Abstract: A robot system for active and passive upper limb rehabilitation training based on a force feedback technology includes a robot body and an active and passive training host computer system. Active and passive rehabilitation training may be performed at degrees of freedom such as adduction/abduction and flexion/extension of left and right shoulder joints, and flexion/extension of left and right elbow joints according to a condition of a patient. In a passive rehabilitation training mode, the robot body drives the upper limb of the patient to move according to a track specified by the host computer, to gradually restore a basic motion function of the upper limb. In an active rehabilitation training mode, the patient holds the tail ends of the robot body with both hands to interact with a rehabilitation training scene, and can feel real and accurate force feedback.

Abstract: A wearable upper limb rehabilitation training robot with precise force control includes a wearable belt, a multi-degree-of-freedom robot arm, and a control box. The robot is worn on the waist of a person by using a belt, and driven by active actuators, to implement active and passive rehabilitation training in such degrees of freedom as adduction/abduction/anteflexion/extension of left and right shoulder joints and anteflexion/extension of left and right elbow joints. In addition, a force/torque sensor is mounted on a tip of the robot arm, to obtain a force between the tip of the robot arm and the human hand during rehabilitation training as a feedback signal, to adjust an operating state of the robot, thereby realizing the precise force control during the rehabilitation training.

Abstract: A natural human-computer interaction system based on multi-sensing data fusion comprises a MEMS anti tracking device, a visual tracking device, a force feedback device and a PC terminal. The MEMS arm tracking device is composed of three sets of independent MEMS sensors for collecting arm joint angle information and measuring an arm motion trajectory. The visual tracking device is composed of a binocular camera for collecting image information and measuring a finger motion trajectory. The force feedback device is mounted in a palm of an operator for providing a feedback force to the finger. The PC terminal comprises a data display module, an arm motion calculating module, an image processing module, a mechanics calculating module and a virtual scene rendering module. The system tracks the arm motion trajectory and the finger motion trajectory of the operator and provides force feedback interaction to the finger of the operator.

Abstract: A flexible finger-wearable haptic feedback device includes a fingertip sleeve sheathing a distal phalanx of a finger, a middle sleeve sheathing a middle phalanx of the finger, a proximal sleeve sheathing a proximal phalanx of the finger, outer and inner transmission rods having bending elasticity. The outer transmission rod is fixed on the fingertip sleeve at one end, positioned at a back of a hand at the other end and connected with an outer driver. The inner transmission rod is fixed on the fingertip sleeve at one end, positioned at a palm at the other end and connected with an inner driver. The fingertip sleeve is provided with first and second contact pressure sensors respectively connected with the ends of the outer and inner transmission rods, and an inner wall of the fingertip finger sleeve contacting the finger is provided with a film pressure sensor.

Abstract: A wearable upper limb rehabilitation training robot with precise force control includes a wearable belt, a multi-degree-of-freedom robot arm, and a control box. The robot is worn on the waist of a person by using a belt, and driven by active actuators, to implement active and passive rehabilitation training in such degrees of freedom as adduction/abduction/anteflexion/extension of left and right shoulder joints and anteflexion/extension of left and right elbow joints. In addition, a force/torque sensor is mounted on a tip of the robot arm, to obtain a force between the tip of the robot arm and the human hand during rehabilitation training as a feedback signal, to adjust an operating state of the robot, thereby realizing the precise force control during the rehabilitation training.

Abstract: A robot system for active and passive upper limb rehabilitation training based on a force feedback technology includes a robot body and an active and passive training host computer system. Active and passive rehabilitation training may be performed at degrees of freedom such as adduction/abduction and flexion/extension of left and right shoulder joints, and flexion/extension of left and right elbow joints according to a condition of a patient. In a passive rehabilitation training mode, the robot body drives the upper limb of the patient to move according to a track specified by the host computer, to gradually restore a basic motion function of the upper limb. In an active rehabilitation training mode, the patient holds the tail ends of the robot body with both hands to interact with a rehabilitation training scene, and can feel real and accurate force feedback.

Abstract: A flexible finger-wearable haptic feedback device includes a fingertip sleeve sheathing a distal phalanx of a finger, a middle sleeve sheathing a middle phalanx of the finger, a proximal sleeve sheathing a proximal phalanx of the finger, outer and inner transmission rods having bending elasticity. The outer transmission rod is fixed on the fingertip sleeve at one end, positioned at a back of a hand at the other end and connected with an outer driver. The inner transmission rod is fixed on the fingertip sleeve at one end, positioned at a palm at the other end and connected with an inner driver. The fingertip sleeve is provided with first and second contact pressure sensors respectively connected with the ends of the outer and inner transmission rods, and an inner wall of the fingertip finger sleeve contacting the finger is provided with a film pressure sensor.

Abstract: A natural human-computer interaction system based on multi-sensing data fusion comprises a MEMS anti tracking device, a visual tracking device, a force feedback device and a PC terminal. The MEMS aim tracking device is composed of three sets of independent MEMS sensors for collecting arm joint angle information and measuring an arm motion trajectory. The visual tracking device is composed of a binocular camera for collecting image information and measuring a finger motion trajectory. The force feedback device is mounted in a palm of an operator for providing a feedback force to the finger. The PC terminal comprises a data display module, an arm motion calculating module, an image processing module, a mechanics calculating module and a virtual scene rendering module. The system tracks the arm motion trajectory of the operator by taking and tracks the finger motion trajectory of the operator and provides force feedback interaction to the finger of the operator.

Abstract: The present invention discloses a minitype haptic rendering method based on active and passive devices, which comprises the following steps of: firstly, calibrating a magnetorheological damper and a direct current motor, and obtaining a relationship between an input current and an output torque; converting an expected force/torque value to a current input of the magnetorheological damper, outputting a corresponding torque through the magnetorheological damper, and applying the torque to a body of an operator through a haptic transmission device; secondly, measuring an actually applied force/torque by a sensor mounted at a force/torque application point, comparing an actually outputted force/torque value with the expected force/torque value, and calculating a force/torque error; and finally, converting the force/torque error to an input signal of the direct current motor, and driving the direct current motor to generate a torque corresponding to the error.

Abstract: The present invention discloses a minitype haptic rendering method based on active and passive devices, which comprises the following steps of: firstly, calibrating a magnetorheological damper and a direct current motor, and obtaining a relationship between an input current and an output torque; converting an expected force/torque value to a current input of the magnetorheological damper, outputting a corresponding torque through the magnetorheological damper, and applying the torque to a body of an operator through a haptic transmission device; secondly, measuring an actually applied force/torque by a sensor mounted at a force/torque application point, comparing an actually outputted force/torque value with the expected force/torque value, and calculating a force/torque error; and finally, converting the force/torque error to an input signal of the direct current motor, and driving the direct current motor to generate a torque corresponding to the error.