Optimal Biomechanical Performance in Upper-Limb Gestures Depends on Velocity and Carried Load

Abstract

In the last few years, there has been increased interest in the preservation of physical and mental health of workers that cooperate with robots in industrial contexts, such as in the framework of the European H2020 Mindbot Project. Since biomechanical analysis contributes to the characterization of the subject interacting with a robotic setup and platform, we tested different speed and loading conditions in a simulated environment to determine upper-limb optimal performance. The simulations were performed starting from laboratory data of people executing upper-limb frontal reaching movements, by scaling the motion law and imposing various carried loads at the hand. The simulated velocity ranged from 20% to 200% of the original natural speed, with step increments of 10%, while the hand loads were 0, 0.5, 1, and 2 kg, simulating carried objects. A 3D inverse kinematic and dynamic model was used to compute upper-limb kinematics and dynamics, including shoulder flexion, shoulder abduction, and elbow flexion. An optimal range of velocities was found in which the expended energy was lower. Interestingly, the optimal speed corresponding to lower exerted torque and energy decreased when the load applied increased. Lastly, we introduced a preliminary movement inefficiency index to evaluate the deviation of the power and expended energy for the shoulder flexion degree of freedom when not coinciding with the minimum energy condition. These results can be useful in human–robot collaboration to design minimum-fatigue collaborative tasks, tune setup parameters and robot behavior, and support physical and mental health for workers.

Editorial to the Special Issue “Human Bodywork: Applications in Health, Disease, and Rehabilitation”

“…Dos Santos et al [ 5 ], Maciejczyk et al [ 6 ], Dufournet et al [ 7 ], Shabat et al [ 8 ], An et al [ 9 ], Martínez-Jiménez et al [ 10 ], Wang et al [ 11 ], Ofran et al [ 12 ], Wang et al [ 13 ], Rostron et al [ 14 ], Silva Caiado et al [ 15 ], Arenales Arauz et al [ 16 ], Souto Braz et al [ 17 ], and Van Heuvelen et al [ 18 ] with an Reporting Guidelines for Whole-Body Vibration Studies in Humans, Animals and Cell Cultures. In the third category, the optimization of athlete performance was discussed in the papers of Brambilla et al [ 19 ], Strutzenberger et al [ 20 ], and Kiers et al [ 21 ]. In the fourth category, the authors focused on the modelization of the musculoskeletal system by Computational Analysis, the Finite Element Method, and the application of a Machine Learning Algorithms approach on rehabilitation, with submissions by Hong et al [ 22 ], Wang et al [ 23 ], Zhang et al [ 24 ], Antunes et al [ 25 ], Da Cunha de Sá-Caputo et al [ 26 ], Cheung et al [ 27 ], and Lu et al [ 28 ].…”