Neuromuscular Control Modeling: from Physics to Data-Driven Approaches

Il controllo neurale della postura umana è stato investigato a partire da un punto di vista fisico. Il paradigma di controllo intermittente è stato usato allo scopo di capire il peso di quest'ultimo nella generazione delle traiettorie del centro di pressione. Un primo contributo di questo lavoro rigurda quindi l'analisi del centro di pressione generato dal suddetto modello biomeccanico attraverso l'extended detrended fluctuation analysis, recentemente proposta in letteratura. Le proprietà di correlazione a lungo termine e disomogeneità sono risultate strettamente legate al guadagno derivativo del modello di controllo intermittente e anche al grado di intermittenza. Il paradigma di controllo è stato poi esteso verso un sistema biomeccanico più complesso, cioè un pendolo inverso doppio link con controllo intermittente alla caviglia. I contributi più significativi hanno riguardato la modellazione matematica del centro di pressione per una struttura multi-link e la verifica della sua plausibilità fisiologica. Si è poi preso in considerazione il caso della postura perturbata, integrando aspetti cinematici, dinamici e relativi all'attività muscolare. A tal fine, si è utilizzato sia un approccio fisico che basato su dati per l'identificazione dei modelli a struttura variabile Si sono prese in considerazione differenti condizioni di sperimentali, e in tutti i casi l'approccio utilizzato ha garantito un adeguato grado di interpretabilità riguardo il ruolo del sistema nervoso centrale nella regolazione del postura eretta in condizioni perturbate. La seconda parte della tesi ha riguardato la caratterizzazione del controllo motorio attraverso il segnale elettromiografico di superfice. Il primo contributo ha riguardato l'identifcazione dell'onset muscolare in condizioni di basso rapporto segnale rumore, sfruttando operatori energetico di tipo Teager-Kaiser al fine del precondizionamento del segnale mioelettrico. La versioe estesa di questo tipo di operatori è risultata particolarmente utile al miglioramento delle performance di numerosi algoritmi di detection. Si è poi proseguito con l'utilizzo di tali segnali al fine della classificazione dei gesti dell'arto superiore. In particoalre si è prerso in considerazione il problema della motion intention detection dei principali movimenti della spalla , utilizzando sia descrittori del segnale elettromiografico nel dominio del tempo e della frequenza. Quest'ultimo aspetto risulta essere un elemento di novità nel contesto scientifico in quanto si sono considerati il riconoscimento l'intezioni di movimento di otto gesti della spalla con particolare attenzione al ruolo dei descrittori del segnale per la classificazione. Infine, con approcci simili, si è preso in considerazione il problema del riconoscimento della scrittura manuale a partire dal dato elettromiografico. Tale aspetto risulta poco investigato sotto la prospettiva della pattern recognition mioelettrica, ma la sua valenza è data dalla crescente richiesta di interfacce uomo-macchina per compiti riabilitativi che coinvolgono una componente cognitiva significativa, Inoltre, vista la tendenza ad investigare il ruolo del polso per il prelievo del segnale elettromiografico al fine della realizzazione delle suddette interfacce, si è analizzato l'utilizzo dei segnali elettromiografici del polso rispetto a quelli dell'avambraccio al fine di predirre le cifre scritte dall'utente, noto che l'avambraccio risulta essere la zona di prelievo più comunemente utilizzata.The biomechanics and the neural control of the human stance was investigated starting from a physical point of view. In particular the intermittent motor control paradigm was investigated with the aim of understanding how such paradigm mirrors in the center of pressure (COP) trajectories. A first contribution given in this work of thesis regards the analysis of COP generated from intermittent controlled inverted pendulum through the extended detrended fluctuation analysis, which was recently introduced in the literature. It has been found that the long-term correlation and inohmogeniety properties of the COP time series strictly depend on the derivative gain term of the intermittent controller and on the degree of intermittency of the control action. Thus, , another contribution provided in this work of thesis regards the use of a more complex biomechanical model of the stance, e.g. a double-link inverted pendulum intermittently controlled at the ankle. In terms of novelty, it deserves to be pointed out the results regarding the mechanical modeling of the COP for a multi-link structure, and the assessment of its physiological plausibility. . On the other hand, when the perturbed posture motor task was taken into account, there was the need to enlarge the perspective, integrating kinematic, dynamic and muscle activity data. The idea of employing different sources of information to develop models of the CNS represents an important element that was investigated using tools related to hybrid system identification theory. Subjects underwent to impulsive support base translations in three different conditions: considering eyes open, closed, and performing mental counting. Although such data were essentially analyzed through a data-driven approach, the identified models guaranteed physical interpretations of the role played by the CNS in the three different conditions. The second main core of this thesis regards the characterization of the motor control using the surface electromyographic (sEMG) signals. A first contribution given in this work regarded the muscle onset detection considering low SNR scenarios. In this framework, energy operators such as the Teager-Kaiser energy operator (TKEO) and its extended version (ETKEO) were investigated as signal preconditioning steps before the application of state of the art onset detection algorithms. The latter have been significantly boosted when ETKEO was used with respect to TKEO. The use of extended energy operators for the sEMG signal preprocessing constitutes a novel element in this field that can be also further investigated in future studies. From the sEMG muscle, one can also predict which movement the subject is going to perform. This aspect can be enclosed in the motion intention detection (MID) field. In this thesis a MID problem was investigated by taking into account two important aspects: as first the study was centered on the shoulder joint movements. Secondly, the MID problem was faced under a pattern recognition perspective. This allowed to verify whether methodologies encountered in the myoelectric hand gesture recognition can be transferred in the affine field of MID In contrast to what reported in the literature, where MID problems generally consider only few movements, in this work of thesis up to eight shoulder movements have been investigated. Myoelectric pattern recognition architectures were also used in the assessment of the ten hand-written digits. Despite the handwriting can be considered a hand movement that involves fine muscular control actions, it has not been consistently investigated in the field of sEMG based hand gesture recognition. Further, since the literature supports the change from forearm to wrist in order to acquire EMG data for hand gesture recognition, it was investigated whether such exchange can be performed when a challenging classification task, as the handwriting recognition has to be performed

From Biomechanics to Robotics

International audienceHow does the central nervous system select and coordinate different degrees of freedom to execute a given movement? The difficulty is to choose one specific motor command among an infinite number of possible ones. If some invari-ants of movement can be identified, there exists however considerable variability showing that motor control favors rather an envelope of possible movements than a strong stereotypy. However, the central nervous system is able to find extremely fast solutions to the problem of muscular and kinematic redundancy by producing stable and precise movements. To date, no computational model has made it possible to develop a movement generation algorithm with a performance comparable to that of humans in terms of speed, accuracy, robustness and adaptability. One of the reasons is certainly that correct criteria for the synthesis of movement as a function of the task have not yet been identified and used for motion generation. In this chapter we propose to study highly dynamic human movement taking into account its variability, making the choice to consider performance biomechanical variables (tasks) for generating motions and involving whole-body articulations

An online impedance adaptation controller for decoding skill intelligence

Variable Impedance control allows robots and humans to safely and efficiently interact with unknown external environments. This tutorial introduces online impedance adaptation control (OIAC) for variable compliant joint motions in a range of control tasks: rapid (<1s) movement control (i.e., whipping to hit), arm and finger impedance quantification, multifunctional exoskeleton control, and robot-inspired human arm control hypothesis. The OIAC has been introduced as a feedback control, which can be integrated into a feedforward control, e.g., learned by data-driven methods. This integration facilitates the understanding of human and robot arm control, closing a research loop between biomechanics and robotics. It shows not only a research way from biomechanics to robotics, but also another reserved one. This tutorial aims at presenting research examples and Python codes for advancing the understanding of variable impedance adaptation in human and robot motor control. It contributes to the state-of-the-art by providing an online impedance adaptation controller for wearable robots (i.e., exoskeletons) which can be used in robotic and biomechanical applications

The green leafhopper, cicadella viridis (hemiptera, auchenorrhyncha, cicadellidae), jumps with near-constant acceleration

Jumping insects develop accelerations that can greatly exceed gravitational acceleration. Although several species have been analysed using different tools, ranging from a purely physical to a morpho-physiological approach, instantaneous dynamic and kinematic data concerning the jumping motion are lacking. This is mainly due to the difficulty in observing in detail events that occur in a few milliseconds. In this study, the behaviour of the green leafhopper, Cicadella viridis, was investigated during the take-off phase of the jump, through high-speed video recordings (8000 frames s-1). We demonstrate that C. viridis is able to maintain fairly constant acceleration during overall leg elongation. The force exerted at the foot-ground interface is nearly constant and differs from the force expected from other typical motion models. A biomechanical model was used to highlight that this ability relies on the morphology of C. viridis hind legs, which act as a motion converter with a variable transmission ratio and use the time-dependent musculo-elastic force to generate a nearly constant thrust at the body-ground interface. This modulation mechanism minimizes the risk of breaking the substrate thanks to the absence of force peaks. The results of this study are of broad relevance in different research fields ranging from biomechanics to robotics. © 2013. Published by The Company of Biologists Ltd