The gait of every adult is unique and expected to be ingrained within the neuromuscular system. The major scientific question that we ask in this paper if it is possible to alter the gait of healthy individuals using special purpose design of robots and training paradigms. This paper describes novel experimental results with an active leg exoskeleton (ALEX) and a force-field controller (FFC) developed for neuromotor training of gait and rehabilitation of patients with walking disabilities. ALEX is a motorized leg orthosis having a total of 7 DOFs with hip and knee actuated in the sagittal plane. The FFC applies forces on the foot to help the leg move on a desired trajectory. The interaction forces between the subject and the orthosis are designed to be ??assist-as-needed?? for safe and effective gait training. Simulations and experimental results with the FFC are presented. Experiments have been performed on six healthy subjects walking on a treadmill. It was shown that a healthy subject could be retrained in about 45 min with ALEX to walk on a treadmill with a considerably altered gait. In the coming months, this powered orthosis will be used for gait training of stroke patients.

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There is a lack of enough attention to the patients’ hand posture. When robots implement assistive training, patients are often asked to grip a handle tightly, which may induce strong hand muscle contractions with the hand at an abnormal posture. If we ignore proper control of the muscle tension of subject’s hand, the flexibility of hand/fingers may decrease and the robot training may potentially cause abnormal muscle tone. On the other hand, since the patient’s fingers are already in an abnormal posture, properly aligning the joint and making the robot easy to attach is important. However, an existing multi-DOF hand exoskeleton systems may be difficult for the patients to put it on. the purpose of this paper was to design a patient-friendly mechanism drive by a single motor and attached to the whole-arm rehab robot for the hand and finger opening and closing functions. Using our 8+2 DOF whole-arm robot including this hand opening and closing mechanism, a novel integrated rehabilitation is performed including 1) strenuous stretching of the MCP-thumb joints and other spastic joints of the upper limb; 2) active assistive exercise is provided to improve voluntary neuromuscular control by using robot-computer games with a griping task; 3) outcome evaluations including the cross-coupling torques between the fingers/thumb and the other joints during hand opening/closing and other upper limb movements.

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Task-oriented repetitive movement can improve movement performance in patients with neurological lesions. The application of robotics can serve to assist, enhance, evaluate and document rehabilitation of movements. ARMin is a robot for arm therapy applicable to the arm training in clinics. It has an exoskeleton structure and is equipped with position and force sensors. Our latest version ARMin II has six degrees of freedom. The mechanical structure, the actuators, and the sensors of the robot are optimized for patient-cooperative control strategies based on impedance and admittance architectures. The device can work in three therapy modes: passive mobilization, game therapy, and task-oriented training. This paper presents the technical components of the new version ARMin II, the therapy modes, the control strategy for a new example of a game therapy, and clinical results of a pilot study with 11 chronic stroke patients and of single case studies conducted with three chronic stroke patients.

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A power-assist exoskeleton robot, which is directly attached to the user’s body and assist the motion in accordance with the user’s intension, is one of the most effective human assist robots for the physically weak persons. Many studies on power-assist robots have been carried out to help the motion of physically weak persons such as disabled, injured, and/or elderly persons. EMG-based control (i.e., control based on the skin surface electromyogram (EMG) signals of the user) is one of the most effective control methods for the power-assist robots, since EMG signals of user’s muscles directly reflect the user’s motion intension. However, the EMG-based control is not easy to be realized because of many reasons. The paper presents an effective human motion prediction method from the EMG signals using a neuro-fuzzy technique for the control of power-assist exoskeleton robots.

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In this paper we investigate the key factors associated with the realization of a hand exoskeleton that could be embedded in an astronaut glove for EVA (Extra Vehicular Activities). Such a project poses several and varied problems, mainly due to the complex structure of the human hand and to the extreme environment in which the glove operates. This work provides an overview of existing exoskeletons and their related technologies and lays the ground for the forthcoming prototype realization, by presenting a preliminary analysis of possible solutions in terms of mechanical structure, actuators and sensors.

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