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.

Brain–computer interfaces are emerging as an important research area and are intended to create an understanding between a computer and the human brain to ensure that robot–human interactions become more intuitive and user-friendly. However, encoding of brain information to derive the intended motion of the user in real time continues to present a problem with respect to the control of wearable robots with multiple degrees of freedom. In this study, a new approach to control several degrees of freedom in a wearable robot is proposed and its feasibility is studied by estimating the user’s motion intention in real time, in terms of the user’s intended tasks to perform, by using electroencephalography signals measured from the scalp of the user. A time-delayed feature matrix is introduced to provide inputs to neural network and support vector machine-based classifiers that harvest the dynamic nature of the electroencephalography signals for motion intention prediction. The experimental results indicate the effectiveness of the proposed methodology in the estimation of user motion intention, in terms of intended task to perform.

Gastrointestinal cancer, when detected early, is treated by accessing the lesion through the natural orifice using flexible endoscopes. However, the limited degree-of-freedom (DOF) of conventional treatment devices and the narrow surgical view through the endoscope demand advanced techniques. In contrast, multi-DOF forceps systems are an excellent alternative; however, these systems often involve high fabrication costs because they require a large number of micro-parts. To solve this problem, we designed compact multi-DOF endoluminal forceps with a monolithic structure comprising compliant hinges. To allow an efficient stress dispersion at the base end when the hinge bends, we proposed a novel design method to obtain the hinge parameters using the beam of uniform strength theory. This method does not involve a high computational cost. The results show that the improved design with a variable hinge thickness can reduce the maximum bending stress, dispersing the stress in a larger area than that of the previous design considering a constant thickness of the hinge. Moreover, the experiments conducted in a prototype confirm that the radius of the curvature was significantly improved. The proposed method could aid in designing other continuum robots relying on compliant hinges.

Minimally invasive surgery (MIS) is a viable alternative to general surgery with distinct advantages. Robotically assisted MIS, has been demonstrated to achieve higher accuracy and repeatability in comparison with those of manual procedures. Despite these advantages, owing to the nature of some surgical procedures in which dexterous tissue manipulations in deep narrow areas of the human body are necessary, there is a need for further miniaturized tools with smaller bending radii. To cater to this requirement, this study proposes a new compliant mechanism based surgical robotic forceps. It can generate four degrees of freedom at the tip of the forceps including two bending motions in two perpendicular axes, grasping and rotation. A better combination of the stress distribution through the elastic material, grasping force, and range of motion was determined based on a series of finite element analyses. In addition, the manufactured prototype underwent a series of laboratory experiments to evaluate its effectiveness. Details of the mechanism, finite element analysis, prototype implementation, and evaluations are presented in this paper. GRAPHICAL ABSTRACT