This work describes the neuro-robotics paradigm: the fusion of neuroscience and robotics. The fusion of neuroscience and robotics, called neuro-robotics, is fundamental to develop robotic systems to be used in functional support, personal assistance and neuro-rehabilitation. While usually the robotic device is considered as a ldquotoolrdquo for neuroscientific studies, a breakthrough is obtained if the two scientific competences and methodologies converge to develop innovative platforms to go beyond robotics by including novel models to design better robots. This paper describes three robotic platforms developed at the ARTS lab of Scuola Superiore Sant’Anna, implementing neuro-robotic design paradigm.

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This paper presents a new exoskeleton with 4 degrees of freedom (DOF) for index finger rehabilitation. The device can generate bi-directional movement for all joints of the finger through cable transmission, which is required for passive and active trainings. With two prismatic kinematic joints in the design, it can accommodate to some extent variety of hand sizes. The kinematic relation between the device joint angles and the corresponding finger joint angles is simple which greatly simplifies the high level motion control. As the motor capability of patients may be different and the range of motion of the finger may change along with the rehabilitation progress, it is important to take the changes into consideration. And the preliminary experiment has shown that the proposed device is capable of accommodating to these varieties.

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This study explores the feasibility of a hybrid system of exoskeletal bracing and multichannel functional electrical stimulation (FES) to facilitate standing, walking, and stair climbing after spinal cord injury (SCI). The orthotic components consist of electromechanical joints that lock and unlock automatically to provide upright stability and free movement powered by FES. Preliminary results from a prototype device on nondisabled and SCI volunteers are presented. A novel variable coupling hip-reciprocating mechanism either acts as a standard reciprocating gait orthosis or allows each hip to independently lock or rotate freely. Rotary actuators at each hip are configured in a closed hydraulic circuit and regulated by a finite state postural controller based on real-time sensor information. The knee mechanism locks during stance to prevent collapse and unlocks during swing, while the ankle is constrained to move in the sagittal plane under FES-only control. The trunk is fixed in a rigid corset, and new ankle and trunk mechanisms are under development. Because the exoskeletal control mechanisms were built from off-the-shelf components, weight and cosmesis specifications for clinical use have not been met, although the power requirements are low enough to provide more than 4 hours of continuous operation with standard camcorder batteries.

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This paper investigates the control algorithm of an exoskeleton for hand rehabilitation, which accomplishes both active and passive control mode. A double closed loop control structure is developed, which consists of position control loop and compensation control loop. The position controller is based on impedance control. The compensation controller is used for compensating the position error caused by deflection of the cable and sheath in the mechanical transmission. To realize the compensation, the spring model is used to represent the elasticity of the cable and sheath. With the proposed method, the maximum joint position error is about 1.5 degree, which satisfies the requirement in hand rehabilitation application. The experimental result demonstrates the validity of the propose method.

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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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