The purpose of this study is to investigate how sensory feedback affects the cognitive strain of grasping using a prosthesis and to develop an efficient method for decreasing the cognitive strain of grasping. We divided a grasping action into two phases, an "approaching phase" and a "grasping phase", and assumed that the tactile feedback affect for the cognitive strain induced by grasping phase and the deep sensory feedback affect for cognitive strain induced by approaching phase. Using dual-task method, we compared the effect of sensory feedback method using strength-changed simulator or using spatially changed stimulators. As a result, we concluded that the sensory feedback can decrease the cognitive strain of grasping action, and the sensory feedback from one strength-changed stimulator is better than the one from spatially changed stimulators to decrease a cognitive strain caused by grasping action.
The usability of a prosthetic hand differs significantly from that of a real hand. Moreover, the complexity of manipulation increases as the number of degrees of freedom to be controlled increases, making manipulation with biological signals extremely difficult. To overcome this problem, users need to select a grasping posture that is adaptive to the object and a stable grasping method that prevents the object from falling. In previous studies, these have been left to the operating skills of the user, which is extremely difficult to achieve. In this study, we demonstrate how stable and adaptive grasping can be achieved according to the object regardless of the user’s operation technique. The required grasping technique is achieved by determining the correlation between the motor output and each sensor through the interaction between the prosthetic hand and the surrounding stimuli, such as myoelectricity, sense of touch, and grasping objects. The agents of the 16-DOF robot hand were trained with the myoelectric signals of six participants, including one child with a congenital forearm deficiency. Consequently, each agent could open and close the hand in response to the myoelectric stimuli and could accomplish the object pickup task. For the tasks, the agents successfully identified grasping patterns suitable for practical and stable positioning of the objects. In addition, the agents were able to pick up the object in a similar posture regardless of the participant, suggesting that the hand was optimized by evolutionary computation to a posture that prevents the object from being dropped.
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