C. De Marchis

Muscle synergies are consistent when pedaling under different biomechanical demands

In this study we investigate the muscle coordination underlying the execution of a pedaling exercise across different biomechanical demands, by using the muscle synergies paradigm. 9 non professional subjects performed a cycling exercise using their preferred pedaling strategy (Preferred Strategy, PS) and then, through the use of a feedback based on the presentation of a real-time index of mechanical efficiency determined by means of instrumented pedals, they were helped to optimize their pedaling technique (Effective Strategy, ES). EMG activity was recorded from 8 muscles of the dominant leg. Nonnegative Matrix Factorization was applied for the extraction of muscle synergies. 4 modules were sufficient to reconstruct the repertoire of muscle activations for all the subjects during PS condition, and these modules were found consistent across all the subjects (correlation >; 83%). 5 muscle synergies were necessary for the characterization in ES condition; 4 out of these modules were shared with PS condition, and the resulting additional module appeared subject-specific. These preliminary results support the existence of a modular motor control in humans.

Neuromuscular adaptations during submaximal prolonged cycling

This study aims at evaluating the neuromuscular adaptations occurring during submaximal prolonged cycling tasks. In particular, we want to assess changes in surface electromyographic (sEMG) signal recorded during a pedaling task, performed by six subjects on a cycle-simulator at a constant power output, until voluntary exhaustion. Task failure was defined as the instant the subject was no longer able to maintain the required task. Electromyographic activity was recorded from eight muscles of the dominant leg and burst characteristics of sEMG signals were analyzed in order to assess the changes in muscle activity level produced by the occurrence of neuromuscular fatigue. In particular, three features were extracted from the sEMG signal for each burst: amplitude, location of the maxima and mean profile of the burst envelope. We have reported an increase in the amplitude parameter for all subjects only for Vastii while bi-articular muscles presented a high variability among subjects. Also the location of the maximal values of the mean envelope of the bursts was found to change when considering bi-articular or mono-articular muscles. The envelope profile was found not to be subject to alterations when comparing the end of the task with the beginning. We speculated that neuromuscular fatigue induces changes essentially in the mono-articular muscles which produce power. This phenomenon is highly correlated with the adopted pedaling strategy which, being not constrained, induces subjects to express the maximal power in the downstroke phase, related to knee extension and involving mainly mono-articular muscles.

Spatio-temporal gait parameters as estimated from wearable sensors placed at different waist levels

Wearable devices are able to capture movement-related characteristics from inertial sensors integrated in them. Many spatio-temporal parameters of gait can be estimated by the acquisition of inertial data, but their accuracy depends on the placement of the devices on the body, as well as on the numerical values chosen for the estimation techniques. In this work, three inertial sensors placed at three different heights of the trunk are used to collect data from healthy adult participants walking at three different speeds. Step length and step velocity were calculated from accelerometer data. For the estimation of these parameters high-pass filtering is required: fifteen different values of the filter cut-off frequency were analyzed for the subsequent step length estimation. The results were compared against those measured from a marker-based movement analysis system. Estimation accuracy of both step length and step velocity resulted significantly affected by both sensor location and cut-off frequency of the filter. These preliminary results suggest that placing the sensor too low leads to an increased estimation error, while frequencies up to around 1 Hz lead to acceptable results, with a significant decrease in estimation accuracy above that value.

ERD/ERS Patterns of Shooting Performance within the Multi-Action Plan Model

The multi-action plan (MAP) model reflects the notion that different psychophysiological states underlie distinct performance-related experiences. Previous empirical evidence suggested that attentional focus, affective states, and psycho-physiological patterns differ among optimal-automatic (type 1), optimal-controlled (type 2), suboptimal-controlled (type 3), and suboptimal-automatic (type 4) performance experiences.

Detection of tremor bursts from the sEMG Signal: An optimization procedure for different detection methods

Two different detection techniques for EMG burst detection are here used to reveal tremor in both a set of synthetic data and in a small sample of experimental trials. An optimization procedure that employs the minimization of a cost function to provide the parameter set characterizing the two techniques is here presented and its performance assessed. The results obtained with the optimization procedure are satisfactory and suitable for practical use: the values for both bias and standard deviation in the estimation of both onset and offset time instants are lower than 10 ms, and the sensitivity and positive predictive value in the detection of tremor bursts are > 96% for SNR levels higher than 6 dB.

The Fatigue Vector: A New Bi-dimensional Parameter for Muscular Fatigue Analysis

The aim of this study is to introduce a new parameter for fatigue investigations, which relies on a bi-dimensional analysis of sEMG signals in temporal and spectral domains. The new parameter, the Fatigue Vector, is defined in a space domain whose coordinates are the amplitude and the mean spectral frequency of the sEMG signal. The performance of the Fatigue Vector has been compared to those of classical parameters. The analysis has been carried on signals recorded from Rectus Femoris, Vastus Lateralis and Vastus Medialis during knee extension repetitions performed until exhaustion. The task was repeated twice with different biomechanical loads in order to test the muscular activity variations with respect to the different force demands.

Neuro-mechanics of muscle coordination during recumbent pedaling in post-acute stroke patients

Motor impairment after stroke has been hypothesized to be related, among others, to impairments in the modular control of movement. In this study we analyzed muscle coordination and pedal forces during a recumbent pedaling exercise from a sample of post-acute stroke patients (n=5) and a population of age-matched healthy individuals (n=4). Healthy subjects and the less impaired patients showed a shared modular organization of pedaling based on 4 similar muscle synergies. The most impaired patient, characterized by a Motricity Index of 52/100, showed a reduced complexity (only 2 muscle synergies for the affected side). Differences between healthy subjects and post-stroke patients in the execution of the task were identified in terms of unbalance in mechanical work production, which well corresponded to the level of impairment. This pedaling unbalance could be traced back to different activation strategies of the 4 identified modules. Investigation on a more representative sample will provide a full characterization of the neuro-mechanics of pedaling after stroke, helping our understandings of the disruption of motor coordination at central level after stroke and of the most effective solutions for functional recovery.

A SNR-independent formulation of a double threshold algorithm for the estimation of muscle activation intervals

The aim of this work is to propose an improvement to the double threshold algorithm for muscular activation intervals estimation developed by Bonato and his co-workers. The proposed method has been designed in order to be adaptive also when the Signal to Noise ratio (SNR) of the sEMG signal changes during the trial, by re-evaluating the parameters of the algorithm according to the estimated local SNR and the desired detection and false alarm probabilities. This novel implementation is also suitable for working in pseudo real-time since it can give information on burst estimation shortly after the end of the current muscular activity. The proposed method was tested on simulated signals taking into account changes in the SNR during the trial, and results were compared with those obtained with the classical implementation of the algorithm.