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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We present an overview of the work devoted to exoskeletons which has been performed for the last seven years at TU Berlin. Three different types of exoskeleton devices have been developed: hand and finger exoskeletons for rehabilitation, a leg exoskeleton for motion support, and an arm exoskeleton for multimodal human-computer interaction. The research has been focused on different types of control strategies and algorithms as well as on the implementation of applications. The main part of this work is devoted to using electrical muscle signals for the control systems. We present the concepts for design and application, review the main algorithms and show experimental results and first experience with applications.

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The design of a wearable robotic exoskeleton needs to consider the interaction, either physical or cognitive, between the human user and the robotic device. This paper presents a method to analyse the interaction between the human user and a unilateral, wearable lower-limb exoskeleton. The lower-limb exoskeleton function was to compénsate for muscle weakness around the kneejoint. It isshown that the cognitive interaction is bidirectional; on theone hand, the robot gathered information from the sensors in order to detect human actions, such as the gait phases, but the subjects also modified their gait patterns to obtain the desired responses from the exoskeleton. The results of the two-phase evaluation of learning with healthy subjects and experiments with a patient case are presented, regarding the analysis of the interaction, assessed in terms of kinematics, kinetics and/or muscle recruitment. Human-driven response of the exoskeleton after training revealed the improvements in the use of the device, while particular modifications of motion patterns were observed in healthy subjects. Also, endurance (mechanical) tests provided criteria to perform experiments with one post-polio patient. The results with the post-polio patient demónstrate the feasibility of providing gait compensation by means of the presented wearable exoskeleton, designed with a testing procedure that involves the human users to assess the human-robot interaction.

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In recent years, the rate of aged people is continuously increasing in Japan: in ten years, around 30% of the Japanese population will be aged more than 65 years old. These people often need some help from caretakers, and this causes a problem for the caretakers: it takes a very big burden for caretakers, for example, to lift people from their bed, when they need to go to toilets. So, a system to help caretakers would be needed: This is a master-slave unit exoskeleton, which helps the caretaker while loading the patient. It is moved by a Hydraulic Bilateral Servo system, whose master cylinders are moved by servo-motors. Presently, the conceptions of the body and hardware are finished. We are developing the servo-loop system and the driving software for the system, and more specially the pulse generation software for the motor of the master part of the exoskeleton. We will be able to discuss about the driving of the entire robotic-type exoskeleton system with a computer, through a PCMCIA interface system.

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