Powered robotic exoskeletons for assistance of human locomotion are currently under development for military and medical applications. However, the energy requirements for such devices is important and becomes an obstacle for practical applications.

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An international institution proposes an innovative exoskeleton that includes a kinematic chain of parallel pivots and comprises a shoulder, a curved and a hand. It has six degrees of freedom, with a support carried by a human operator with rigid front and rear plates. The shoulder is fixed to the front plate and the hand is fixed to a rigid glove on the operator’s hand. The active areas are controlled by flexible tendons of cables activated by control units disposed on the rear rigid plate.

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Background: Surface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also po-tentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern re-cognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work.
Methods: Both sEMG data from four muscles of the forearm and wrist torque were collected from eight volunteers by using a custom-made testing rig. The features that were extracted from the sEMG signals included root mean square (rms) EMG amplitude, autoregres-sive (AR) model coefficients and waveform length. Support Vector Machines (SVM) was employed to extract classes of different force intensity from the sEMG signals. After assessing the off-line performance of the used classification technique, the WEP was used to validate in real-time the proposed classification scheme.
Results: The data gathered from the volunteers were divided into two sets, one with nineteen classes and the second with thirteen classes. Each set of data was further divided into training and testing data. It was observed that the average testing accuracy in the case of nineteen classes was about 88% whereas the average accuracy in the case of thirteen classes reached about 96%. Clas-sification and control algorithm implemented in the WEP was executed in less than 125 ms.
Conclusions: The results of this study showed that classification of EMG signals by separating different levels of torque is possi-ble for wrist motion and the use of only four EMG channels is suitable. The study also showed that SVM classification technique is suitable for real-time classification of sEMG signals and can be effectively implemented for controlling an exoskeleton device for assisting the wrist.

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Rehabilitation environments combining virtual reality with everyday motor tasks can promote recovery from neurological illness, such as stroke. Tactile devices, providing physical stimulation to the skin, may improve motor retraining. While many tactile devices have been reported, there is a distinct paucity of studies evaluating how they are perceived. This multidisciplinary research has investigated three tactile devices (vibration motors, a motor-driven ‘squeezer’, and shape memory alloys) for providing a realistic sensation of static interaction with virtual objects. These devices have been iterati-vely redesigned and qualitatively evaluated with healthy human participants. This paper presents the devices, their evaluation, and iterative redesign.

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In this paper, a technique to generate the wrench-closure workspace for general case completely restrained cable driven parallel mechanisms is proposed. Existing methods can be classified as either numerically or analytically based approaches. Numerical techniques exhaustively sample the task space, which can be inaccurate due to discretisation and is computationally expensive. In comparison, analytical formulations have higher accuracy, but often provides only qualitative workspace information. The proposed hybrid approach combines the high accuracy of the analytical approach and the algorithmic versatility of the numerical approach. Additionally, this is achieved with significantly lower computational costs compared to numerical methods. It is shown that the wrench-closure workspace can be reduced to a set of univariate polynomial inequalities with respect to a single variable of the end-effector motion. In this form, the workspace can then be efficiently determined and quantitatively evaluated. The proposed technique is described for a 3-DOF and a 6-DOF cable driven parallel manipulator. A detailed example in workspace determination using the proposed approach and comparison against the conventional numerical approach are presented.

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