This paper presents the preliminary results of the project BRAVO (Brain computer interfaces for Robotic enhanced Action in Visuo-motOr tasks). The objective of this project is to define a new approach to the development of assistive and rehabilitative robots for motor impaired users to perform complex visuomotor tasks that require a sequence of reaches, grasps and manipulations of objects. BRAVO aims at developing new robotic interfaces and HW/SW architectures for rehabilitation and regain/restoration of motor function in patients with upper limb sensorimotor impairment through extensive rehabilitation therapy and active assistance in the execution of Activities of Daily Living. The final system developed within this project will include a robotic arm exoskeleton and a hand orthosis that will be integrated together for providing force assistance. The main novelty that BRAVO introduces is the control of the robotic assistive device through the active prediction of intention/action. The system will actually integrate the information about the movement carried out by the user with a prediction of the performed action through an interpretation of current gaze of the user (measured through eye-tracking), brain activation (measured through BCI) and force sensor measurements.

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Stroke is very common and always leads to severe hand impairment. Traditional robotic systems have been introduced to assist therapy for a long time and already achieved certain effect. However, with further development of therapy strategies in the last few years, higher request to the rehabilitation robot has been put forward. Thus, we are developing a novel hand rehabilitation robot named HIT-glove to provide patient-cooperative therapy for hand-impaired post-stroke patients. In this paper, the mechanical design of the actuated hand exoskeleton is detailed and the control strategy for patient-cooperative therapy is introduced. The actuated hand exoskeleton, which can be applied to hands in different sizes and shapes, is able to bilaterally actuate every joint of the fingers. The patient-cooperative control strategy, based on the sensing system and an interactive interface, is well designed to let the patient actively participate into the therapy. Besides, to get a better effect of functional rehabilitation of the hand, our robot is expected to provide the following two categories of therapy: motor function exercise for joints and movement coordination training for fingers.

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This paper introduces a novel kinematic design paradigm for ergonomic human machine interaction. Goals for optimal design are formulated generically and applied to the mechanical design of an upper-arm exoskeleton. A nine degree-of-freedom (DOF) model of the human arm kinematics is presented and used to develop, test, and optimize the kinematic structure of an human arm interfacing exoskeleton. The resulting device can interact with an unprecedented portion of the natural limb workspace, including motions in the shoulder-girdle, shoulder, elbow, and the wrist. The exoskeleton does not require alignment to the human joint axes, yet is able to actuate each DOF of our redundant limb unambiguously and without reaching into singularities. The device is comfortable to wear and does not create residual forces if misalignments exist. Implemented in a rehabilitation robot, the design features of the exoskeleton could enable longer lasting training sessions, training of fully natural tasks such as activities of daily living and shorter dress-on and dress-off times. Results from inter-subject experiments with a prototype are presented, that verify usability over the entire workspace of the human arm, including shoulder and shoulder girdle

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Power-Assist for lower-limb is expected by many physically weak persons to assist their daily activities. It is important for the power-assist robots that the assist motion is generated based on users motion intention. This paper presents a muscle-model-oriented EMG-based (electromyogram-based) control method to activate a lower-limb power-assist robot according to the users motion intention since the EMG directly reflects the users muscle activities. In the proposed method, a matrix which expresses the relationship between the muscle activities and the generating joint torque is applied to estimate the users motion intention in real-time. The effectiveness of the proposed control method was evaluated by performing experiments.

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In order to assist the lower limb disabled people to walk, walking-support exoskeletons with bio/kinesthetic sensors such as EMG (Electromyography), gyroscope, and FSR (Force Sensing Resistor) have been developed. It is important to implicitly recognize the walking intention and control the walking-support mechanism. The objective of this work is to examine the walking intention while a person is walking with Lofstrand crutches by analyzing multiple bio/kinesthetic sensor signals. We developed watch/glove and shoe-insoles sensor modules using FSR sensors to recognize the user’s walking states as well as intention to walk. The FSR signals measuring force at the palm and Gyro sensor signals reflecting the pose of the upper limb were used as clues for recognizing walking intention. While seventeen normal subjects walked for five gate cycles based on the triped walking (i.e., placing crutches in forward and then moving one step) with crutches, the FSR and Gyro signals were acquired from their palms and area of vertebrae lumbales. The result showed that the combination of FSR and Gyro signals could recognize user’s implicit intention to walk — start walking, keep walking, and stop walking.

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