For human-machine cooperation and interfaces, the need to accurately estimate hand and finger postures and movements without restricting action is increasing. Clarifying the relationship between hand and finger postures and musculoskeletal activity will help understand human motor function and biological mechanism modeling. This study aimed to develop a system to estimate hand-finger postures from the tissue distribution of a wrist section obtained by using electrical impedance tomography (EIT) and reveal the influence of the musculoskeletal features on estimation performance. Specifically, this study proposed a method to classify hand postures using a Support Vector Machine (SVM) based on the reconstructed relative conductivity distribution of a wrist cross-section using EIT, and compared its accuracy with the conventional classification methods using original voltage data, i.e., EIT signals. In the experiment, a band with 16 electrodes was placed around the wrists of 6 participants and EIT signals were obtained for 7 different hand postures. The reconstructed musculoskeletal activity-based classification performed better than EIT voltage signal-based classification, achieving the highest accuracy of 96.1%. The spatial distribution of images from the reconstruction of musculoskeletal activity also reflected the positions of the flexors and extensors, with different activities observed for each posture.

In this study, we propose a method using gyro torque of two rotating discs as a new approach for rehabilitation robots. The robot has two rotating discs and two servomotors, and by combining their rotary motions, it is possible to generate strong torque with respect to one axis. Compared to rehabilitation robots using general large servo motors, this method is superior joints are not as the restricted, and rotating parts are not exposed. As a preliminary experiment, we show the combination and cancellation of two gyro torques. A gyro torque generator was manufactured, and the output torque was measured. Based on the measurement results, it was confirmed that the two gyro torques were correctly combined and canceled. Next, to evaluate the effectiveness of the method, we developed a robot capable of rehabilitation of the elbow joint. Elbow joint rehabilitation support was performed on nine subjects using the developed robot. Using an electromyograph, we compared the difference in action potentials when the device was worn. We confirmed the effectiveness of this method from the measurement results.