Dataglove is an important interface device for dexterous interactions with virtual environments, which should be calibrated before used. Calibration is the initialization of dataglove and establishes a mapping from the sensor values to the joint angles of an articulated hand. For the exoskeleton dataglove, we build a measurement model of the finger flexion angle for human hand based on the theory of four-bar-linkage motion stabilization. The four postures obtained by the standard block are used to calibrate the parameters of the dataglove that are solved by the genetic algorithms (GA). The calibration based on GA has competitive merits, namely, insusceptibility to getting trapped in local extremum and no need of initial values. Experiment verifies that the method is simple and the result is accurate

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This paper presents a new design of an arm exoskeleton using shape memory alloy wires and springs embedded in fabric and taking the form of a sleeve. The device is intended to facilitate everyday life tasks by providing the user with an easily operated lift aiding device. The “SMA Aiding Sleeve” will bring extra lifting power to various classes of users (medical and non-medical). This paper presents the details of the design including the hardware and control scheme. The prototype preliminary tests provide encouraging results in terms of controllability and actuation. The results demonstrate the feasibility of position control.

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Augmentation of human power is an important problem in the design of human assist devices such as an exoskeleton system. The overall system consisting of a human and an assistive device is a parallel control system that includes two controllers: the human brain and the assistive device controller. It has been postulated in a previous work that the motion control system of a human is regarded as a feedback control loop that consists of the brain, muscles, and the dynamics of the extended human body, and that the brain function is modeled as a control algorithm amplified by a fictitious gain. The fictitious gain compensates for characteristic changes in the muscle and dynamics. If the human is physically impaired or subjected to demanding work, the fictitious gain is increased to assist the human. The assistive device is controlled to augment human power, the amount of which is equivalent to the one attained by the fictitious gain. This paper presents the design and verification of an assistive device for elbow joint and the controller for the device based on the fictitious gain concept.

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Body weight supported treadmill training (BWSTT) has been confirmed to be an effective gait rehabilitation therapy for patients with locomotor dysfunction of the lower limbs. Powered Gait Orthosis (PGO) is a pair of powered mechanical legs in exoskeleton structure, which can guide the patient’s legs to move in a preprogrammed physiological gait pattern during BWSTT. A prototype of single-leg PGO has been designed and constructed. It has linear actuators at hip and knee joints; it is also instrumented with sensors and encoders. To realize the physiological gait movement of the single-leg PGO, a control platform, a safe protection device, and a set-point gait motion control method have been developed. The effectiveness of the proposed set-point gait motion control method is confirmed by the preliminary experimental results..

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Several regression models have been proposed for estimation of isometric joint torque using surface electromyography (SEMG) sig-nals. Common issues related to torque estimation models are degradation of model accuracy with passage of time, electrode displace-ment, and alteration of limb posture. This work compares the performance of the most commonly used regression models under these circumstances, in order to assist researchers with identifying the most appropriate model for a specific biomedical application. Methods Eleven healthy volunteers participated in this study. A custom-built rig, equipped with a torque sensor, was used to mea-sure isometric torque as each volunteer flexed and extended his wrist. SEMG signals from eight forearm muscles, in addition to wrist joint torque data were gathered during the experiment. Additional data were gathered one hour and twenty-four hours following the completion of the first data gathering session, for the purpose of evaluating the effects of passage of time and electrode dis-placement on accuracy of models. Acquired SEMG signals were filtered, rectified, normalized and then fed to models for training. Re-sults It was shown that mean adjusted coefficient of determination values decrease between 20%-35% for different models after one hour while altering arm posture decreased mean values between 64% to 74% for different models. Conclusions Model estimation accu-racy drops significantly with passage of time, electrode displacement, and alteration of limb posture. Therefore model retraining is crucial for preserving estimation accuracy. Data resampling can significantly reduce model training time without losing estima-tion accuracy. Among the models compared, ordinary least squares linear regression model (OLS) was shown to have high isometric tor-que estimation accuracy combined with very short training times

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