Accurate Continuous Prediction of 14 Degrees of Freedom of the Hand from Myoelectrical Signals through Convolutive Deep Learning - Simpetru, Raul C.; Oßwald, Marius; Braun, Dominik; Souza de Oliveira, Daniela; Cakici, Andre L.; Del Vecchio, Alessandro (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2022)
Natural control of assistive devices requires continuous positional encoding and decoding of the user's volition. Human movement is encoded by recruitment and rate coding of spinal motor units. Surface electromyography provides some information on the neural code of movement and is usually decoded into finger joint angles. However, the current approaches to mapping the electrical signal into joint angles are unsatisfactory. There are no methods that allow precise estimation of joint angles during natural hand movements within the large numbers of degrees of freedom of the hand. We propose a framework to train a neural network from digital cameras and high-density surface electromyography from the extrinsic (forearm and wrist) hand muscles. Furthermore, we show that our 3D convolutional neural network optimally predicted 14 functional flexion/extension joints of the hand. We found in our experiments (4 subjects; mean age of 26-2.12years) that our model can predict individual sinusoidal finger movement at different speeds (0.5 and 1.5 Hz), as well as two and three finger pinching, and hand opening and closing, covering 14 degrees of freedom of the hand. Our deep learning method shows a mean absolute error of 2.78-0.28 degrees with a mean correlation coefficient between predicted and expected joint angles of 0.94, 95% confidence interval (CI) [0.81, 0.98] with simulated real-time inference times lower than 30 milliseconds. These results demonstrate that our approach is capable of predicting the user's volition similar to digital cameras through a non-invasive wearable neural interface. Clinical relevance - This method establishes a viable interface that can be used for both immersive virtual reality medical simulations environments and assistive devices such as exoskeleton and prosthetics.
CRIS-Team Scopus Importer:2022-10-14

Accurate Continuous Prediction of 14 Degrees of Freedom of the Hand from Myoelectrical Signals through Convolutive Deep Learning - Simpetru, Raul; Oßwald, Marius; Braun, Dominik; Souza de Oliveira, Daniela; Cakici, Andre; Del Vecchio, Alessandro (44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022, 2022)
Natural control of assistive devices requires continuous positional encoding and decoding of the user's volition. Human movement is encoded by recruitment and rate coding of spinal motor units. Surface electromyography provides some information on the neural code of movement and is usually decoded into finger joint angles. However, the current approaches to mapping the electrical signal into joint angles are unsatisfactory. There are no methods that allow precise estimation of joint angles during natural hand movements within the large numbers of degrees of freedom of the hand. We propose a framework to train a neural network from digital cameras and high-density surface electromyography from the extrinsic (forearm and wrist) hand muscles. Furthermore, we show that our 3D convolutional neural network optimally predicted 14 functional flexion/extension joints of the hand. We found in our experiments (4 subjects; mean age of 26 +- 2.12 years) that our model can predict individual sinusoidal finger movement at different speeds (0.5 and 1.5 Hz), as well as two and three finger pinching, and hand opening and closing, covering 14 degrees of freedom of the hand. Our deep learning method shows a mean absolute error of 2.78 +- 0.28 degrees with a mean correlation coefficient between predicted and expected joint angles of 0.94, 95% confidence interval (CI) [0.81, 0.98] with simulated real-time inference times lower than 30 milliseconds. These results demonstrate that our approach is capable of predicting the user's volition similar to digital cameras through a non-invasive wearable neural interface. Clinical relevance— This method establishes a viable interface that can be used for both immersive virtual reality medical simulations environments and assistive devices such as exoskeleton and prosthetics

A direct spinal cord–computer interface enables the control of the paralysed hand in spinal cord injury - Souza de Oliveira, Daniela; Ponfick, Matthias; Braun, Dominik; Oßwald, Marius; Sierotowicz, Marek; Chatterjee, Satyaki; Weber, Douglas J.; Eskofier, Björn; Castellini, Claudio; Farina, Dario; Kinfe, Thomas Mehari; Del Vecchio, Alessandro (Brain, 2024)
The paralysis of the muscles controlling the hand dramatically limits the quality of life of individuals living with spinal cord injury (SCI). Here, with a non-invasive neural interface, we demonstrate that eight motor complete SCI individuals (C5-C6) are still able to task-modulate in real-time the activity of populations of spinal motor neurons with residual neural pathways. In all SCI participants tested, we identified groups of motor units under voluntary control that encoded various hand movements. The motor unit discharges were mapped into more than 10 degrees of freedom, ranging from grasping to individual hand-digit flexion and extension. We then mapped the neural dynamics into a real-time controlled virtual hand. The SCI participants were able to match the cue hand posture by proportionally controlling four degrees of freedom (opening and closing the hand and index flexion/extension). These results demonstrate that wearable muscle sensors provide access to spared motor neurons that are fully under voluntary control in complete cervical SCI individuals. This non-invasive neural interface allows the investigation of motor neuron changes after the injury and has the potential to promote movement restoration when integrated with assistive devices.<br
Keywords: spinal cord injury; motor neuron; motor unit; high density surface electromyography; neural interface

A Generalized Framework for the Study of Spinal Motor Neurons Controlling the Human Hand During Dynamic Movements - Cakici, Andre L.; Oßwald, Marius; Souza de Oliveira, Daniela; Braun, Dominik; Simpetru, Raul C.; Kinfe, Thomas Mehari; Eskofier, Björn; Del Vecchio, Alessandro (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2022)
The human hand possesses a large number of degrees of freedom. Hand dexterity is encoded by the discharge times of spinal motor units (MUs). Most of our knowledge on the neural control of movement is based on the discharge times of MUs during isometric contractions. Here we designed a noninvasive framework to study spinal motor neurons during dynamic hand movements with the aim to understand the neural control of MUs during sinusoidal hand digit flexion and extension at different rates of force development. The framework included 320 high-density surface EMG electrodes placed on the forearm muscles, with markerless 3D hand kinematics extracted with deep learning, and a realistic virtual hand that displayed the motor tasks. The movements included flexion and extension of individual hand digits at two different speeds (0.5 Hz and 1.5 Hz) for 40 seconds. We found on average 4.7-1.7 MUs across participants and tasks. Most MUs showed a biphasic pattern closely mirroring the flexion and extension kinematics. Indeed, a factor analysis method (non-negative matrix factorization) was able to learn the two components (flexion/extension) with high accuracy at the individual MU level (R=0.87-0.12). Although most MUs were highly correlated with either flexion or extension movements, there was a smaller proportion of MUs that was not task-modulated and controlled by a different neural module (7.1% of all MUs with R < 0.3). This work shows a noninvasive visually guided framework to study motor neurons controlling the movement of the hand in human participants during dynamic hand digit movements.
CRIS-Team Scopus Importer:2022-10-14

A Generalized Framework for the Study of Spinal Motor Neurons Controlling the Human Hand During Dynamic Movements - Cakici, Andre; Oßwald, Marius; Souza de Oliveira, Daniela; Braun, Dominik; Simpetru, Raul; Kinfe, Thomas Mehari; Eskofier, Björn; Del Vecchio, Alessandro (44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022, 2022)
The human hand possesses a large number of degrees of freedom. Hand dexterity is encoded by the discharge times of spinal motor units (MUs). Most of our knowledge on the neural control of movement is based on the discharge times of MUs during isometric contractions. Here we designed a noninvasive framework to study spinal motor neurons during dynamic hand movements with the aim to understand the neural control of MUs during sinusoidal hand digit flexion and extension at different rates of force development. The framework included 320 high-density surface EMG electrodes placed on the forearm muscles, with markerless 3D hand kinematics extracted with deep learning, and a realistic virtual hand that displayed the motor tasks. The movements included flexion and extension of individual hand digits at two different speeds (0.5 Hz and 1.5 Hz) for 40 seconds. We found on average 4.7 +- 1.7 MUs across participants and tasks. Most MUs showed a biphasic pattern closely mirroring the flexion and extension kinematics. Indeed, a factor analysis method (non-negative matrix factorization) was able to learn the two components (flexion/extension) with high accuracy at the individual MU level (R = 0.87 +- 0.12). Although most MUs were highly correlated with either flexion or extension movements, there was a smaller proportion of MUs that was not task-modulated and controlled by a different neural module (7.1% of all MUs with R < 0.3). This work shows a noninvasive visually guided framework to study motor neurons controlling the movement of the hand in human participants during dynamic hand digit movements.

Learning a Hand Model from Dynamic Movements Using High-Density EMG and Convolutional Neural Networks - Simpetru, Raul; Arkudas, Andreas; Braun, Dominik; Oßwald, Marius; Souza de Oliveira, Daniela; Eskofier, Björn; Kinfe, Thomas Mehari; Del Vecchio, Alessandro (IEEE Transactions on Biomedical Engineering, 2024)
Objective: Surface electromyography (sEMG) can sense the motor commands transmitted to the muscles. This work presents a deep learning method that can decode the electrophysiological activity of the forearm muscles into the movements of the human hand. Methods: We have recorded the kinematics and kinetics of the hand during a wide range of grasps and individual digit movements that cover 22 degrees of freedom of the hand at slow (0.5 Hz) and comfortable (1.5 Hz) movement speeds in 13 healthy participants. The input of the model consists of 320 non-invasive EMG sensors placed on the extrinsic hand muscles. Results: Our network achieves accurate continuous estimation of both kinematics and kinetics, surpassing the performance of comparable networks reported in the literature. By examining the latent space of the network, we find evidence that it mapped EMG activity into the anatomy of the hand at the individual digit level. In contrast to what is observed from the low-pass filtered EMG and linear decoding approaches, we found that the full-bandwidth EMG (monopolar unfiltered) signals during synergistic and individual digit movements contain distinct neural embeddings that encode each movement of the human hand. These manifolds consistently represent the anatomy of the hand and are generalized across participants. Moreover, we found a task-specific distribution of the embeddings without any presence of correlated activations during multi- and individual-digit tasks. Conclusion/Significance: The proposed method could advance the control of assistive hand devices by providing a robust and intuitive interface between muscle signals and hand movement
Keywords: Thumb; Task analysis; Deep learning; EMG; Force; Deep Learning; Electromyography; Muscles; Recording; Kinematics; Kinematics

No Differences in Motor Units Discharge Rate Between Females and Males in Explosive Ankle Dorsiflexions - Grootenhuis, Anna; Hiereth, Fiona C.; Škarabot, Jakob; Oßwald, Marius; Del Vecchio, Alessandro; Gruber, Markus; Ruggiero, Luca (Scandinavian journal of medicine & science in sports, 2025)
Males and females exhibit comparable levels of explosive strength if the rate of torque development (RTD) is considered relative to the maximal voluntary isometric torque (MVT). Given the greater proportion of type I to type II fibers area in muscles of females than males ( 20% in tibialis anterior muscle), with slower contractile kinetics, the discharge rate of motor units (MUDR) in explosive efforts may be higher to compensate for the slower muscle contractile properties. Yet, it is to date unknown whether sex differences in MUDR in explosive efforts exist. Twenty-two females and 12 males (20–34 years) performed electrically evoked and voluntary rapid isometric ankle dorsiflexions. Electrically evoked contractions consisted of three trains of 50 supramaximal stimuli at the common peroneal nerve at 200 Hz. Ten voluntary explosive contractions were then performed while recording high-density electromyography signals from the tibialis anterior. No significant differences were reported between males and females for both voluntary and electrically evoked relative peak RTD (8.0 ± 2.0 vs. 7.7 ± 0.8 and 6.4 ± 0.7 vs. 6.1 ± 2.1 MVT s−1), and for RTD and muscle activity throughout the first 150 ms (p > 0.07). Despite no significant differences, these metrics were not equivalent between sexes when assessed with equivalence testing. Time to peak RTD was shorter in males than females for both contraction types (voluntary: 61 ± 12 vs. 74 ± 14 ms; electrically evoked: 24 ± 3 vs. 28 ± 7 ms; p < 0.01). MUDR at the beginning of the explosive voluntary efforts was not different between sexes (males: 61 ± 15; females: 67 ± 16 Hz; p < 0.23). Overall, despite known sex-related differences within the skeletal muscle, the control of rapid torque production is not different between sexes.
CRIS-Team Scopus Importer:2025-05-23
Keywords: ballistic; EMG decomposition; firing rate; motoneurones; rapid; sex differences

Simplifying Computer Aided Ergonomics: A User-Product Interaction-Modeling Framework in CAD Based on a Taxonomy of Elementary Affordances - Wolf, Alexander; Wagner, Yvonne; Oßwald, Marius; Miehling, Jörg; Wartzack, Sandro (IISE Transactions on Occupational Ergonomics and Human Factors, 2021)
OCCUPATIONAL APPLICATIONS
CRIS-Team WoS Importer:2021-07-09

Simplifying Ergonomic Assessment for Designers: A User-Product Interaction-Modelling Framework in CAD - Wolf, Alexander; Wagner, Yvonne; Oßwald, Marius; Miehling, Jörg; Wartzack, Sandro; Wolf, Alexander; Wagner, Yvonne; Oßwald, Marius; Miehling, Jörg; Wartzack, Sandro (Proceedings of the 21st Congress of the International Ergonomics Association (IEA 2021), 2021)
Digital human models have not yet reached their full potential for proactive virtual assessment of ergonomics in engineering and industrial design. Especially the modelling of interaction between user and product often is time demanding, cumbersome, unstandardized and embedded insufficiently in the computer-aided engineering environment. On the one hand, the interaction modelling needs to be applicable for a majority of products and shall contain as much a-priori knowledge regarding human behavior as possible. On the other hand, the method needs to be appropriate for designers, without special ergonomic expertise or human behavior training. In this contribution, we present an interaction-modelling framework based on the concept of affordances, which ought to resolve these partly contradictable demands. Hence, 31 elementary affordances, describing fundamental physical interaction possibilities between human end effectors and rudimental (product) geometries, were deduced using a classification method. The elementary affordances shall serve as a medium for interaction modeling. For this purpose, we introduce an interaction modelling routine, implemented in a CAD system, which makes use of the identified elementary affordances in terms of CAD-features. Those enable designers to apply interaction possibilities directly to a CAD-model in order to define the constraints for a DHM simulatio
CRIS-Team Scopus Importer:2021-08-13
Keywords: User product interaction; Digital human models; Affordance; CAD; Interaction modelling