Human upper-limb is involved in many daily human activities. The human intention for upper-limb motions can be estimated based on the activation pattern of upper-limb muscles. The upperlimb muscle activities during basic upper-limb motions and the daily upper-limb motions have been studied to enable power-assist robotic exoskeleton systems to estimate human upper-limb motions based on the electromyographic (EMG) signals of related muscles. The relationship between the upperlimb motions and the activity levels of main related muscles concerning the daily upper-limb motions are analyzed in this study. The muscle combinations are identified to separate some basic motions of upper-limb. The EMG signals of identified muscles will be used as input information to design a controller of the upper-limb power-assist robotic exoskeleton systems.

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Weight support can facilitate upper-limb movements, with which the patients may do more and more meaningful exercises earlier in the rehabilitation process. Most rehabilitation devices support the arm against gravity in one way or the other. Weight support can be realized by limiting vertical displacement or applying constant supportive forces which counteract the gravitational pull. Of these, using constant supportive forces is the most natural way to facilitate natural arm move-ments as it allows full freedom of movement and the amount of weight support is scalable to the patients needs. To apply the supporting forces to the arm, endpoint mechanisms and exoskeletons are more complex to build and use then cable suspensions, but offer more control over the movements. Finally, passive weight support is inherently safe, but active systems have enhanced control options and the capability to create training conditions beyond limb weight.

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The presented paper concerns development of a parallel kinematic struc-ture of a palm fingers exoskeleton as a prospective solution for common design difficulties encountered in typical serial-kinematics exoskeletons for the fingers rehabi-litation. The performed state of the art review showed that in spite of over 40 years of development of exoskeletons, considered to be particularly useful as programmable medical devices, there is not achieved the acceptable level of safety and usability.

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This thesis puts forward a fuzzy logic-based control strategy for artificially reproducing the sit-to-stand movement. The aim of this work is to contribute to the machine intelligence being developed for advanced mobility support devices; and specifically, those which are able to assist the mobility impaired user with the sitto-stand task. Three fuzzy logic controllers were designed. The first controller seeks to move the model into the «most stable» configuration. The second seeks to move the model toward the goal configuration (i.e., standing). And the third combines the output from the first two controllers to produce a unified control action. Each controller was implemented and tested in software using Mathwork’s MatlabTM. The results of the software simulation were compared against motion capture data taken from a single healthy male test subject. The automated controller was shown to produce a movement very similar to the natural sit-to-stand movement.

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Background: Parkinson’s disease is a chronic, neurodegenerative disease characterized by gait abnormalities. Freezing of gait(FOG), an episodic inability to generate effective stepping, is reported as one of the most disabling and distressing parkinsonian symptoms. While there are no specific therapies to treat FOG, some external physical cues may alleviate these types of motor dis-ruptions. The purpose of this study was to examine the potential effect of continuous physical cueing using robot-assisted sensori-motor gait training on reducing FOG episodes and improving gait. Methods: Four individuals with Parkinson’s disease and FOG symp-toms received ten 30-minute sessions of robotassisted gait training (Lokomat) to facilitate repetitive, rhythmic, and alternating bilateral lower extremity movements. Outcomes included the FOG-Questionnaire, a clinician-rated video FOG score, spatiotemporal mea-sures of gait, and the Parkinson’s Disease Questionnaire-39 quality of life measure. Results: All participants showed a reduction in FOG both by self-report and clinician-rated scoring upon completion of training. Improvements were also observed in gait veloci-ty, stride length, rhythmicity, and coordination. Conclusions: This pilot study suggests that robot-assisted gait training may be a feasible and effective method of reducing FOG and improving gait. Videotaped scoring of FOG has the potential advantage of provi-ding additional data to complement FOG self-report.

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