To date, various hand exoskeleton devices have been proposed. Most of them, however, contain linkage mechanisms which are relatively weighty. This research presents a novel lightweight hand exoskeleton robot, which is called Advanced Service Robots laboratory (ASR) glove. The glove is shape memory alloy tendon-based actuated which can be utilized for both rehabilitation exercises and assistance for people with hand disability. To realize the proposed glove, after the conceptual design of the proposed robot, the kinematics and force analysis of the system are elaborated analytically and also verified using an experimental prototype of the proposed device. The study shows that the proposed system can flex and extend fingers in their range of motion and grasp objects efficiently. The resulted tool is capable to exert extremely high force levels to grasp objects in various hand configurations. Also, using a 0.25-mm shape memory alloy wire, an angular movement of 80°, 90°, and 70° can be generated at the distal interphalangeal, metacarpophalangeal, and proximal interphalangeal joints of a finger, respectively. In addition, the total grasping force more than 40 N can be produced at the tip of the fingers.

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Human gait is the result of a complex and fascinating cooperation between different joints and segments in the lower extremity. This study aims at investigating the existence of this cooperation or the so-called synergy between the shank motion and the ankle motion. One potential use of this synergy is to develop the high level controllers for active foot prostheses/orthoses. The central point in this paper is to develop a high level controller that is able to continuously map shank kinematics (inputs) to ankle angles and torques (outputs). At the same time, it does not require speed determination, gait percent identification, switching rules, and look-up tables. Furthermore, having those targets in mind, an important part of this study is to determine which input type is required to achieve such targets. This should be fulfilled through using minimum number of inputs. To do this, the Gaussian process (GP) regression has been used to estimate the ankle angles and torques for 11 subjects at three walking speeds (0.5, 1, and 1.5 m/s) based on the shank angular velocity and angle. The results show that it is possible to estimate ankle motion based on the shank motion. It was found that the estimation achieved less quality with only shank angular velocity or angle, whereas the aggregated angular velocity and angle resulted in much higher output estimation quality. In addition, the estimation quality was acceptable for the speeds that there was a training procedure before and when it was tested for the untrained speeds, the estimation quality was not as acceptable as before. The pros and cons of the proposed method are investigated at different scenarios.

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