This thesis details the design and development of an intelligent prosthetic hand based on hybrid DC and Shape Memory Alloy (SMA) actuation and controlled by only two myoelectric sensors. A prosthesis as a tool makes no pretence of trying to replace the lost limb physiologically but it works as an aid to help provide some of the lost functions and is an interchan-geable device worn and used as needed. Much research has been carried out to develop artificial prosthetic hands with capabilities similar to the human hand. The human hand is a very complex grasping tool, that can handle objects of different size, weight and shape; however, they are far from providing its manipu-lation capabilities. This is for many different reasons, such as active bending is limited to two or three joints and user-unfriendliness. These limitations are present in commercial prosthetic hands, together with others always complained about by patients and amputees, such as inability to provide enough grasping functionality and heavy weight. Several robotic and anthropomorphic hands may have sufficient active de-grees of freedom to allow dexterity comparable to that of the human hand. Unfortunately, they cannot be used as prostheses due to their physical characteristic that poses several serious limitations on human- and interaction. Hence, the motivation for this research is to investigate the use of a hybrid actuation mechanism in the design and development of an intelligent prosthetic hand. This work highlights user- riendliness and involves a proper mechanical design with more active degrees of freedom and incorporating an intelligent control system. A system with a finger prototype is considered. Testing through simulation and physical models reveals a number of limitations. A hybrid actuation system, to increase the finger active degrees of freedom is therefore developed, with a mechanism consisting of DC and SMA actuators. Besides, only two myoelectrodes channels (enhancing the user-friendliness of the device) are used for the system control input signal. Two novel features are developed in the new prosthetic hand. Firstly, its hybrid actuation mechanism has the advantage of increasing the active degrees of freedom; secondly, using only two myoelectric sensors has potential for controlling more than three patterns of fingers movements. By using artificial neural network patterns classification technique, three and five patterns of wrist joint movement corresponding to finger movement can be recognised as more than 85% correct and further-more, seven as 70% correct.

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Warfighters who suffer combat, training or accidental injuries that damage their sensory capabilities or mobility have great difficulty returning to productive lifestyles once healed from the initial trauma. This project advanced technologies for non-invasive sensory and mobility augmentation in order to allow these individuals to regain hope and social, productive lifesty-les. We addressed the needs of these wounded warriors through evaluation of current sensory substitution technology, identification of specific injured servicemember requirements for sensory augmentation, deve-lopment and demonstration of new sensory substitution techniques and application of these techniques to mobility enhancing devices to improve teir usability.

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The Dampace exoskeleton combines functional exercises resembling activities of daily living with impairment-targeted force-coordination training. The goal of this paper is to evaluate the performance of the Dampace. In the design, the joint rotations are decoupled from the joint translations; the robot axes align themselves to the anatomical axes, overcoming some of the traditional difficulties of exoskeletons. Setup times are reduced to mere minutes and static reaction forces are kept to a minimum. The Dampace uses hydraulic disk brakes, which can resist rotations with up to 50 N m and have a torque bandwidth of 10 Hz for multisine torques of 20 N m. The brakes provide passive control over the movement; the patients’ movements can be selectively resisted, but active movement assistance is impossible and virtual environments are restricted. However, passive actuators are inherently safe and force active patient participation. In conclusion, the Dampace is well suited to offer force-coordination training with functional exercises.

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An electromyography (EMG) signal based fuzzy-neuro control method is proposed in this paper for a human upper-limb motion assist exo-skeleton robot. The upper-limb exoskeleton robot (named W-EXOS) assists the motions of human forearm pronation/supination, wrist flexion/extension and ulnar/radial deviation. The paper presents the EMG signal based fuzzy-neuro control method with multiple fuzzy-neuro controllers and the adaptation method of the controllers. The skin surface EMG signals of muscles in the forearm of the exoskeleton user and the hand force/forearm torque measured from the sensors of the exoskeleton robot are used as input information for the controllers. Fuzzy-neuro control method, which is a combination of flexible fuzzy control and adaptive neural network cont-rol, has been applied to realize the natural and flexible motion assist. In the control method, multiple fuzzy-neuro controllers are applied, since the muscle activation levels change in accordance with the angles of motions. The control method is able to adapt in accordance with the changing EMG signal le-vels of different users. Experiments have been performed to evaluate the propo-sed control method.

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To guide development of robotic lower limb exoskeletons,it is necessary to understand how humans adapt topowered assistance. The purposes of this study were to quantify joint moments while healthy subjects adapted to a robotic ankle exoskeleton and to determine if the period of motor adaptation is dependent on the magnitude of robotic assistance. The pneumatically powered ankle exoskeleton provided plantar flexor torque controlled by the wearer’s soleus electromyography (EMG). Eleven na¨ıve individuals completed two 30-min sessions walking on a split-belt instrumented treadmill at 1.25m/s while wearing the ankle exoskeleton. After two sessions of practice, subjects reduced their soleus EMG activation by 36% and walked with total ankle moment patterns similar to their unassisted gait (r2=0.9870.02, THSD, p40.05). They had substantially different ankle kinematic patterns compared to their unassisted gait (r2=0.7970.12,THSD, po0.05). Not all of the subjects reached a steady-state gait pattern within the two sessions, in contrast to a previous study using a weaker robotic ankle exoskeleton (Gordon and Ferris,2007). Our results strongly suggest that humans aim for similar joint moment patterns when walking with robotic assistance rather than similar kinematic patterns. In addition, greater robotic assistance provided during initial use results in a longer adaptation process than lesser robotic assis-tance.

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