Walking impairments are a common sequela of neurological injury, severely affecting the quality of life of both adults and children. Gait therapy is the traditional approach to ameliorate the problem by re-training the nervous system and there have been some attempts to mechanize such approach. In this paper, we present a novel device to deliver gait therapy, which, in contrast to previous approaches, takes advantage of the concept of passive walkers and the natural dynamics of the lower extremity in order to deliver more “ecological” therapy. We also discuss the closed-loop control scheme, which enables safe and efficient operation of the device, and present the initial feasibility tests with unimpaired subjects.

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Most recent findings in robot-assisted therapy suggest that the therapy is more successful if the patient actively participates to the movement (“assistance-as-needed”). In the present contribution, we propose a novel approach for designing highly flexible protocols based on this concept. This approach uses adaptive oscillators: a mathematical primitive having the capacity to learn the high-level features of a quasi-sinusoidal signal (amplitude, frequency, offset). Using a simple inverse model, we demonstrate that this method permits to synchronize with the torque produced by the user, such that the effort associated with the movement production is shared between the user and the assistance device, without specifying any arbitrary reference trajectory. Simulation results also establish the method relevance for helping patients with movement disorders. Since our method is specifically designed for rhythmic movements, the final target is the assistance/rehabilitation of locomotory tasks. As an initial proof of concept, this paper focuses on a simpler movement, i.e. rhythmic oscillations of the forearm about the elbow.

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This paper presents a new gravity compensation method for an upper extremity exoskeleton mounted on a wheel chair. This new device is dedicated to regular and efficient rehabilitation training for post-stroke and injured people without the continuous presence of a therapist. The exoskeleton is a wearable robotic device attached to the human arm. The user provides information signals to the controller by means of the force sensors around the wrist and the arm, and the robot controller generates the appropriate control signals for different training strategies and paradigms. This upper extremity exoskeleton covers four basic degrees of freedom of the shoulder and the elbow joints with three additional adaptability degrees of freedom in order to match the arm anatomy of different users. For comfortable and efficient rehabilitation, a new heuristic method have been studied and applied on our prototype in order to calculate the gravity compensation model without the need to identify the mass parameters. It is based on the geometric model of the robot and accurate torque measurements of the prototype’s actuators in a set of specifically chosen joint positions. The weight effect has been successfully compensated so that the user can move his arm freely while wearing the exoskeleton without feeling its mass.

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Control and overall system performance of an upper limb exoskeleton, as a wearable robot, is dictated in part by the human machine interface and the implemented control algorithm. The ultimate goal is to develop algorithms so the operator feels as if theexoskeleton is a natural extension the body. The aim of the current research is to compare the system performance of a 7 degree of freedom wearable upper limb exoskeleton (EXO-UL7) using two multi-sensor admittance controllers (1) task space control and (2) joint space control. Multiple force sensors are needed due to the redundancy in the system (7 DOF). This redundancy is explored and a method is developed to calculate a closed form inverse kinematics (IK) solution. The IK solution is used to develop the task space controller. The joint space controller uses the transpose of the jacobian to resolve sensor forces into joint torques. Six subjects performed a peg in hole task. Six targets covered the main part of the device workspace. Velocities and Interaction forces at the upper arm, lower arm, handle and tip were recorded during the experiments. Power exchange between the subject and device was calculated. Task space based control was about 11% lower in mean interaction energy for the peg in hole task compared to joint space control. Task completion time increased with both controllers compared to back-driving the device.

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A wearable action-assist device which assists or executes an action of a wearer by substituting for the wearer is provided with an action-assist tool 2 having an actuator 201 which gives power to the wearer 1, a biosignal sensor 221 which detects a wearer’s biosignal, a biosignal processing unit 3 which acquires from a biosignal «a» detected by the biosignal sensor a nerve transfer signal «b» for operating a wearer’s muscular line skeletal system, and a myoelectricity signal «c» accompanied with a wearer’s muscular line activity, an optional control unit 4 which generates a command signal «d» for causing the actuator 201 to generate power according to the wearer’s intention using the nerve transfer signal «b» and the myoelectricity signal «c» acquired by the biosignal processing unit 3, and a driving current generating unit 5 which generates a current according to the nerve transfer signal b and a current according to the myoelectricity signal «c», respectively, based on the command signal «d» generated by the optional control unit 4, and supplies the currents to the actuator 201.

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