Joint Control During Arm Movements Performed in Reaching Activities of Daily Living

abstract: The ultimate goal of human movement control research is to understand how natural movements performed in daily reaching activities, are controlled. Natural movements require coordination of multiple degrees of freedom (DOF) of the arm. Patterns of arm joint control were studied during daily functional tasks, which were performed through the rotation of seven DOF in the arm. Analyzed movements which imitated the following 3 activities of daily living: moving an empty soda can from a table and placing it on a further position; placing the empty soda can from initial position at table to a position at shoulder level on a shelf; and placing the empty soda can from initial position at table to a position at eye level on a shelf. Kinematic and kinetic analyses were conducted for these three movements. The studied kinematic characteristics were: hand trajectory in the sagittal plane, displacements of the 7 DOF, and contribution of each DOF to hand velocity. The kinetic analysis involved computation of 3-dimensional vectors of muscle torque (MT), interaction torque (IT), gravity torque (GT), and net torque (NT) at the shoulder, elbow, and wrist. Using the relationship NT = MT + GT + IT, the role of active control and passive factors (gravitation and inter-segmental dynamics) in rotation of each joint by computing MT contribution (MTC) to NT was assessed. MTC was computed using the ratio of the signed MT projection on NT to NT magnitude. Despite a variety of joint movements available across the different tasks, 3 patterns of shoulder and elbow coordination prevailed in each movement: 1) active rotation of the shoulder and predominantly passive rotation of the elbow; 2) active rotation of the elbow and predominantly passive rotation of the shoulder; and 3) passive rotation of both joints. Analysis of wrist control suggested that MT mainly compensates for passive torque and provides adjustment of wrist motion according to requirements of each task. In conclusion, it was observed that the 3 shoulder-elbow coordination patterns (during which at least one joint moved) passively represented joint control primitives, underlying the performance of well-learned arm movements, although these patterns may be less prevalent during non-habitual movements.Dissertation/ThesisMasters Thesis Biomedical Engineering 201

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

In the last few decades, a number of technological developments have advanced the spread of wearable sensors for the assessment of human motion. These sensors have been also developed to assess athletes’ performance, providing useful guidelines for coaching, as well as for injury prevention. The data from these sensors provides key performance outcomes as well as more detailed kinematic, kinetic, and electromyographic data that provides insight into how the performance was obtained. From this perspective, inertial sensors, force sensors, and electromyography appear to be the most appropriate wearable sensors to use. Several studies were conducted to verify the feasibility of using wearable sensors for sport applications by using both commercially available and customized sensors. The present study seeks to provide an overview of sport biomechanics applications found from recent literature using wearable sensors, highlighting some information related to the used sensors and analysis methods. From the literature review results, it appears that inertial sensors are the most widespread sensors for assessing athletes’ performance; however, there still exist applications for force sensors and electromyography in this context. The main sport assessed in the studies was running, even though the range of sports examined was quite high. The provided overview can be useful for researchers, athletes, and coaches to understand the technologies currently available for sport performance assessment

Industrial human-robot collaboration: maximizing performance while maintaining safety

The goal of this thesis is to maximize performance in collaborative applications, while maintaining safety. For this, assembly workplaces are analyzed, typical tasks identified, and the potential of collaborative robots is elaborated. Current safety regulations are analyzed in order to identify the challenges in safe human-robot collaboration. Different methods are proposed to solve inefficiency in collaborative applications, in particular, intuitive programming of collaborative robots, efficient control with human-in-the-loop constraints, and a hardware solution, the Robotic Airbag.Das Ziel dieser Arbeit ist die Steigerung der Effizienz in kollaborativen Anwendungen, bei gleichzeitiger Einhaltung der Sicherheitsbestimmungen. Dazu werden Montagearbeitsplätze analysiert und das Potenzial kollaborativer Roboter erarbeitet. Aktuelle Sicherheitsvorschriften werden analysiert, um die Herausforderungen einer sicheren Mensch-Roboter-Zusammenarbeit zu identifizieren. Verschiedene Methoden wie intuitive Programmierung von kollaborativen Robotern, eine effiziente Steuerung mit Human-in-the-Loop Beschränkungen und eine Hardwarelösung - der Robotic Airbag - werden präsentiert

Safety Awareness for Rigid and Elastic Joint Robots: An Impact Dynamics and Control Framework

This thesis aims at making robots with rigid and elastic joints aware of human collision safety. A framework is proposed that captures human injury occurrence and robot inherent safety properties in a unified manner. It allows to quantitatively compare and optimize the safety characteristics of different robot designs and is applied to stationary and mobile manipulators. On the same basis, novel motion control schemes are developed and experimentally validated

Deep state-space modeling for explainable representation, analysis, and generation of professional human poses

The analysis of human movements has been extensively studied due to its wide variety of practical applications. Nevertheless, the state-of-the-art still faces scientific challenges while modeling human movements. Firstly, new models that account for the stochasticity of human movement and the physical structure of the human body are required to accurately predict the evolution of full-body motion descriptors over time. Secondly, the explainability of existing deep learning algorithms regarding their body posture predictions while generating human movements still needs to be improved as they lack comprehensible representations of human movement. This paper addresses these challenges by introducing three novel approaches for creating explainable representations of human movement. In this work, full-body movement is formulated as a state-space model of a dynamic system whose parameters are estimated using deep learning and statistical algorithms. The representations adhere to the structure of the Gesture Operational Model (GOM), which describes movement through its spatial and temporal assumptions. Two approaches correspond to deep state-space models that apply nonlinear network parameterization to provide interpretable posture predictions. The third method trains GOM representations using one-shot training with Kalman Filters. This training strategy enables users to model single movements and estimate their mathematical representation using procedures that require less computational power than deep learning algorithms. Ultimately, two applications of the generated representations are presented. The first is for the accurate generation of human movements, and the second is for body dexterity analysis of professional movements, where dynamic associations between body joints and meaningful motion descriptors are identified.Comment: Under revie

Bio-inspired vertebral column, compliance and semi-passive dynamics in a lightweight robot

International audienceThis paper presents the humanoid robot Acroban. We study two main issues: 1) Compliance and semi-passive dynamics for locomotion of humanoid robots regarding robustness against unknown external perturbations; 2) The advantages of a bio-inspired multi-articulated vertebral column. We combine mechatronic compliance with structural compliance due to the use of flexible materials. And we explore how these capabilities allow to enforce morphological computation in the design of robust dynamic locomotion. We also investigate the use of compliance to design semi-passive motor primitives using the torso and the arms as a system of accumulation/release of potential/kinetic energy