Southeast University

Hong Zeng

Aiguo Song

Baoguo Xu

Huijun Li

Jianwei Lai

Jun Zhang

Huanhuan Qin

Ke Shi

Jianqing Li

Xiao Li

Qingqing Chen

Jianxi Zhang

Lifeng Zhu

Xinyu Tang

Xuhui Hu

Shuyan Yang

Yiting Mo

Mingxin Leng

Bincheng Shao

Wenbin Zhang

Ting Wu

Yinxin Duan

Zhikai Wei

Provided are a multi-degree-of-freedom myoelectric artificial hand control system and a method for using same. The system comprises a robotic hand, a robotic wrist (), a stump receiving cavity () and a data processor (), wherein the robotic hand and the stump receiving cavity () are respectively mounted on two ends of the robotic wrist (); a multi-channel myoelectric array electrode oversleeve, a control unit circuit board, and a battery are connected in the stump receiving cavity (); and the other end of the control unit circuit board is connected to the robotic hand and the robotic wrist (). The method for using the system comprises the following steps: (S1) a user wearing a multi-channel myoelectric array electrode oversleeve, and connecting a battery and a control unit circuit board; (S2) the user completing a gesture, collecting a surface electromyography signal and then uploading same to a data processor (); (S3) the data processor () receiving the surface electromyography signal and inputting same into a neural network algorithm to generate a gesture prediction model; and (S4) the user controlling the multi-degree-of-freedom movement of the robotic wrist () and the robotic hand. By means of the system, continuous gestures and the gesture strength thereof can be identified, and multi-degree-of-freedom gestures can be made.

Multi-degree-of-freedom myoelectric artificial hand control system and method for using same

A exoskeleton finger rehabilitation training device includes an exoskeleton finger rehabilitation training mechanism including a supporting base, a finger sleeve actuating mechanism, and a finger joint sleeve connected to a power output end of the finger sleeve actuating mechanism, wherein the finger joint sleeve can be sheathed at the periphery of a finger joint to be rehabilitated, and the finger joint sleeve can be driven by the power actuation of the finger sleeve actuating mechanism to drive the finger joint to be rehabilitated in order to passively bend or stretch; the supporting base includes a profiled shell, with an inner surface of the profiled shell being configured based on the profile of the complete back of a palm or part of the back of the palm, and with the back of the profiled shell being provided with a power fixed base.

Exoskeleton finger rehabilitation training device and usage method thereof

The present disclosure discloses an automated calibration system and calibration method for a flexible robot actuator. The calibration system includes a support frame. A visual positioning system, a pressure measuring system and a pneumatic pressure control system are respectively installed on the support frame. The visual positioning system is configured to measure a relative displacement and an angle between two ends of the flexible actuator. The pneumatic pressure control system is configured to charge air into an actuating end of the flexible actuator and measure an input pneumatic pressure of the flexible actuator. The pressure measuring system includes a pressure gauge installed on the support frame through a vertical axis motor system, and the flexible actuator to be calibrated installed on the support frame through a horizontal axis motor system and a rotating motor system. The rotating motor system is installed on the support frame through the horizontal axis motor system, the actuating end of the flexible actuator is fixed on the rotating motor system, and a free end of the flexible actuator is in contact with a measuring end of the pressure gauge to carry out pressure measurement. The calibration system is high in accuracy and simple to use.

Automated calibration system and calibration method for flexible robot actuator

A robot-assisted hand-eye coordination training system based on a smooth pursuit eye movement and a guidance force field includes a virtual interactive scene module, a smooth pursuit eye movement detection module, a robot-assisted interception module and an impact force rendering module. The virtual interactive scene module can generate a virtual interactive scene having a virtual moving object and a virtual handle agent. The smooth pursuit eye movement detection module collects an eye movement signal of a user when the user performs pursuit eye movements on the virtual moving object to detect a smooth pursuit eye movement event. The robot-assisted interception module estimates a movement direction of the virtual moving object, generates an interception and guidance force field, and therefore generates assisting force to assist the user in interception. The impact force rendering module generates impact force according to an impact force computation model after collision is detected.

Robot-assisted hand-eye coordination training system based on smooth pursuit eye movement and guidance force field

An adaptive control method and system for an upper limb rehabilitation robot based on a game theory and surface Electromyography (sEMG) is disclosed. A movement trajectory that a robot is controlled to run within a training time is designed during subject operation. An sEMG-based Back Propagation Neural Network (BPNN) muscle force estimation model establishes a nonlinear dynamic relationship between an sEMG signal and end force by constructing a three-layer neural network. A human-computer interaction system is analyzed by the game theory principle, and a role of the robot is deduced. The control rate between the robot and a subject is updated by Nash equilibrium, and adaptive weight factors of the robot and the subject are determined. The robot adaptively adjusts the training mode thereof according to a movement intention of the subject during operation and a weight coefficient obtained by the game theory principle.

Adaptive control method and system for upper limb rehabilitation robot based on game theory and surface electromyography (sEMG)

A palm-supported finger rehabilitation training device comprises a mounting base, a finger rehabilitation training mechanism mounted on the mounting base, and a driving mechanism for driving the finger rehabilitation training mechanism; wherein the finger rehabilitation training mechanism comprises four independent and structurally identical combined transmission devices for finger training corresponding to a forefinger, a middle finger, a ring finger and a little finger of a human hand, respectively, and the mounting base is provided with a supporting surface capable of supporting a human palm; wherein each combined transmission device for finger training comprises an MP movable chute, a PIP fingerstall, a DIP fingerstall and a connecting rod transmission mechanism; a force sensor is provided to acquire force feedback information to determine and control force stability, and a space sensor is provided to acquire space angle information to control space positions of fingers in real time.

Palm-supported finger rehabilitation training device and application method thereof

A rehabilitation glove based on a bidirectional driver of a honeycomb imitating structure, including five bidirectional drivers and a cotton glove. The drivers are fixed to a back of the glove through hook and loop fasteners. Each driver includes a hollow buckling air bag in a continuous bent state, a middle guide layer in a continuous bent state and a hollow stretching air bag. The buckling air bag and the middle guide layer are symmetrically arranged, and the stretching air bag in a straightened state is arranged below the middle guide layer. A novel bidirectional driver of a honeycomb imitating structure is provided, which may provide a patient with rehabilitation training in two degrees of freedom: buckling and stretching. A control algorithm of the bidirectional driver is further provided to perform force control output for the driver, which may better help the patient recover hand functions.

Method for manufacturing and controlling rehabilitation glove based on bidirectional driver of honeycomb imitating structure

A method for reducing hysteresis error and high frequency noise error of capacitive tactile sensors includes the following steps: stepcalibration, specifically including positive stroke calibration to form n positive stroke curves and negative stroke calibration to form n negative stroke curves; stepaveraging, specifically including positive stroke averaging to form an average positive stroke curve, negative stroke averaging to form an average negative stroke curve, and comprehensive averaging to form a comprehensive stroke curve; stepfitting modeling, to obtain a positive stroke fitting function, a negative stroke fitting function, and a comprehensive fitting function; stepmeasurement; stepnoise filtering; stepstroke direction discrimination; and stepresolving, to obtain the force at the current time by using a corresponding fitting function based on the stroke direction discrimination result.

Method for reducing the hysteresis error and the high frequency noise error of capacitive tactile sensors

The present invention provides a two-degree-of-freedom rope-driven finger force feedback device. The two-degree-of-freedom rope-driven finger force feedback device includes a hand support mechanism, a thumb movement mechanism, an index finger movement mechanism, and a handle mechanism. The hand support mechanism includes a motor, a motor shaft sleeve, a sliding rail, and an inertial measurement unit (IMU) sensor. The thumb movement mechanism includes a long rotary disc, a torque sensor, an angle sensor, a thumb sleeve, a pressure sensor, two links, a thumb brace, and a thumb fixing ring. The handle mechanism includes a cylindrical handle, a pressure sensor, a flexible fixing band, and a slider. Torque is driven between the rotary disc and the motor by using a rope. The handle mechanism is movable forward and backward and is capable of automatic restoration. By means of the present invention, the problems of the high costs of a conventional finger force feedback device and the unadjustable characteristic of the conventional finger force feedback device are overcome. The device can be tightly worn and has a self-adaptive degree of freedom. Rope driving can ensure a gentle, smooth, and real feedback force. By means of the mounted sensors, information such as a hand posture, a rotation angle and a grip force of a thumb and an index finger, and a contact force of a middle finger can be transmitted in real time.

Two-degree-of-freedom rope-driven finger force feedback device

A virtual scene interactive rehabilitation training robot based on a lower limb connecting rod model and force sense information and a control method thereof are disclosed. The thigh, calf and foot of a leg of a human body are equated to a three-connecting rod series-connected mechanical arm. A human body leg gravity compensation model is constructed. The leg posture of a patient is detected by Kinect. An interaction force between a limb of the patient and a rehabilitation robot is detected by a force sensor on the rehabilitation robot. Then, a progressive rehabilitation training method is designed for the model. According to a set weight reduction ratio, the motion of the rehabilitation robot is controlled by judging plantar force data.

Virtual scene interactive rehabilitation training robot based on lower limb connecting rod model and force sense information and control method thereof

Brain-computer interface method and system include displaying and providing a motor imagery task to a subject, and collecting a generated digital electroencephalogram signal; reading the digital electroencephalogram signal, performing interception if a preset time period is exceeded, and performing continuous reading if not; performing band-pass filtering, obtaining time-frequency characteristics of the digital electroencephalogram signal, and extracting a frequency value with highest frequency energy as a main frequency; obtaining an instantaneous phase of the digital electroencephalogram signal; generating predicted sine waves by respectively using the main frequency and the instantaneous phase as a frequency and an initial phase of sine waves, and predicting and obtaining real-time phase information; and judging whether the real-time phase is in a vibration stimulation application phase interval, generating and outputting a control instruction, and controlling a vibration motor to vibrate and to stimulate a sensory channel of the subject according to the control instruction.

Brain-computer interface method and system based on real-time closed loop vibration stimulation enhancement

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.

Flexible finger-wearable haptic feedback device

An exoskeleton finger rehabilitation training apparatus includes a housing. A first motor and a second motor are disposed inside the housing. A direction of an output shaft of the first motor is opposite to a direction of an output shaft of the second motor. The output shaft of the first motor is provided with a first motor gear. A right side of the first motor gear is engaged with a first transmission gear. An edge of the first transmission gear is sequentially connected to an index finger sleeve and a middle finger sleeve that are axially arranged. The output shaft of the second motor is provided with a second motor gear. A right side of the second motor gear is engaged with a second transmission gear. An edge of the second transmission gear is sequentially connected to a pinky sleeve and a ring finger sleeve that are axially arranged.

Exoskeleton finger rehabilitation training apparatus

An artificial fingertip sliding tactile sensor includes a PVDF film, a rubber fingertip, a filling liquid, a sealing plug, a hydraulic sensor, a housing, an inner framework, and strain gauges. The rubber fingertip is a hemispherical cavity. The PVDF film is attached to the outside of the rubber fingertip. The sealing plug seals the rubber fingertip, and the hydraulic sensor is installed at the bottom of the sealing plug. The main body of the housing is a rigid cylindrical structure. The top of the housing is provided with a circular opening, and the bottom of the housing is a flange-like structure. Four circular through holes are uniformly distributed on the flange-like structure. The inner framework includes a cylindrical head, a vertical strain rod and a base. The strain gauges are respectively attached on four sides of the vertical strain rod and adjacent to the base.

Artificial finger tip sliding touch sensor

A method for controlling a limb motion intention recognizing and rehabilitation training robot based on force sense information includes: acquiring data of a three-dimensional force and a three-dimensional moment by a six-dimensional force sensor held in a hand; calculating forces and moments produced by a palm, a forearm and an upper arm of a human body according to a constructed human arm model to achieve recognition of limb motion intention; fixing the six-dimensional force sensor on a rocker at an end of the three-degree-of-freedom upper limb rehabilitation training robot, acquiring motion intention of an arm of the human body according to the motion intention recognition method, and controlling the rehabilitation training robot to achieve auxiliary active training under a weak active force.

Method for controlling a limb motion intention understanding and upper limb rehabilitation training robot based on force sense information and posture information

Growing community of inventors

Jiangsu, China

Hong Zeng