Numerous researches have been carried out to use robot in the area of stroke rehabilitations. The conventional approach is to assist patient to perform Activities of Daily Living (ADL) through a set of pre-programmed trajectories. The main drawback lies in the lack of voluntary movement by the patient. Many of these works have been carried out by using positional feedback control. In this kind of system, it is difficult to assess patient’s muscle condition, which may results in inconveniences due to torques or forces asserted by the rehabilitation robot. The main aim of the project is to build a stroke rehabilitation system using socially inspired robot technique. The system monitors patient’s muscle activity and uses this information to drive an exoskeleton robot that will assist patient to his/her arm. Movement is generated based on voluntary muscle activity by patient and therefore will improve their learning curve. Another main advantage is the system minimizes patient’s inconveniences due to movement by robot. The system is based on torque feedback control. A sliding mode control was implemented in replace of conventional control. The main advantage of sliding mode control over conventional control is its robustness. Sliding mode control does not require precise mathematical model of the system and it is insensitive to parametric changes and uncertainties within the system.

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This paper presents the design of the latest game aimed at gross motor movement training of the upper extremity for NJIT RAVR system, which already employs various games to help stroke patients in rehabilitation. The unique feature of this game is to aid motor function of patients that come to use in day to day life like making use of hands, fingers and shoulders to pick small objects on table, moving them and placing them elsewhere. This game employs physics for the objects within the VR world and CyberGrasp hand exoskeleton which gives the subject a more realistic feeling to the grasping and releasing in real-time.

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This paper presents a novel sensing information forecasting algorithm based on time series analysis for the power assist walking legs (PAWL). The goal of this algorithm is to improve the dynamic response of the exoskeleton. The algorithm is built up with the autoregressive (AR) model, the recursive least square (RLS) method and the final prediction error (FPE) criterion. The method of RLS is utilized to make the on-line parameters estimation, and the FPE criterion is used to select the order of AR model. The forecasting algorithm is designed to be used on-line and to make predictions of force sensing information to ensure the real-time quality of the whole system. The algorithm can be categorized into two types: one step forecasting method and multi-step forecasting method. Meanwhile, we make some simulations to verify the validity of this algorithm. At the end of this paper, the correlative experiments have been carried out, and the results demonstrate this sensing information forecasting algorithm can predict the value and the trend of the sensing signal precisely and effectively.

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The paper briefly describes the semi-anthropomorphic telemanipulation system and discusses the advanced capabilities that were demonstrated in the initial performance evaluation. The system’s terminus devices are anthropomorphic: an exoskeleton sixteen degree of freedom (DOF) glove controller that senses human finger forces and backdrives slave motions to every joint of its four instrumented fingers; and a four fingered sixteen DOF anthropomorphic slave hand-wrist-forearm. The master glove is attached to a non-anthropomorphic six DOF universal force-reflecting hand controller (FRHC). The mechanical forearm is mounted to an industrial robot (PUMA 560), replacing its standard forearm. Active electromechanical compliance (AEC) systems for each finger and the wrist provide adjustable compliance, enabling human-like soft grasping. The system is controlled by a high performance distributed control system. Initial performance evaluations focused on tool handling capabilities and astronaut equivalent task executions. Results reveal that the combination of a fingered hand and active compliance enables unprecedented task executions. But it also became evident that complex manipulations require a dual arm robot

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In this paper, a wearable robot gives assistance or support to human locomotion is proposed, it will be of great value for certain activities or for special groups. As the wearable exoskeleton moves according to human intention, how to acquire human locomotion information accurately and quickly become so significantly. Firstly, we make a dynamic analysis of walking human body and build a correlative dynamic model, then introduces our wearable robot which depends on human-robot interaction force to capture human motion information real-time. Its design is strictly in accordance with dynamic model. We also make a detailed introduction of the acquisition method for mechanical information from lower limbs, detection device and its principle of operation, a LAN(Local Area Network) based on CAN(Controller Area Network) bus used to acquire motion information of human lower limbs is constructed. At last, a series of experiments are carried out, by analysis of experimental data, we can clearly recognize and depict a whole process of human motion, this acquisition method is verified feasible and effective.

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