Exoskeletons are wearable robots exhibiting a close cognitive and physical interaction with the human user. These are rigid robotic exoskeletal structures that typically operate alongside human limbs. Scientific and technological work on exoskeletonsbegan in the early 1960s but have only recently been applied to rehabilitation and functional substitution in patients suffering from motor disorders. Key topics for further development of exoskeletons in rehabilitation scenarios include the need for robust human-robot multimodal cognitive interaction, safe and dependable physical interaction, true wearability and portability, and user aspects such as acceptance and usability. This discussion provides an overview of these aspects and draws conclusions regarding potential future research directions in robotic exoskeletons.

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Walking power-assisted robot is a wearable exoskeleton leg for augmentation of the human walking ability. It is anthropomorphic, parallels to the human lower limbs and interfaces to the human via shoulder straps, waist belt, thigh cuffs, and a shoe connection such that the geometry of the human and the machine approximately match one another. It is important and necessary for analyzing the human walking gait in order to reality the power-assisted robot walking coincident with the operator, and human walking gait data will provide some useful bases for the design of power-assisted robot. So in this paper we focus on introducing the walking gait analysis system which we have developed.

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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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