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Functional electrical stimulation (FES), a rehabilitation technique, typically relies on physiotherapists using trial-and-error tests to determine effective stimulation patterns. Therefore, this study proposed a kind of pedal hill modeling to establish an optimal stimulus mode with the maximum torque efficiency optimization objective. This study also proposed a new model based on the particle swarm optimization (PSO) algorithm, the back propagation (BP) neural network algorithm, and the proportional integral derivative (PID) control composite algorithm. Six participants were recruited for the experiment. Using the proposed modeling method, we found an appropriate stimulation mode for each of the six subjects, and then each subject performed three sets of experiments for cycling without electrical stimulation, cycling with fixed pulse width, and cycling with adaptive adjustment of pulse width by the fabricated controller. The results of the study showed that root mean square error (RMSE), average (AVE), training time and number of stops all performed well compared to the no control and fixed control conditions and the adaptive pulse width control system of the fabricated controller allows subjects to train at a longer continuous running time and a more stable cycling training speed., Competing Interests: Declarations. Competing interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The corresponding author is responsible for submitting a competing interests statement on behalf of all authors of the paper. Informed consent statement: Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patients to publish this paper., (© 2025. The Author(s).)

Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capability and a U-Net model with a coding and decoding network structure. Three channels are used to extract vessel features separately, and finally, the segmentation results of the three channels are merged. The algorithm proposed in this paper has been verified and evaluated on the DRIVE, STARE, and CHASE_DB1 datasets. The experimental results show that the proposed algorithm can segment small blood vessels better than most other methods. We conclude that our algorithm has reached 0.8745, 0.8903, and 0.8916 on the three datasets in the sensitivity metric, respectively, which is nearly 0.1 higher than other existing methods., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2021 Yuliang Ma et al.)

This paper reports on a novel approach to using a 3-axis accelerometer to capture body segment angle for upper limb functional electrical stimulation (FES) control. The approach calculates the angle between the accelerometer x -axis and the gravity vector, while avoiding poor sensitivity at certain angles and minimizing errors when true acceleration is relatively large in comparison to gravity. This approach was incorporated into a state-machine controller which is used for the real-time control of FES during upper limb functional task performance. An experimental approach was used to validate the new method. Two participants with different upper limb impairments resulting from a stroke carried out four different FES-assisted tasks. Comparisons were made between angle calculated from arm-mounted accelerometer data using our algorithm and angle calculated from limb-mounted reflective marker data. After removal of coordinate misalignment error, mean error across tasks and subjects ranged between 1.4 and 2.9°. The approach shows promise for use in the control of upper limb FES and other human movement applications where true acceleration is relatively small in comparison with gravity. [ABSTRACT FROM AUTHOR]