Robotic prostheses are expected to allow amputees greater freedom and mobility. However, available options to control transhumeral prostheses are reduced with increasing amputation level. In addition, for electromyography-based control of prostheses, the residual muscles alone cannot generate sufficiently different signals for accurate distal arm function. Thus, controlling a multi-degree of freedom (DoF) transhumeral prosthesis is challenging with currently available techniques. In this paper, an electroencephalogram (EEG)-based hierarchical two-stage approach is proposed to achieve multi-DoF control of a transhumeral prosthesis. In the proposed method, the motion intention for arm reaching or hand lifting is identified using classifiers trained with motion-related EEG features. For this purpose, neural network and k-nearest neighbor classifiers are used. Then, elbow motion and hand endpoint motion is estimated using a different set of neural-network-based classifiers, which are trained with motion information recorded using healthy subjects. The predictions from the classifiers are compared with residual limb motion to generate a final prediction of motion intention. This can then be used to realize multi-DoF control of a prosthesis. The experimental results show the feasibility of the proposed method for multi-DoF control of a transhumeral prosthesis. This proof of concept study was performed with healthy subjects.

The number of patients suffering from colorectal cancer (CRC) has been increasing. CRC is known to be curable if detected and treated early. Colonoscopy is currently one of the best screening methods for CRC because it can observe and treat disorders in the large intestine. However, operating the colonoscope is technically demanding for doctors because the insertion of the instrument into the large intestine requires considerable training and skill. To address this issue, we propose a novel self-propelled robot with flexible paddles for the intestinal tract. In this device, the torque is transmitted from a motor outside the patient body to a worm gear at the tip of the colonoscope by a flexible shaft. The worm gear is engaged with two spur gears, and flexible paddles fixed to these spur gears contact the wall of the large intestine to provide the propulsive force. We constructed a force transmission model of the robot to confirm the suitability of the design. The prototype of the self-propelled robot was fabricated by a 3D printer, and its locomotion in a simulated rubber intestine was evaluated. The velocity of the robot was faster than the required speed of 6.5 mm/s. The propulsive force was approximately 1 N; thus, the effectiveness of the robotic principle was confirmed. The mechanical locomotion design, its fabrication, and analysis results are reported in this paper.

Recently, brain–computer interfaces, combined with feedback systems and goal-oriented training, have been investigated for their capacity to promote functional recovery after stroke. Accordingly, we developed a brain–computer interface-triggered robotic hand orthosis that assists hand-closing and hand-opening for post-stroke patients without sufficient motor output. In this system, near-infrared spectroscopy is used to monitor the affected motor cortex, and a linear discriminant analysis-based binary classifier estimates hand posture. The estimated posture then wirelessly triggers the robotic hand orthosis. For better performance of the brain–computer interface, we tested feature windows of different lengths and varying feature vector compositions with motor execution data from seven neurologically intact participants. The interaction between a feature window and a delay in the hemodynamic response significantly affected both classification accuracy (Matthew Correlation Coefficient) and detection latency. The ‘preserving channels’ feature vector was able to increase accuracy by 13.14% and decrease latency by 29.48%, relative to averaging. Oxyhemoglobin combined with deoxyhemoglobin improved accuracy by 3.71% and decreased latency by 6.01% relative to oxyhemoglobin alone. Thus, the best classification performance resulted in an accuracy of 0.7154 and a latency of 2.8515 s. The hand rehabilitation system was successfully implemented using this feature vector composition, which yielded better classification performance.