The objective of this study was to determine the utility of a passive gravity-balanced arm orthosis, the Wilmington roboticexoskeleton (WREX), for patients with neuromuscular diseases. The WREX, a four-degrees-of-freedom functional orthosis, is energized by rubber bands to eliminate gravity and is attached to the wheelchair. The development and clinical testing of WREX is described in this report. Seventeen patients (14 boys and 3 girls) with muscular disabilities participated in the study. Ages ranged from 4 to 20 years. Criteria for inclusion included a weakened arm, use of a wheelchair, the ability to grasp and release objects, and the ability to provide feedback on device use. Testing consisted of administering the Jebsen test of hand function without WREX and then testing again after approximately two weeks of wearing the WREX orthosis. The timed results of each task within the test then were compared. Specific tasks related to vertical movement required less time to perform with the WREX. A large number of subjects were able to perform the Jebsen tasks with the WREX, where they were unable to perform the task without the WREX. Patients can benefit from WREX because it increases their performance in daily living activities and makes many tasks possible. The range-of-motion in the patients’ arms increased considerably, while the time required to complete some of the Jebsen test tasks decreased. Most patients were very receptive to WREX, although a few were ambivalent.

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In this paper, we propose a multipurpose sensing system and fast phase analysis algorithms to predict and estimate behaviors of human being in advance. The sensing system is designed lightly and simply to minimize data acquisition errors and composed of six absolute encoders, eight FSR sensors, and FPGA board for data processing. The eight FSR sensors are attached at four points of sole of each foot where human weight is largely concentrated. The leg detaching structure that connects up with six light steel links is tuned according to the fittest condition of a subject. Thus, each link can be adjusted according to subject’s body length. The phase analysis algorithms have two major categories for analyzing walk behaviors and stand behaviors, and four subordinate phases for standing, lift, swing, and landing. This algorithm considers standing motion and two leg behaviors. Experimental implementations are performed successfully to estimate various human gaits. This acquisition data are efficient for locomotion trajectory generation of biped robots and exoskeleton systems

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The Lower-limb Power Assisted Robot is a typical exoskeleton power assistance device, which is worn on the user’s lower extremities. It can help the elderly and the infirm walk independently. According to the characteristics of human lower extremity joints, a robot with four-bar knee joint whose pelvis and knee joint are driven by the motor-driven screw-nut body is designed. Employing the Denanit-Hartenberg (D-H) method, the robot kinematics is analyzed, the equation of which was established. Based upon the law of human gait, the robot’s trajectory planning is accomplished. According to the dSPACE system, the hardware-in-the-loop experiment system of the single leg is established; the step experiment, sine response and walking test with load are accomplished. Experimental results show that the robot can achieve the gait law and has strong load capacity.

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It is thought that the dynamic motions with lower limbs such as walking can be made more effectively by moving each joint along to its natural frequency. If the natural frequency were able to be modified purposefully, the effective motion could be performed at any angular frequency around joint. We realized that with the exoskeleton-type powered suit, HAL (Hybrid Assistive Leg)-3 we developed for walking aid. In this research, we considered the operator’s leg as pendulum model, identified the physical parameters around human’s knee joints, tried to adjust the impedance, and applied that to pendular movement of leg. The effectiveness of adjusting the natural frequency in power assist control can be confirmed through the experiments evaluated with myoelectricity.

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The effectiveness of a robotic training device was evaluated in a 24-year-old male, cervical level four, ASIA Impairment Scale D injury. Robotic training of both upper extremities was provided for three hr/day for ten consecutive sessions using the RiceWrist, an electrically-actuated forearm and wrist haptic exoskeleton device that has been designed for rehabilitation applications. Training involved wrist flexion/extension, radial/ulnar deviation and forearm supination/pronation. Therapy sessions were tailored, based on the patient’s movement capabilities for the wrist and forearm, progressed gradually by increasing number of repetitions and resistance. Outcome measures included the ASIA upper-extremity motor score, grip and pinch strength, the Jebsen-Taylor Hand Function test and the Functional Independence Measure. After the training, improvements were observed in pinch strength, and functional tasks. The data from one subject provides valuable information on the feasibility and effectiveness of robotic-assisted training of forearm and hand functions after incomplete spinal cord injury.

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