arXiv subjects

Alessandro Del Vecchio

Publications and source records attributed to Alessandro Del Vecchio.

4 recordsLinked to original sources

arXiv2026

MyoKin3X: A Myoelectric Framework for Full-Hand 3D Force Recording

Simultaneous multi-directional force measurement across all five digits is essential for studying hand coordination, compensatory forces, and myoelectric control, yet existing systems trade off digit coverage, force dimensionality, and anatomical adaptability. Reliable full-hand acquisition remains challenging because multi-axis calibration, hand-size adjustment, and consistent digit-specific force reconstruction are technically demanding. We present MyoKin3X, a customizable full-hand framework for simultaneous 3D force measurement of up to five digits providing robust and validated force reconstruction. It combines an anatomically versatile structure with five integrated 3D force sensors and a standalone software for synchronized electromyography and force acquisition. MyoKin3X provides in-place cross-calibration of all five sensors, single- and multi-digit maximal voluntary contraction recording, and automated coordinate transformation to digit-specific coordinate systems for standardized analysis across subjects and tasks. Calibration validation demonstrates high stability of the axis-specific calibration factors, with a mean coefficient of variation of 0.04% and maximum force error of +- 0.06N at 50N. It also shows effective inter-axis decoupling (mean crosstalk reduction: 92.71%; residual crosstalk below 0.02% for most axis pairs) and high predictive accuracy (R2 > 0.99 across sensors). The software includes four feedback modes: 1D ramps, fatigue protocols, 2D arbitrary target ramps, and 2D exploratory tasks. MyoKin3X therefore enables standardized full-hand force acquisition with validated measurement reliability, flexible protocol control, and real-time visualization for high-fidelity studies of hand motor control, muscle synergies, and human-machine interfacing.

cs.HC↗

arXiv2026

Closed-loop Neuroprosthetic Control through Spared Neural Activity Enables Proportional Foot Movements after Spinal Cord Injury

Loss of voluntary foot movement after spinal cord injury (SCI) can significantly limit independent mobility and quality of life. To improve motor output after injury, functional electrical stimulation (FES) is used to deliver stimulation pulses through the skin to affected muscles. While commercial FES systems typically use motion-based triggers, prior research shows that spared movement intent can be decoded after SCI using surface electromyography (EMG). Our aim is to assess how well spared neural signals of the lower limb after SCI can be decoded and used to control electrical stimulation for restoring foot movement. We developed a wearable machine learning-powered neuroprosthetic that records EMG from the affected lower limb using a 32-channel electrode bracelet and enables closed-loop control of a FES device for foot movement restoration. Five participants with SCI used the predicted control signal to follow trajectories on a screen with their foot and achieve distinct motor activation patterns for foot flexion, extension, and inversion or eversion. Three of these participants also achieved 2 proportional activation levels during foot flexion/extension with more than 70% accuracy. To validate how these neural signals can be used for closed-loop neuroprosthetic control, two participants used their decoded activity to control a FES device and stimulate their affected foot. This resulted in an increased foot flexion range for both participants of 33.6% and 40% of a functional healthy range, respectively (p smaller than 0.001). One of the participants also achieved voluntary proportional control of up to 6 stimulation levels during foot flexion/extension. These results suggest that wearable EMG decoding coupled with FES systems provides a scalable strategy for closed-loop neuroprosthetic control supporting voluntary foot movement.

cs.HC↗

arXiv2024

Simultaneous Control of Human Hand Joint Positions and Grip Force via HD-EMG and Deep Learning

In myoelectric control, simultaneous control of multiple degrees of freedom can be challenging due to the dexterity of the human hand. Numerous studies have focused on hand functionality, however, they only focused on a few degrees of freedom. In this paper, a 3DCNN-MLP model is proposed that uses high-density sEMG signals to estimate 20 hand joint positions and grip force simultaneously. The deep learning model maps the muscle activity to the hand kinematics and kinetics. The proposed models' performance is also evaluated in estimating grip forces with real-time resolution. This paper investigated three individual dynamic hand movements (2pinch, 3pinch, and fist closing and opening) while applying forces in 10% and 30% of the maximum voluntary contraction (MVC). The results demonstrated significant accuracy in estimating kinetics and kinematics. The average Euclidean distance across all joints and subjects was 11.01 $\pm$ 2.22 mm and the mean absolute error for offline and real-time force estimation were found to be 0.8 $\pm$ 0.33 N and 2.09 $\pm$ 0.9 N respectively. The results demonstrate that by leveraging high-density sEMG and deep learning, it is possible to estimate human hand dynamics (kinematics and kinetics), which is a step forward to practical prosthetic hands.

cs.HC↗

arXiv2024

MyoGestic: EMG Interfacing Framework for Decoding Multiple Spared Degrees of Freedom of the Hand in Individuals with Neural Lesions

Restoring limb motor function in individuals with spinal cord injury (SCI), stroke, or amputation remains a critical challenge, one which affects millions worldwide. Recent studies show through surface electromyography (EMG) that spared motor neurons can still be voluntarily controlled, even without visible limb movement . These signals can be decoded and used for motor intent estimation; however, current wearable solutions lack the necessary hardware and software for intuitive interfacing of the spared degrees of freedom after neural injuries. To address these limitations, we developed a wireless, high-density EMG bracelet, coupled with a novel software framework, MyoGestic. Our system allows rapid and tailored adaptability of machine learning models to the needs of the users, facilitating real-time decoding of multiple spared distinctive degrees of freedom. In our study, we successfully decoded the motor intent from two participants with SCI, two with spinal stroke , and three amputees in real-time, achieving several controllable degrees of freedom within minutes after wearing the EMG bracelet. We provide a proof-of-concept that these decoded signals can be used to control a digitally rendered hand, a wearable orthosis, a prosthesis, or a 2D cursor. Our framework promotes a participant-centered approach, allowing immediate feedback integration, thus enhancing the iterative development of myocontrol algorithms. The proposed open-source software framework, MyoGestic, allows researchers and patients to focus on the augmentation and training of the spared degrees of freedom after neural lesions, thus potentially bridging the gap between research and clinical application and advancing the development of intuitive EMG interfaces for diverse neural lesions.

cs.HC↗