Cristiano De Marchis

Feedback of mechanical effectiveness induces adaptations in motor modules during cycling.

Recent studies have reported evidence that the motor system may rely on a modular organization, even if this behavior has yet to be confirmed during motor adaptation. The aim of the present study is to investigate the modular motor control mechanisms underlying the execution of pedaling by untrained subjects in different biomechanical conditions. We use the muscle synergies framework to characterize the muscle coordination of 11 subjects pedaling under two different conditions. The first one consists of a pedaling exercise with a strategy freely chosen by the subjects (Preferred Pedaling Technique, PPT), while the second condition constrains the gesture by means of a real time visual feedback of mechanical effectiveness (Effective Pedaling Technique, EPT). Pedal forces, recorded using a pair of instrumented pedals, were used to calculate the Index of Effectiveness (IE). EMG signals were recorded from eight muscles of the dominant leg and Non-negative Matrix Factorization (NMF) was applied for the extraction of muscle synergies. All the synergy vectors, extracted cycle by cycle for each subject, were pooled across subjects and conditions and underwent a 2-dimensional Sammons non-linear mapping. Seven representative clusters were identified on the Sammons projection, and the corresponding eight-dimensional synergy vectors were used to reconstruct the repertoire of muscle activation for all subjects and all pedaling conditions (VAF > 0.8 for each individual muscle pattern). Only 5 out of the 7 identified modules were used by the subjects during the PPT pedaling condition, while 2 additional modules were found specific for the pedaling condition EPT. The temporal recruitment of three identified modules was highly correlated with IE. The structure of the identified modules was found similar to that extracted in other studies of human walking, partly confirming the existence of shared and task specific muscle synergies, and providing further evidence on the modularity of the motor system.

Inter-individual variability of forces and modular muscle coordination in cycling: A study on untrained subjects

The aim of this study was to investigate the muscle coordination underlying pedaling in untrained subjects by using the muscle synergies paradigm, and to connect it with the inter-individual variability of EMG patterns and applied forces. Nine subjects performed a pedaling exercise on a cycle-simulator. Applied forces were recorded by means of instrumented pedals able to measure two force components. EMG signals were recorded from eight muscles of the dominant leg, and Nonnegative Matrix Factorization was applied to extract muscle synergy vectors W and time-varying activation coefficients H. Inter-individual variability was assessed for EMG patterns, force profiles, and H. Four modules were sufficient to reconstruct the muscle activation repertoire for all the subjects (variance accounted for >90% for each muscle). These modules were found to be highly similar between subjects in terms of W (mean r=.89), while most of the variability in force profiles and EMG patterns was reflected, in the muscle synergy structure, in the variability of H. These four modules have a functional interpretation when related to force distribution along the pedaling cycle, and the structure of W is shared with that present in human walking, suggesting the existence of a modular motor control in humans.

An optimized method for tremor detection and temporal tracking through repeated second order moment calculations on the surface EMG signal

In this study, the problem of detecting and tracking tremor from the surface myoelectric signal is addressed. A method based on the calculation of a Second Order Moment Function (SOMF) inside a window W sliding over the sEMG signal is here presented. An analytical formulation of the detector allows the extraction of the optimal parameters characterizing the algorithm. Performance of the optimized method is assessed on a set of synthetic tremor sEMG signals in terms of sensitivity, precision and accuracy through the use of a properly defined cost function able to explain the overall detector performance. The obtained results are compared to those emerging from the application of optimized versions of traditional detection techniques. Once tested on a database of synthetic tremor sEMG data, a quantitative assessment of the SOMF algorithm performance is carried out on experimental tremor sEMG signals recorded from two patients affected by Essential Tremor and from two patients affected by Parkinsons Disease. The SOMF algorithm outperforms the traditional techniques both in detecting (sensitivity and positive predictive value >99% for SNR higher than 3dB) and in estimating timings of muscular tremor bursts (bias and standard deviation on the estimation of the onset and offset time instants lower than 8ms). Its independence from the SNR level and its low computational cost make it suitable for real-time implementation and clinical use.

Multi-contact functional electrical stimulation for hand opening: electrophysiologically driven identification of the optimal stimulation site

BackgroundFunctional Electrical Stimulation (FES) is increasingly applied in neurorehabilitation. Particularly, the use of electrode arrays may allow for selective muscle recruitment. However, detecting the best electrode configuration constitutes still a challenge.MethodsA multi-contact set-up with thirty electrodes was applied for combined FES and electromyography (EMG) recording of the forearm. A search procedure scanned all electrode configurations by applying single, sub-threshold stimulation pulses while recording M-waves of the extensor digitorum communis (EDC), extensor carpi radialis (ECR) and extensor carpi ulnaris (ECU) muscles. The electrode contacts with the best electrophysiological response were then selected for stimulation with FES bursts while capturing finger/wrist extension and radial/ulnar deviation with a kinematic glove.ResultsThe stimulation electrodes chosen on the basis of M-waves of the EDC/ECR/ECU muscles were able to effectively elicit the respective finger/wrist movements for the targeted extension and/or deviation with high specificity in two different hand postures.ConclusionsA subset of functionally relevant stimulation electrodes could be selected fast, automatic and non-painful from a multi-contact array on the basis of muscle responses to subthreshold stimulation pulses. The selectivity of muscle recruitment predicted the kinematic pattern. This electrophysiologically driven approach would thus allow for an operator-independent positioning of the electrode array in neurorehabilitation.

Automatic artifact suppression in simultaneous tDCS-EEG using adaptive filtering.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation method that can be used in cognitive and clinical protocols in order to modulate neural activity. Although some macro effects are known, the underlying mechanisms are still not clear. tDCS in combination with electroencephalography (EEG) could help to understand these mechanisms from a neural point of view. However, simultaneous tDCS-EEG still remains challenging because of the artifacts that affect the recorded signals. In this paper, an automated artifact cancellation method based on adaptive filtering is proposed. Using independent component analysis (ICA), the artifacts were characterized using data from both a phantom and a group of healthy subjects. The resulting filter can successfully remove tDCS-related artifacts during anodal and cathodal stimulations.

Gait parameters are differently affected by concurrent smartphone-based activities with scaled levels of cognitive effort

The widespread and pervasive use of smartphones for sending messages, calling, and entertainment purposes, mainly among young adults, is often accompanied by the concurrent execution of other tasks. Recent studies have analyzed how texting, reading or calling while walking–in some specific conditions–might significantly influence gait parameters. The aim of this study is to examine the effect of different smartphone activities on walking, evaluating the variations of several gait parameters. 10 young healthy students (all smartphone proficient users) were instructed to text chat (with two different levels of cognitive load), call, surf on a social network or play with a math game while walking in a real-life outdoor setting. Each of these activities is characterized by a different cognitive load. Using an inertial measurement unit on the lower trunk, spatio-temporal gait parameters, together with regularity, symmetry and smoothness parameters, were extracted and grouped for comparison among normal walking and different dual task demands. An overall significant effect of task type on the aforementioned parameters group was observed. The alterations in gait parameters vary as a function of cognitive effort. In particular, stride frequency, step length and gait speed show a decrement, while step time increases as a function of cognitive effort. Smoothness, regularity and symmetry parameters are significantly altered for specific dual task conditions, mainly along the mediolateral direction. These results may lead to a better understanding of the possible risks related to walking and concurrent smartphone use.

EMG and Kinematics Assessment of Postural Responses during Balance Perturbation on a 3D Robotic Platform: Preliminary Results in Children with Hemiplegia

Dynamic posturography has been proposed as a valuable tool for the assessment of balance impairments. This paper compares the postural responses of healthy and hemiplegic children while keeping balance on a 3D robotic perturbed platform, addressed as Rotobit3D. Dynamic postural responses are assessed by using surface EMG, recorded from four muscles of both legs, and lower limb kinematics. These preliminary results show that the used protocol is able to highlight significant differences in the postural responses, in terms of postural asymmetries between the less affected and more affected side for both electrophysiological and kinematic recordings.

Gait Ratios and Variability Indices to Quantify the Effect of Using Smartphones in Dual-Task Walking

Smartphone use is one of the most common activities performed while walking: recent studies showed how this behaviour affected spatio-temporal, smoothness, symmetry and regularity gait parameters. In this study, we investigated a subset of additional gait parameters, potentially indicative of gait instability, to check whether concurrent smartphone activities cause deviations from stable walking. Ten young healthy adults were asked to walk outdoor normally and while performing five smartphone-based dual-task activities, with different levels of cognitive effort. Three groups of gait parameters, extracted by a single waist-mounted tri-axial inertial sensor, were analyzed: Gait Ratios group included Stride-to-Stance Time Ratio (SSTR)—equal to the golden ratio \( \upvarphi \) ≈ 1.618 in normal walking—and Walk Ratio (WR)—the ratio between Step Length (SL) and cadence, roughly constant within healthy subjects—Variability Measures group included Coefficients of Variation (CV) of SL and step time; Acceleration Ratios group composed of Root Mean Squared acceleration Ratios (RMSR)—the ratio between rms along a single direction and the total rms acceleration. When a dual-task is present, SSTR did not show significant variations from Baseline. A continuous typing activity with low cognitive engagement caused a significant decrease of WR with respect to all the other tasks. RMSR in the mediolateral direction and the CV SL showed visible yet not significant proportion with the amount of experienced cognitive effort. The resulting alterations were in general inconclusive as to their possible link with a reduced ability to adapt the locomotion structure to the context changes, even if for some parameters the observed proportion with cognitive effort and visual domain may need to be deepened on a bigger sample size, possibly including more challenging dual-task demands.

Consistent visuomotor adaptations and generalizations can be achieved through different rotations of robust motor modules

Humans can adapt their motor commands in response to alterations in the movement environment. This is achieved by tuning different motor primitives, generating adaptations that can be generalized also to relevant untrained scenarios. A theory of motor primitives has shown that natural movements can be described as combinations of muscle synergies. Previous studies have shown that motor adaptations are achieved by tuning the recruitment of robust synergy modules. Here we tested if: 1) different synergistic tunings can be achieved in response to the same perturbations applied with different orders of exposure; 2) different synergistic tunings can explain different patterns of generalization of adaptation. We found that exposing healthy individuals to two visuomotor rotation perturbations covering different parts of the same workspace in a different order resulted in different tunings of the activation of the same set of synergies. Nevertheless, these tunings resulted in the same net biomechanical adaptation patterns. We also show that the characteristics of the different tunings correlate with the presence and extent of generalization of adaptation to untrained portions of the workspace. Our results confirm synergies as invariant motor primitives whose recruitment is dynamically tuned during motor adaptations.

An automatic, adaptive, information-based algorithm for the extraction of the sEMG envelope

Surface ElectroMyography (sEMG) is widely used as a non-invasive tool for the assessment of motor control strategies. However, the standardization of the methods used for the estimation of sEMG amplitude is a problem yet to be solved; in most cases, sEMG amplitude is estimated through the extraction of the envelope of the signal via different low-pass filtering procedures with fixed cut-off frequencies chosen by the experimenter. In this work, we have shown how it is not possible to find the optimal choice of the cut-off frequency without any a priori knowledge on the signal; considering this, we have proposed an updated version of an iterative adaptive algorithm already present in literature, aiming to completely automatize the sEMG amplitude estimation. We have compared our algorithm to most of the typical solutions (fixed window filters and the previous version of the adaptive algorithm) for the extraction of the sEMG envelope, showing how the proposed adaptive procedure significantly improves the quality of the estimation, with a lower fraction of variance unexplained by the extracted envelope for different simulated modulating waveforms (p < 0.005). The definition of an entropy-based convergence criterion has allowed for a complete automatization of the process. We infer that this algorithm can ensure repeatability of the estimation of the sEMG amplitude, due to its independence from the experimental choices, so allowing for a quantitative interpretation in a clinical environment.