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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. [ABSTRACT FROM AUTHOR]

Highly rigid surgical robots are capable of precise positioning; however, there is a risk of injury to the surrounding organs owing to undesired contact. To solve this problem, surgeons can change their stiffness according to the desired motion by contracting and relaxing the muscles. Therefore, surgical robots that can change their stiffness according to their application, similar to a surgeon, are useful in improving safety. However, existing variable stiffness actuators cannot easily achieve a wide variable stiffness range while maintaining a small size and lightweight, which are critical factors for surgical robots. This study presents the design, fabrication, and evaluation of a variable stiffness actuator that is compact and provides a wide range of variable stiffness, with elastic elements arranged in a circumferential direction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Impairment of hand function greatly affects the independence of a human being. Proper assessment of hand function before and after any treatment for functional restoration is important to decide better treatment strategies. Despite traditional techniques of hand function evaluation, individual joint based assessment is vital to better track the details of the hand function. Current clinical assessments with goniometers are labour intensive, cumbersome and highly depend on the skill level of the practitioner. This study introduces an active range of motion (AROM) measurement system to measure individual range of motion of finger joints using an optical sensor. The proposed method is highly efficient, and the results demonstrated that the measurements are instant, repeatable and can successfully be employed in a clinical setup for patient evaluations.Clinical Relevance-Closely working with clinician to develop rehabilitation systems, we have identified that the assessment of patient hand functions is time consuming, and accuracy can be depended on the skill level of the practitioner in measuring joint range of motions (ROM). System introduced in this study can measure the joint AROMs instantly and independent of the practitioner's skill level and hence can provide a reliable, repeatable assessment of patient's hand function.

An anthropomorphic transhumeral robotic arm prosthesis is proposed in this study. It is capable of generating fifteen degrees-of-freedom, seven active and eight passive. In order to realize wrist motions, a parallel manipulator-based mechanism is proposed. It simulates the human anatomical structure and generates motions in two axes. The hand-of-arm prosthesis consists of under-actuated fingers with intrinsic actuation. The finger mechanism is capable of generating three degrees of freedom, and it exhibits the capability of adjusting the joint angles passively according to the geometry of the grasping object. Additionally, a parameter to evaluate finger mechanisms is introduced, and it measures the adoptability of a finger mechanism. In order to verify the mechanism's efficacy in terms of motion generation, motion simulation and kinematic analysis were carried out. Results demonstrated that the mechanisms are capable of generating the required motions., (Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.)