Grasping force estimation using state-space model and Kalman Filter

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches

Upper limb functional impairments persisting after stroke significantly affect patients' quality of life. Precise adjustment of robotic assistance levels based on patients' motion intentions using sEMG signals is crucial for active rehabilitation. This paper systematically reviews studies on continuous prediction of upper limb single joints and multi-joint combinations motion intention using Model-Based (MB) and Model-Free (MF) approaches over the past decade, based on 186 relevant studies screened from six major electronic databases. The findings indicate ongoing challenges in terms of subject composition, algorithm robustness and generalization, and algorithm feasibility for practical applications. Moreover, it suggests integrating the strengths of both MB and MF approaches to improve existing algorithms. Therefore, future research should further explore personalized MB-MF combination methods incorporating deep learning, attention mechanisms, muscle synergy features, motor unit features, and closed-loop feedback to achieve precise, real-time, and long-duration prediction of multi-joint complex movements, while further refining the transfer learning strategy for rapid algorithm deployment across days and subjects. Overall, this review summarizes the current research status, significant findings, and challenges, aiming to inspire future research on predicting upper limb motion intentions based on sEMG.

Temperature Control Unit—Modeling and Implementation of a Particle Filter on a Microcontroller

The paper discusses the possibilities of using particle filter estimation algorithms in embedded systems. For this purpose, the dedicated testing platform was built, which allowed for the determination of the estimation quality of a particle filter on a real system, and the microcontroller performance in that scenario. Tests were performed using the obsolete and not very efficient, although energy-saving, STM32F4 Discovery board—it has allowed for an in-depth analysis, and the results can be easily improved by switching to a modern platform. The quality of operations in open- and closed-loop systems was investigated based on the analysis of time simulations conducted for various mathematical models. These analyses made it possible to establish a correlation between the number of particles and the required calculation power. They have shown that it is possible to successfully implement and run a particle filter algorithm on an older and computationally limited device, as well as in real-time scenarios.