject experiments. Experimental results have demonstrated the effectiveness of the proposed RA-DPLM algorithm, showing that its integration with dimensionality reduction techniques significantly improves the cross-subject classification accuracy for classical left- and right- hand motor MI tasks.
