Daniele Bibbo

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.

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.

Varying behavior of different window sizes on the classification of static and dynamic physical activities from a single accelerometer

Accuracy of systems able to recognize in real time daily living activities heavily depends on the processing step for signal segmentation. So far, windowing approaches are used to segment data and the window size is usually chosen based on previous studies. However, literature is vague on the investigation of its effect on the obtained activity recognition accuracy, if both short and long duration activities are considered. In this work, we present the impact of window size on the recognition of daily living activities, where transitions between different activities are also taken into account. The study was conducted on nine participants who wore a tri-axial accelerometer on their waist and performed some short (sitting, standing, and transitions between activities) and long (walking, stair descending and stair ascending) duration activities. Five different classifiers were tested, and among the different window sizes, it was found that 1.5 s window size represents the best trade-off in recognition among activities, with an obtained accuracy well above 90%. Differences in recognition accuracy for each activity highlight the utility of developing adaptive segmentation criteria, based on the duration of the activities.

How to assess performance in cycling: the multivariate nature of influencing factors and related indicators

Finding an optimum for the cycling performance is not a trivial matter, since the literature shows the presence of many controversial aspects. In order to quantify different levels of performance, several indexes have been defined and used in many studies, reflecting variations in physiological and biomechanical factors. In particular, indexes such as Gross Efficiency (GE), Net Efficiency (NE) and Delta Efficiency (DE) have been referred to changes in metabolic efficiency (EffMet), while the Indexes of Effectiveness (IE), defined over the complete crank revolution or over part of it, have been referred to variations in mechanical effectiveness (EffMech). All these indicators quantify the variations of different factors [i.e., muscle fibers type distribution, pedaling cadence, setup of the bicycle frame, muscular fatigue (MFat), environmental variables, ergogenic aids, psychological traits (PsychTr)], which, moreover, show high mutual correlation. In the attempt of assessing cycling performance, most studies in the literature keep all these factors separated. This may bring to misleading results, leaving unanswered the question of how to improve cycling performance. This work provides an overview on the studies involving indexes and factors usually related to performance monitoring and assessment in cycling. In particular, in order to clarify all those aspects, the mutual interactions among these factors are highlighted, in view of a global performance assessment. Moreover, a proposal is presented advocating for a model-based approach that considers all factors mentioned in the survey, including the mutual interaction effects, for the definition of an objective function E representing the overall effectiveness of a training program in terms of both EffMet and EffMech.

How much can we trust the electromechanical delay estimated by using electromyography

In this paper different estimation techniques are evaluated for the assessment of electromechanical delay (EMD). The following techniques are compared for benchmarking purposes: envelope estimation and thresholding, with different subjective combinations of filters and thresholds, and a double threshold statistical detector (DTD). Performances are compared in terms of bias, standard deviation and erroneous detections of the estimations. DTD showed higher robustness and repeatability of results, guaranteed by the objective settings based on the statistical characteristics of the algorithm

Analysis of different image-based biofeedback models for improving cycling performances

Sport practice can take advantage from the quantitative assessment of task execution, which is strictly connected to the implementation of optimized training procedures. To this aim, it is interesting to explore the effectiveness of biofeedback training techniques. This implies a complete chain for information extraction containing instrumented devices, processing algorithms and graphical user interfaces (GUIs) to extract valuable information (i.e. kinematics, dynamics, and electrophysiology) to be presented in real-time to the athlete. In cycling, performance indexes displayed in a simple and perceivable way can help the cyclist optimize the pedaling. To this purpose, in this study four different GUIs have been designed and used in order to understand if and how a graphical biofeedback can influence the cycling performance. In particular, information related to the mechanical efficiency of pedaling is represented in each of the designed interfaces and then displayed to the user. This index is real-time calculated on the basis of the force signals exerted on the pedals during cycling. Instrumented pedals for bikes, already designed and implemented in our laboratory, have been used to measure those force components. A group of subjects underwent an experimental protocol and pedaled with (the interfaces have been used in a randomized order) and without graphical biofeedback. Preliminary results show how the effective perception of the biofeedback influences the motor performance.

Measurements of Generated Energy/Electrical Quantities from Locomotion Activities Using Piezoelectric Wearable Sensors for Body Motion Energy Harvesting

In this paper, two different piezoelectric transducers—a ceramic piezoelectric, lead zirconate titanate (PZT), and a polymeric piezoelectric, polyvinylidene fluoride (PVDF)—were compared in terms of energy that could be harvested during locomotion activities. The transducers were placed into a tight suit in proximity of the main body joints. Initial testing was performed by placing the transducers on the neck, shoulder, elbow, wrist, hip, knee and ankle; then, five locomotion activities—walking, walking up and down stairs, jogging and running—were chosen for the tests. The values of the power output measured during the five activities were in the range 6 µW–74 µW using both transducers for each joint.

A wireless integrated system to evaluate efficiency indexes in real time during cycling

In cycling, instrumented devices providing a quantitative assessment of the task execution could facilitate the functional evaluation and the training organization generally carried on by sport trainers. To this purpose, this work deals with the design and the implementation of a system able to evaluate the pedaling efficiency. The system is based on an Instrumented Pedal (IPed) that measures the components of the force (i.e. the perpendicular to the load plane and the tangential to the motion direction) exerted during pedaling and the angle between the pedal and the crank. The force signals are transmitted to a PDA by using a wireless connection and are processed in real time to obtain a performance index. This index is based on the ratio between the amplitude of the force component tangent to the crank (i.e. the component useful to the task) and the amplitude of the overall force vector applied to the pedal. The software packet calculates the performance index and displays it by using both numerical and graphical representations. The performance index can be used by the athletes to monitor the execution of the motor task in order to develop a more efficient pedaling strategy, and also by the trainers to control the training program and the ongoing performance.

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.