This paper presents a novel design for a 4 degree of freedom pneumatically-actuated upper-limb rehabilitation device. BONES is based on a parallel mechanism that actuates the upper arm by means of two passive, sliding rods pivoting with respect to a fixed structural frame. Four, mechanically-grounded pneumatic actuators are placed behind the main structural frame to control shoulder motion via the sliding rods, and a fifth cylinder is located on the structure to control elbow flexion/extension. The device accommodates a wide range of motion of the human arm, while also achieving low inertia and direct-drive force generation capability at the shoulder. A key accomplishment of this design is the ability to generate arm internal/external rotation without any circular bearing element such as a ring, a design feature inspired by the biomechanics of the human forearm. The paper describes the rationale for this device and its main design aspects including its kinematics, range of motion, and force generation capability.

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Gait training of stroke survivors can help in retraining their muscles and improving their gait pattern. Robot assisted gait training (RAGT) was developed for stroke survivors using ALEX and force-field controller, which use assist-as-needed paradigm for rehabilitation. In this paradigm undesirable gait motion is resisted and assistance is provided towards the desirable motion. The force-field controller achieves this paradigm by applying forces at the foot of the subject. Two stroke survivors participated in a 15-day gait training study each with ALEX. The results show that by the end of the training the gait pattern of the patients was improved towards healthy subjects gait pattern. Improvement is seen as increase in the size of the patientspsila gait pattern, increase in knee and ankle joint excursions and increase in their walking speed on the treadmill.

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This paper presents multi-criteria design optimization of parallel mechanism based force feedback exoskeletons for human forearm and wrist. The optimized devices are aimed to be employed as a high fidelity haptic interfaces. Multiple design objectives are discussed and classified for the devices and the optimization problem to study the trade-offs between these criteria is formulated. Dimensional syntheses are performed for optimal global kinematic and dynamic performance, utilizing a Pareto front based framework, for two spherical parallel mechanisms that satisfy the ergonomic necessities of a human forearm and wrist. Two optimized mechanisms are compared and discussed in the light of multiple design criteria. Finally, kinematic structure and dimensions of an optimal exoskeleton are decided.

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Background: In virtual reality (VR) systems, the user’s finger and hand positions are sensed and used to control the virtual envi-ronments. Direct biocontrol of VR environments using surface electromyography (SEMG) signals may be more synergistic and uncons-training to the user. The purpose of the present investigation was to develop a technique to predict the finger joint angle from the surface EMG measurements of the extensor muscle using neural network models.
Methodology: SEMG together with the actual joint angle measurements were obtained while the subject was performing flexion-exten-sion rotation of the index finger at three speeds. Several neural networks were trained to predict the joint angle from the para-meters extracted from the SEMG signals. The best networks were selected to form six committees. The neural network committees were evaluated using data from new subjects.
Results: There was hysteresis in the measured SMEG signals during the flexion-extension cycle. However, neural network committees were able to predict the joint angle with reasonable accuracy. RMS errors ranged from 0.085 ± 0.036 for fast speed finger-extension to 0.147 ± 0.026 for slow speed finger extension, and from 0.098 ± 0.023 for the fast speed finger flexion to 0.163 ± 0.054 for slow speed finger flexion.
Conclusion: Although hysteresis was observed in the measured SEMG signals, the committees of neural networks were able to predict the finger joint angle from SEMG signals.

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Background: It is the purpose of this article to iden-tify and review criteria that rehabilitation technology should meet in order to offer arm-hand training to stroke patients, based on recent principles of motor learning. Methods: A literature search was conducted in PubMed, MEDLINE, CINAHL, and EMBASE (1997–2007). Results: One hundred and eighty seven scientific pa-pers/book references were identified as being relevant. Rehabilitation approaches for upper limb training after stroke show to have shifted in the last decade from being analytical towards being focussed on envi-ronmentally contextual skill training (task-oriented training). Training programmes for enhancing motor skills use patient and goal-tailored exercise schedules and individual feedback on exercise performance. Therapist criteria for upper limb rehabilitation technology are suggested which are used to evaluate the strengths and weaknesses of a number of current technological systems. Conclusion: This review shows that technology for supporting upper limb training after stroke needs to align with the evolution in rehabi-litation training approaches of the last decade. A major challenge for related technological developments is to provide engaging patient-tailored task oriented arm-hand training in natural environments with pa-tient-tailored feedback to support (re) learning of motor skills.

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