We developed an intuitively operational shoulder disarticulation prosthesis system that can be used without long-term training. The developed system consisted of four degrees of freedom joints, as well as a user adapting control system based on a machine learning technique and surface electromyogram (EMG) of the trunk. We measured the surface EMG of the trunk of healthy subjects at multiple points and analyzed through principal component analysis to identify the proper EMG measurement portion of the trunk, which was determined to be distributed in the chest and back. Additionally, evaluation experiments demonstrated the capability of four healthy subjects to grasp and move objects in the horizontal as well as the vertical directions, using our developed system controlled via the EMG of the chest and back. Moreover, we also quantitatively confirmed the ability of a bilateral shoulder disarticulation amputee to complete the evaluation experiment similar to healthy subjects.
The brain must coordinate with redundant bodies to perform motion tasks. The aim of the present study is to propose a novel control model that predicts the characteristics of human joint coordination at a behavioral level. To evaluate the joint coordination, an uncontrolled manifold (UCM) analysis that focuses on the trial-to-trial variance of joints has been proposed. The UCM is a nonlinear manifold associated with redundant kinematics. In this study, we directly applied the notion of the UCM to our proposed control model called the “UCM reference feedback control.” To simplify the problem, the present study considered how the redundant joints were controlled to regulate a given target hand position. We considered a conventional method that pre-determined a unique target joint trajectory by inverse kinematics or any other optimization method. In contrast, our proposed control method generates a UCM as a control target at each time step. The target UCM is a subspace of joint angles whose variability does not affect the hand position. The joint combination in the target UCM is then selected so as to minimize the cost function, which consisted of the joint torque and torque change. To examine whether the proposed method could reproduce human-like joint coordination, we conducted simulation and measurement experiments. In the simulation experiments, a three-link arm with a shoulder, elbow, and wrist regulates a one-dimensional target of a hand through proposed method. In the measurement experiments, subjects performed a one-dimensional target-tracking task. The kinematics, dynamics, and joint coordination were quantitatively compared with the simulation data of the proposed method. As a result, the UCM reference feedback control could quantitatively reproduce the difference of the mean value for the end hand position between the initial postures, the peaks of the bell-shape tangential hand velocity, the sum of the squared torque, the mean value for the torque change, the variance components, and the index of synergy as well as the human subjects. We concluded that UCM reference feedback control can reproduce human-like joint coordination. The inference for motor control of the human central nervous system based on the proposed method was discussed.
This study investigated the motion required to carry a cup filled with water without spilling it, which is a common human dexterous task. This task requires the individual to dampen hand vibration while walking. We hypothesize that a reduction in hand jerk and a constant cup angle are required to achieve this task. We measured movements while human subjects carried a cup with water (WW task) and with stones (WS task) using a three-dimensional position measurement system and then analyzed joint coordination. We empirically confirmed that the value of hand jerk and the variance in cup angle in the WW task were smaller than those in the WS task. We used uncontrolled manifold (UCM) analysis to quantify joint coordination corresponding to the motor synergy required to reduce the hand jerk and variance of the cup angle. UCM components, which did not affect the hand jerk and cup angle, were larger than orthogonal components, which directly affected the hand jerk and cup angle in the WW task. These results suggest that there is a coordinated control mechanism that reduces hand jerk and maintains a constant cup angle when carrying a cup filled with water without spilling it. In addition, we suggest that humans adopt a flexible and coordinated control strategy of allowing variance independent of the variables that should be controlled to achieve this dexterous task.
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