Rehabilitation devices based on soft robotics and bio-mechatronic sensors
Funded by University of Rijeka
Stroke is one of the world’s increasing problems, often leaving a person permanently disabled and unable to perform activities of daily living. Robotic devices are increasingly being imposed as a solution for timely and optimal rehabilitation of such patients, which, along with advanced control and measuring technologies, enable adaptive therapy and monitoring of parameters in real-time. Devices that are safe and comfortable to wear can be developed by combining the benefits of traditional and soft robotics with appropriate sensor technologies. Those systems can also enable the measurement of muscle activation as an input for adapting the amount of the generated force by an actuator. Such actuators should assure the necessary force in the rehabilitation process so the patients can perform specific movements. The proposed research represents an important segment of this chain, and contains two important directions. The first direction refers to the research and analysis of materials for developing soft robots using numerical methods based on the finite element method. Such research should result in the selection of materials with suitable mechanical properties in order to achieve the necessary functionality and durability of the component. Another important direction is the continuation of the development of a module for detecting the activation of muscle fibers of the human forearm, using machine learning and artificial intelligence methods. Namely, within the framework of the UNIRI-INOVA project whose outcomes were highly rated, significant results were accomplished and the necessary module for electromyographic (EMG) sensor signal processing was developed to achieve a high correlation with the dynamometer signal. One of the aims of this project proposal, is an extension of the functionality that allows real-time modeling and prediction of imposed force, in a very short period of time using only the EMG signal. Such a module, which would be tested experimentally among the members of the research team, should in future be a key component in the bio-mechanical interaction between a person and a rehabilitation robot. For such comprehensive research, the collaborators on the project have already applied for a competitive EU funded project.
Loss of hand function due to conditions like stroke or multiple sclerosis significantly impacts daily activities. Robotic rehabilitation provides tools to restore hand function, while novel methods based on surface electromyography (sEMG) enable the adaptation of the device’s force output according to the user’s condition, thereby improving rehabilitation outcomes. This study aims to achieve accurate force estimations during medium wrap grasps using a single sEMG sensor pair, thereby addressing the challenge of escalating sensor requirements for precise predictions. We conducted sEMG measurements on 13 subjects at two forearm positions, validating results with a hand dynamometer. We established flexible signal-processing steps, yielding high peak cross-correlations between the processed sEMG signal (representing meaningful muscle activity) and grip force. Influential parameters were subsequently identified through sensitivity analysis. Leveraging a novel data-driven Koopman operator theory-based approach and problem-specific data lifting techniques, we devised a methodology for the estimation and short-term prediction of grip force from processed sEMG signals. A weighted mean absolute percentage error (wMAPE) of approx. 5.5% was achieved for the estimated grip force, whereas predictions with a 0.5-second prediction horizon resulted in a wMAPE of approx. 17.9%. The methodology proved robust regarding precise electrode positioning, as the effect of sensing position on error metrics was non-significant. The algorithm executes exceptionally fast, processing, estimating, and predicting a 0.5-second sEMG signal batch in just approx. 30 ms, facilitating real-time implementation.
@article{bazina2024koopman,title={Koopman-driven grip force prediction through EMG sensing},author={Bazina, Tomislav and Kamenar, Ervin and Fonoberova, Maria and Mezi{\'c}, Igor},journal={arXiv preprint arXiv:2409.17340},year={2024},}
Robotics
Reducing Hand Kinematics by Introducing Grasp-Oriented Intra-Finger Dependencies
Tomislav Bazina, Goran Mauša, Saša Zelenika, and Ervin Kamenar
Loss of hand functions, often manifesting in the form of weakness or spasticity from conditions like stroke or multiple sclerosis, poses challenges in performing activities of daily living (ADLs). The broad area of rehabilitation robotics provides the tools and knowledge necessary for implementing efficient restorative therapies. These therapies aim to improve hand functionality with minimal therapist intervention. However, the human hand evolved for various precision and power gripping tasks, with its intricate anatomy featuring a large number of degrees of freedom—up to 31—which hinder its modeling in many rehabilitation scenarios. In the process of designing prosthetic devices, instrumented gloves, and rehabilitation devices, there is a clear need to obtain simplified rehabilitation-oriented hand models without compromising their representativeness across the population. This is where the concept of kinematic reduction, focusing on specific grasps, becomes essential. Thus, the objective of this study is to uncover the intra-finger dependencies during finger flexion/extension by analyzing a comprehensive database containing recorded trajectories for 23 different functional movements related to ADLs, involving 77 test subjects. The initial phase involves data wrangling, followed by correlation analysis aimed at selecting 116 dependency-movement relationships across all grasps. A regularized generalized linear model is then applied to select uncorrelated predictors, while a linear mixed-effect model, with reductions based on both predictor significance and effect size, is used for modeling the dependencies. As a final step, agglomerative clustering of models is performed to further facilitate flexibility in tradeoffs in hand model accuracy/reduction, allowing the modeling of finger flexion extensions using 5–15 degrees of freedom only.
@article{bazina2024reducing,title={Reducing Hand Kinematics by Introducing Grasp-Oriented Intra-Finger Dependencies},author={Bazina, Tomislav and Mau{\v{s}}a, Goran and Zelenika, Sa{\v{s}}a and Kamenar, Ervin},journal={Robotics},volume={13},number={6},pages={82},year={2024},publisher={MDPI},doi={10.3390/robotics13060082},}
