Upper Limb Sensory-Motor Control During Exposure to Different Mechanical Environments in Multiple Sclerosis Subjects With No Clinical Disability

Multiple sclerosis (MS) is an autoimmune and neurodegenerative disease resulting in motor impairments associated with muscle weakness and lack of movement coordination. The goal of this work was to quantify upper limb motor deficits in asymptomatic MS subjects with a robot-based assessment including performance and muscle synergies analysis. A total of 7 subjects (MS: 3 M-4 F; 42 +/- 10 years) with clinically definite MS according to McDonald criteria, but with no clinical disability, and 7 age- and sex-matched subjects without a history of neurological disorders participated in the study. All subjects controlled a cursor on the computer screen by moving their hand or applying forces in 8 coplanar directions at their self-selected speed. They grasped the handle of a robotic planar manipulandum that generated four different environments: null, assistive or resistive forces, and rigid constraint. Simultaneously, the activity of 15 upper body muscles was recorded. Asymptomatic MS subjects generated less smooth and less accurate cursor trajectories than control subjects in controlling a force profile, while the end-point error was significantly different also in the other environments. The EMG analysis revealed different muscle activation patterns in MS subjects when exerting isometric forces or when moving in presence of external forces generated by a robot. While the two populations had the same number and similar structure of muscle synergies, they had different activation profiles. These results suggested that a task requiring to control forces against a rigid environment allows better than movement tasks to detect early sensory-motor signs related to the onset of symptoms of multiple sclerosis and to differentiate between stages of the disease

Data-driven body–machine interface for the accurate control of drones

The teleoperation of nonhumanoid robots is often a demanding task, as most current control interfaces rely on mappings between the operator’s and the robot’s actions, which are determined by the design and characteristics of the interface, and may therefore be challenging to master. Here, we describe a structured methodology to identify common patterns in spontaneous interaction behaviors, to implement embodied user interfaces, and to select the appropriate sensor type and positioning. Using this method, we developed an intuitive, gesture-based control interface for real and simulated drones, which outperformed a standard joystick in terms of learning time and steering abilities. Implementing this procedure to identify body-machine patterns for specific applications could support the development of more intuitive and effective interfaces

A Neurally Inspired Robotic Control Algorithm for Gait Rehabilitation in Hemiplegic Stroke Patients

Abstract-Cerebrovascular accident or stroke is one of the major brain impairments that affects numerous people globally. After a unilateral stroke, sensory motor damages contralateral to the brain lesion occur in many patients. As a result, gait remains impaired and asymmetric. This paper describes and simulates a novel closed loop algorithm designed for the control of a lower limb exoskeleton for post-stroke rehabilitation. The algorithm has been developed to control a lower limb exoskeleton including actuators for the hip and knee joints, and feedback sensors for the measure of joint angular excursions. It has been designed to control and correct the gait cycle of the affected leg using kinematics information from the unaffected one. In particular, a probabilistic particle filter like algorithm has been used at the top-level control to modulate gait velocity and the joint angular excursions. Simulation results show that the algorithm is able to correct and control velocity of the affected side restoring phase synchronization between the legs

Analisi e studio di una metodica innovativa per la valutazione funzionale dell'arto superiore

Il sistema robotico MIT-Manus per la riabilitazione dell’arto superiore è stato scelto da molti gruppi di ricerca come valido strumento per la terapia riabilitativa dell’arto superiore in soggetti colpiti da ictus, e come sistema robotico che permette di acquisire dati biomeccanici durante la registrazione del segnale elettroencefalografico. Gli obiettivi della tesi proposta sono i seguenti: 1) valutare gli effetti della terapia robotica nei soggetti emiparetici, 2) analizzare i meccanismi neurofisiologici alla base delle disabilità motorie a seguito di un ictus e 3) individuare parametri di interesse clinico a partire dai segnali EEG che possano essere utilizzati per applicazioni di tipo Brain Machine Interface (BMI) da integrare al sistema robotico MIT-Manus. L’approccio utilizzato consiste nello sviluppo e validazione di un metodo di valutazione funzionale innovativo per gli esercizi riabilitativi proposti durante la terapia robotica basato sull’analisi dell’attività cerebrale e di dati biomeccanici dell’arto superiore. In dettaglio, sono stati registrati e analizzati segnali EEG, dati cinematici e dinamici durante movimenti di reaching. Il lavoro di tesi presenta un metodo innovativo per la valutazione funzionale che integra parametri derivanti dall’analisi di segnali EEG registrati durante l’esecuzione di movimenti di reaching e dati biomeccanici registrati mediante il sistema robotico. Inoltre, sono state studiate le correlazioni tra i più significativi parametri cinematici e dinamici relativi al movimento di reaching e quelli ottenuti a partire da Motor Cortical Related Potential (MRCP) e Event Related Desynchronization (ERD)

Muscle activities in similar arms performing identical tasks reveal the neural basis of muscle synergies

Are the muscle synergies extracted from multiple electromyographic signals an expression of neural information processing, or rather a by-product of mechanical and task constraints? To address this question, we asked 41 right-handed adults to perform a variety of motor tasks with their left and right arms. The analysis of the muscle activities resulted in the identification of synergies whose activation was different for the two sides. In particular, tasks involving the control of isometric forces resulted in larger differences. As the two arms essentially have identical biomechanical structure, we concluded that the differences observed in the activation of the respective synergies must be attributed to neural control