Marija Radmilović

Manipulation robotique à deux mains inspirée des aptitudes humaines: Le nombre de robot humanoïde s’est accru ces dernières années pour pouvoir collaborer avec l’homme ou le remplacer dans des tâches fastidieuses. L’objectif de cette thèse est de transférer aux robots humanoïdes, des habilités ou compétences humaines, en particulier pour des mouvements impliquant une coordination entre les deux bras. Dans la première partie de la thèse, un processus de conversion d’un mouvement humain vers un mouvement de robot, dans un objectif d’imitation est proposé. Comme les humains possèdent beaucoup plus de degrés de liberté qu’un robot humanoïde, les mouvements identiques ne peuvent pas être produits, les caractéristiques(longueurs des corps) peuvent aussi être différentes. Notre processus de conversion prend en compte l’enregistrement des localisations de marqueurs attachés aux corps de l’humain et des articulations pour améliorer les processus d’imitation. La deuxième partie de la thèse vise à analyser les stratégies de génération du mouvement utilisées par l’homme. Les mouvements humains sont supposés optimaux et notre objectif est de trouver un critère à minimiser pendant les manipulations. Nous faisons l’hypothèse que ce critère est une combinaison de critères classiquement utilisés en robotique et nous recherchons les poids de chaque critère qui représente au mieux le mouvement humain. De cette façon, une approche de commande cinématique optimale peut ensuite être utilisée pour générer des mouvements du robot humanoïde.

Automatsko prepoznavanje emocija na osnovu pokreta tela ima potencijal da unapredi interakciju čovek–računar i čovek–robot, uz širok spektar primena. Ipak, istraživanja u ovoj oblasti i dalje su u relativno ranim fazama. Ovaj rad predstavlja preliminarni sistematski pregled automatskog prepoznavanja emocija na osnovu pokreta tela. Analizirani su modaliteti i metode prikupljanja podataka, obuhvaćena emocionalna stanja, primenjene tehnike prepoznavanja, ostvareni učinak, kao i kontekst i karakteristike učesnika studija. Rezultati pokazuju da se većina istraživanja zasniva na upotrebi skupih sistema za snimanje pokreta i analizi različitih tipova pokreta, uključujući gestove, hod i plesne sekvence. Studije su obuhvatile prepoznavanje do osam emocionalnih stanja, primenom metoda mašinskog i dubokog učenja. Većina radova upoređivala je više pristupa radi identifikacije metoda sa najboljim performansama, pri čemu su pojedine studije ostvarile tačnost veću od 95%. Skupovi podataka uključivali su od 2 do 59 učesnika različitih profila. Iako postoje brojni izazovi, dalji razvoj oblasti zavisiće pre svega od unapređenja tehnologija za prikupljanje podataka, tehnika prepoznavanja i njihove validacije u realnim uslovima.

Motion capture technology finds wide application in various fields such as entertainment, sports analysis, and biomechanics, enabling accurate digitization of real-world movements for replication in digital environments. There exist diverse motion capture systems utilizing different technologies, supported by software tools for marker movement extraction. Further processing of acquired data requires additional investment in software development. This paper presents the human motion estimator application developed using GUI in MATLAB App Designer. This application aims to generate human movement based on recorded marker data obtained from motion capture systems (MCS), currently supporting two MCSs: Vicon and ART. The developed application aims to become a universal tool for estimating human movements, supporting data from ART or Vicon MCS. The application provides visual instructions for mounting markers on the body and initialization position, while data processing is based on optimization algorithms for defined criteria functions. The application targets research institutions by providing essential software support for ART or Vicon MCSs, thus enhancing data analysis capabilities for estimating upper body movement. The application is a framework, a starting point, to be upgraded and future developed with additional MCS and features.

The ultimate goal of rehabilitation engineering is to provide objective assessment tools for the level of injury and/or the degree of neurorehabilitation recovery based on a combination of different sensing technologies that enable the monitoring of relevant measurable variables, as well as the assessment of non-measurable variables (such as muscle effort/force and joint mechanical stiffness). This paper aims to present a feasibility study for a general assessment methodology for subject-specific non-measurable elbow model parameter prediction and elbow joint stiffness estimation. Ten participants without sensorimotor disorders performed a modified “Reach and retrieve” task of the Wolf Motor Function Test while electromyography (EMG) data of an antagonistic muscle pair (the triceps brachii long head and biceps brachii long head muscle) and elbow angle were simultaneously acquired. A complete list of the Hill’s muscle model and passive joint structure model parameters was generated using a genetic algorithm (GA) on the acquired training dataset with a maximum deviation of 6.1% of the full elbow angle range values during the modified task 8 of the Wolf Motor Function Test, and it was also verified using two experimental test scenarios (a task tempo variation scenario and a load variation scenario with a maximum deviation of 8.1%). The recursive least square (RLS) algorithm was used to estimate elbow joint stiffness (Stiffness) based on the estimated joint torque and the estimated elbow angle. Finally, novel Stiffness scales (general patterns) for upper limb functional assessment in the two performed test scenarios were proposed. The stiffness scales showed an exponentially increasing trend with increasing movement tempo, as well as with increasing weights. The obtained general Stiffness patterns from the group of participants without sensorimotor disorders could significantly contribute to the further monitoring of motor recovery in patients with sensorimotor disorders.