313 results about "Motor function" patented technology

The present disclosure provides a humanoid robot with an arrangement of components that allows the robot to mimic the movements, functionality and capabilities of a human being. The robot includes a torso coupled to a waist, an arm assembly, and a head assembly. A pelvis is coupled to the waist and has left and right actuator mounts. Left and right hip assemblies are coupled to the respective actuator mounts. Each hip assembly includes a hip pitch actuator assembly, a hip roll actuator assembly, and a leg twist actuator assembly. The hip pitch actuator assembly has a portion positioned within the pelvis and is coupled to the actuator mount. The hip roll actuator assembly is coupled to the hip pitch actuator assembly, with a non-90 degree angle formed between their axes. The leg twist actuator assembly is coupled to the hip roll actuator assembly and positioned below extents of both the hip pitch and hip roll actuator assemblies.

The invention belongs to the field of cerebral apoplexy rehabilitation training, and provides a cerebral apoplexy upper limb dynamic rehabilitation method and system based on vagus nerve electrical stimulation, the excitement degree of a cortical spinal cord pathway of a patient is measured and evaluated by using motor evoked potential to obtain a motor function evaluation result, and an initial rehabilitation training task is set according to the motor function evaluation result; the training motion intention of the patient is determined by monitoring the electroencephalogram signal and the electromyographic signal of the patient, and percutaneous ear vagus nerve electrical stimulation is triggered; after percutaneous ear vagus nerve electrical stimulation, performing rehabilitation training analysis on basic clinical characteristics and multi-modal signals of the patient to determine intermuscular coordination, brain region connectivity and brain muscle coupling of the patient; the training condition of the patient is evaluated according to the rehabilitation training analysis result, and parameters of percutaneous ear vagus nerve electrical stimulation and training tasks are adjusted based on the evaluation result for iterative rehabilitation training; and stopping iteration until the rehabilitation training analysis result of the patient reaches a preset standard, and completing rehabilitation training.

The invention discloses a nerve disease detection and analysis method and system, and the method comprises the steps: obtaining a bracelet collection signal, a sphygmomanometer collection signal, a movement behavior image and behavior test data, and extracting tremor intensity features, gait symmetry features and autonomic nerve rhythm features through multi-band decomposition of the bracelet collection signal; analyzing the motion behavior image and the standardized motion test to obtain a motion function score; carrying out heart rate variability analysis to identify a neural function abnormality mode; constructing a neural function state map and calculating a feature weight; predicting a disease progress trend in combination with historical monitoring data; and dynamically adjusting a prediction result through subsequent feedback correction information. Through a mode of combining short-time intensive monitoring and long-term intermittent acquisition, long-term trend prediction and dynamic correction based on initial data are realized, and the reliability and practicability of nerve disease risk assessment in a home scene are remarkably improved.

The invention belongs to the technical field of cerebral apoplexy rehabilitation medicine, and relates to evaluation of myoelectric medicine, in particular to an HD-sEMG-based evaluation method and evaluation system for hand dyskinesia of a cerebral apoplexy hemiplegia patient. According to the invention, the high-density surface myoelectricity data of the front arms of the healthy side and the affected side can be respectively collected when the stroke hemiplegia patient executes different hand actions, and the microscopic neuron characteristics are analyzed, so that the degree and progress of dyskinesia of the stroke hemiplegia patient can be accurately evaluated. Through statistical analysis of the number of motion units, the average discharge rate and the discharge mode, changes and rules of neurons in the rehabilitation process are revealed, the difference between the healthy side and the affected side is compared, and the motion function condition of the neurons is evaluated.

The invention belongs to the technical field of medical instruments, and particularly relates to a spinal epidural ventral side double-wing-shaped stimulation electrode and motion function reconstruction system, the spinal epidural ventral side double-wing-shaped stimulation electrode and motion function reconstruction system comprises two wing panels, each wing panel comprises a flexible substrate, the flexible substrate is provided with two bifurcated preset-curvature double-wing structures; the electrode contact array is integrated on the flexible polar plate through a bonding pad; according to the double-wing-shaped flexible substrate, through a double-wing structure with a preset curvature, after being unfolded under the driving of a nickel-titanium alloy framework, the double-wing-shaped flexible substrate accurately covers a spinal cord ventral side and double-side anterior angle motor neuron pool, and the problem that a traditional single-piece / rod-shaped electrode can only cover a back side sensing area and cannot cover a back side sensing area is solved. The ventral motor neuron group cannot be directly activated; a stimulation target spot is transferred to a ventral functional core area from a dorsal non-target area for the first time, and the anatomical suitability bottleneck of motor function reconstruction is solved.

The invention relates to the technical field of medical rehabilitation, and discloses a virtual fusion personalized upper limb rehabilitation training cloud platform and method. The platform is composed of a multi-source biological signal acquisition module, a motion function feature modeling module, a heterogeneous data fusion module, a rehabilitation knowledge graph construction module, a dynamic training planning module and the like. The method comprises the steps of collecting multi-source biological signals, carrying out hierarchical feature modeling, carrying out data fusion, constructing an atlas database, generating a personalized rehabilitation scheme and the like. In this way, individuation and precision of upper limb rehabilitation training are achieved. Patient data can be comprehensively collected, fusion rehabilitation state representation is generated, a double-layer atlas database is constructed to assist training planning, training safety is guaranteed, the rehabilitation effect is predicted, pertinence, scientificity and efficiency of rehabilitation training are effectively improved, and high-quality rehabilitation service is provided for upper limb dysfunction patients.

The invention relates to the technical field of rehabilitation data prediction, and discloses a motion function rehabilitation prediction method and system based on multi-modal data, a terminal and a storage medium, and the method comprises the steps: obtaining an electroencephalogram signal, an electromyographic signal and a functional near infrared spectrum signal of a target user, pre-processing to obtain a target electroencephalogram signal, a target electromyographic signal and a target functional near infrared spectrum signal; respectively carrying out feature extraction to obtain three time-frequency features, and carrying out source positioning processing on the target electroencephalogram signal and the target functional near infrared spectrum signal to obtain brain source intensity; and fusing the three time-frequency features to obtain a target time-frequency feature, inputting the target time-frequency feature and the brain source intensity into a multi-modal data rehabilitation prediction model, and outputting a motor function rehabilitation prediction result. According to the method, the relationship between the three signals of the target user and the motion function of the target user is modeled in the time sequence, so that the long-term evolution of the motion function in the time sequence is quickly evaluated, and the accuracy of a prediction result is improved.

The invention relates to an accurate collision-free automatic docking control method which is characterized by comprising the following steps: S1, remote guidance: triggering an automatic docking signal, establishing a three-dimensional semantic map, completing autonomous positioning, planning a docking path, performing real-time obstacle avoidance navigation, and executing safe and stable movement to reach a pre-docking position; s2, posture adjustment: when reaching the pre-docking position, determining the target posture of the target docking object and the posture of the object to be docked, and adjusting the posture of the object to be docked to the pre-docking posture; and S3, short-range docking: based on motion compensation, detecting a motion error, generating a motion correction instruction according to error signal feedback, and realizing collision-free accurate docking. The movement function and the body position changing function of the intelligent wheelchair are achieved through the intelligent wheelchair, smooth and stable switching can be achieved in the changing process of the intelligent wheelchair, and meanwhile the intelligent wheelchair is an indoor power assisting system, has the obstacle avoiding walking function and can be in collision-free automatic butt joint with a bed body and a closestool.

The invention discloses a multi-scene gait monitoring and motion function assessment method and system, a multi-camera cooperation system is deployed in a life scene to accurately identify an identity and extract multi-scene motion features, and an accurate old people gait function assessment scheme is provided through key motion hierarchical modeling, multi-scene data fusion and a risk suppression mechanism. Specifically, 2D human body key points are estimated based on an OpenPose algorithm, and a PoseLifter model is utilized to lift the 2D key points to 3D, so that a three-dimensional motion track of a target is estimated; dynamically adjusting the weights of the face features and the skeleton features during identity recognition according to ambient light; designing an independent long-short-term memory network for different actions to evaluate the motion function score and the fall risk rating of the actions; and designing a risk sensitivity mechanism and fusing multi-scene data to carry out comprehensive scoring. The gait function abnormity of the old people can be found in time, and a basis is provided for health management.

The invention discloses a method and a system for evaluating movement of two lower limbs of a stroke patient, and relates to the technical field of movement function evaluation. The invention discloses a stroke patient double-lower-limb movement evaluation system which comprises a task guide module, a movement acquisition module, a movement scoring module, an interference evaluation module, a trend analysis module, a combination analysis module and a result output module. According to the method, the scoring model based on crowd category feature matching is constructed, exercise ability scoring is performed by adopting a machine learning algorithm or a fixed weighted combination, personalized evaluation is realized according to the group features of different patients, and the generalization ability and prediction accuracy of the scoring model are improved; group modeling is carried out by introducing features such as patient age, gender and motion level, and a scoring strategy is automatically switched according to the number of samples, so that the scoring mechanism also has reliability and clinical consistency in a family scene with limited data volume.

The invention relates to the technical field of data processing, and discloses a gait rehabilitation evaluation method and system based on spatial-temporal characteristics of multi-modal data, a terminal and a storage medium, and the method comprises the steps: synchronously collecting myoelectricity, electroencephalogram and exoskeleton robot IMU signals of a dyskinesia crowd in a certain motion normal form in a specific period interval; a motion function scoring result of a doctor on a patient according to a scale is synchronously recorded, data preprocessing, channel selection and feature extraction are performed on myoelectricity, electroencephalogram and exoskeleton robot IMU signals, a model is established based on a neural network, motion evaluation is performed according to a fused feature array, and a motion function quantitative score is obtained; and the motion function state of the evaluated person is graded according to the score. According to the method, the multi-modal synchronous data rehabilitation evaluation model of the movement function of the dyskinesia crowd is constructed according to multiple information mining, and the movement function state of the evaluated person is rapidly evaluated, predicted and graded.

The invention provides an ankle rehabilitation exercise intelligent management system and method based on multi-modal data fusion, and relates to the technical field of medical rehabilitation, and the method comprises the steps: collecting sole state data and electromyographic signal data of a patient end through a sensor network; determining a target gait mode and gait parameters according to the sole state data, and summarizing to obtain real-time gait data; determining muscle strength and muscle coordination degree according to the electromyographic signal data, and summarizing to obtain muscle state data; constructing an ankle motion function abnormity prediction model, and determining an ankle fault score according to the real-time gait data and the muscle state data; the ankle fault grade is judged according to the ankle fault score, and a personalized ankle rehabilitation strategy is made based on the ankle fault grade and sent to the visual display terminal, so that the ankle movement data can be accurately collected, a personalized rehabilitation exercise scheme is customized, the rehabilitation effect of a patient is improved in combination with an exercise auxiliary structure, and the rehabilitation effect of the patient is improved. The rapid recovery of the ankle function is promoted.

The invention provides a limb movement function evaluation system which can be applied to the technical field of movement evaluation. The limb movement function evaluation system comprises a display; the first wearable sensor is worn on the limb of the object; the processor is connected to the display and the first wearable sensor, and is configured to control the display to sequentially display position indication images of the K areas so as to sequentially indicate position points in the K areas to an object, receive first position data from the first wearable sensor at the same time, obtain first limb position data of the K areas, and send the first limb position data to the first wearable sensor; the K areas are distributed in different directions of the object in the space where the object is located, and K is an integer greater than 1; determining limb movement information of the K areas according to the first limb position data of the K areas; and according to the limb movement information of the K areas, generating a limb assessment result of the object. According to the method, the movement performance of the limb of the object in each region can be objectively evaluated, and the accuracy of limb movement function evaluation is improved.

The invention provides an old people dyskinesia risk monitoring method, system and device and a medium, basic information of old people is acquired, a compliance evaluation model is established, and training parameters are adjusted based on a medical care manual intervention interface; identifying a high-risk factor combination by using an association rule, analyzing and predicting a movement function change trend of a future preset time length by combining a synchronous time sequence, and optimizing a decision weight by using a reinforcement learning module according to a medical care manual adjustment record; a dynamic risk assessment model is constructed by using a Bayesian network, cross-mechanism data cooperative training is realized by using a federal learning framework, time sequence features are extracted through feature engineering, and motion function indexes and risks of old people in a period of time in the future are predicted. And comprehensive evaluation, intelligent early warning and personalized intervention on the dyskinesia of the old people are realized. Rehabilitation training execution conditions of old people are monitored in real time, and rehabilitation prescriptions are dynamically adjusted according to execution data and subjective feedback, so that effectiveness of intervention is ensured.

The invention relates to the technical field of disease rehabilitation assessment, and discloses a functional behavior-based assessment and rehabilitation system for lower limb artery occlusion, which is characterized in that gait data, muscle force data, blood flow change data and electromyographic signal data of a patient are acquired by arranging a data acquisition module, and a rehabilitation assessment report of the patient is generated by using a multi-modal fusion algorithm; and the artery occlusion degree, the lower limb blood circulation condition and the motion function state of the patient are comprehensively evaluated. On the basis, the system generates a personalized exercise scheme through a reinforcement learning algorithm, and dynamically adjusts the exercise scheme according to the real-time exercise data feedback of the patient, so as to ensure the effectiveness and safety of the rehabilitation scheme. Meanwhile, the system provides a guide feedback module, the patient is guided to complete the correct motion posture through voice, vision or vibration prompts, and secondary injury caused by improper motion posture is avoided. By dynamically optimizing the exercise scheme and guiding feedback in real time, personalized rehabilitation guidance of the patient is achieved, and the rehabilitation effect and exercise compliance are improved.

The invention discloses a magnesium-containing nanoparticle modified biological scaffold composite material as well as a preparation method and application thereof. The magnesium-containing nano particle modified biological scaffold composite material comprises a biological scaffold material and magnesium-containing nano particles modified on the biological scaffold material, the magnesium-containing nano particles comprise nano magnesium oxide particles and / or nano magnesium oxide-polyphenol compound particles, the nano magnesium oxide-polyphenol compound particles are of a shell-core structure, and the shell-core structure is of a hollow structure. Comprising magnesium oxide serving as a core and a polyphenol compound shell layer coating the surface of the magnesium oxide. The magnesium-containing nanoparticle-modified biological scaffold composite material provided by the invention can prolong the degradation and release of magnesium ions, promote improvement of microenvironment problems such as inflammation and active oxygen outbreak after spinal cord injury, regulate and control oxidative stress and inflammatory response, promote nerve regeneration and motor function recovery, and improve the survival rate of spinal cord injury. The problem that nanoparticles stop in a spinal cord injury area is effectively solved, and support is provided for migration, growth and differentiation of endogenous neural stem cells.

The invention discloses an intelligent analysis system and method for rehabilitation treatment of lumbar intervertebral disc herniation, and relates to the field of intelligent analysis. Abstract clinical information amounts such as lesion part features, pain perception description and movement function requirements are converted into high-dimensional vectors by adopting a semantic coding technology based on deep learning; the system can more accurately understand and simulate the condition of a patient in a multi-dimensional space, further, a dynamic mapping mechanism between a rehabilitation starting point and a target is established to capture non-linear correlation between the current state of the patient and an ideal rehabilitation target, and intelligent derivation of a rehabilitation path is achieved based on the non-linear correlation. Through the dynamic analysis of the multi-dimensional information of the patient, the system can better understand the unique demand of each patient, the matching precision of the rehabilitation treatment scheme is improved, and the treatment is more personalized and effective.

Disclosed in the present invention is a lower-limb-powered exoskeleton device for rehabilitation assistance, comprising ankle-joint sagittal-plane driving units, knee-joint sagittal-plane driving units, hip-joint sagittal-plane driving units, a hip-joint horizontal-plane driving unit, a joint driver connecting frame, a lower-limb fixing support, sensors, a controller and a power supply. Antagonistic-drive-type linear driving units are used in the sagittal plane of a hip joint so as to enable the bending and extension movements of the hip joint, and a linear driving unit having the function of self-locking when powered off is used in the horizontal plane of the hip joint so as to enable the turning movements of the exoskeleton; and a linear driving unit in cooperation with a magnetorheological brake is used in the sagittal plane of each knee joint, thereby significantly reducing the power consumption of a knee-joint driver while lowering the control difficulty. The powered exoskeleton device can help patients with an impaired lower-limb motor function to carry out walking rehabilitation training.

The invention provides a human body motor function decline risk prediction method based on multi-modal data evaluation. The method comprises the following steps: S1, collecting multi-modal data; s2, data preprocessing; s3, multi-dimensional feature extraction is carried out; s4, labeling and quantifying; s5, feature coding and fusion; s6, model training; s7, evaluating and verifying the model; and S8, outputting the model application. Through multi-modal data fusion and intelligent modeling, the defects in the prior art are effectively overcome.

The invention discloses an intelligent feedback type encephalopathy rehabilitation guidance training system which collects physiological data of a patient in real time through a wearable device or a brain-computer interface, carries out accurate evaluation and generates a personalized training plan in combination with individual differences and rehabilitation progress of the patient. In the training process, the system provides immediate feedback and guidance, and correctness and effectiveness of training are ensured. Meanwhile, the system also integrates comprehensive rehabilitation guidance functions, including cognition, language and motion function recovery, psychological support, life skill training and the like; through the cloud data processing center, medical personnel can remotely monitor the rehabilitation condition of a patient and adjust a training plan in time. The system not only improves the pertinence and effectiveness of rehabilitation training, but also enhances the rehabilitation confidence and participation degree of the patient. In a word, the intelligent feedback type encephalopathy rehabilitation guidance training system provides all-around and personalized rehabilitation services for encephalopathy patients, and the rehabilitation effect and the satisfaction degree of the patients are remarkably improved.

The invention relates to the technical field of medical rehabilitation, in particular to a post-stroke upper limb function recovery training system combined with a vr technology, which comprises a fixed base, and a motor is fixedly connected to the center of an inner cavity of the fixed base. The upper limb rehabilitation training device has the advantages that the angle adjusting and rotating functions are achieved, a personalized training plan can be made according to the specific conditions of a patient, and therefore upper limb rehabilitation training of the patient is more effectively promoted; through cooperative use of a fixed base, a motor, a fixed column, a supporting rod, a fixed sleeve, a rotating rod, an adjusting rod, an electric push rod, a lifting block, a seat and a sliding rod, the rotating and angle adjusting functions of the seat can be achieved, it is ensured that a patient can obtain the best comfort degree and experience feeling in the training process, the angle is flexibly adjusted through the seat, and the training efficiency is improved. A patient can experience a more real training scene in an immersive manner, and the brain is stimulated to generate strong neural feedback, so that the movement function recovery of the upper limbs is effectively promoted.

To provide a motion analysis device that can be used for evaluating a motor function or motion disorder.SOLUTION: A motion analysis device (20) includes elbow flexure ratio calculation means (222) for calculating an elbow flexure ratio of an upper limb in elevation exercise which is expressed by an expression (1) or an expression (2) from respective three-dimensional coordinate data of the shoulder, elbow, and hand of a person to be evaluated when the person to be evaluated is in upper limb elevation exercise. In the expression (2), SH represents a vector from the shoulder to the hand, SE represents a vector from the shoulder to the elbow, and EH represents a vector from the elbow to the hand.SELECTED DRAWING: Figure 1

The embodiment of the invention provides a patient dyskinesia assessment system and method based on multi-modal data fusion, and relates to the field of motion function assessment and artificial intelligence. The system comprises an acquisition module used for acquiring multi-modal motion performance data of a to-be-evaluated target; the first extraction module is used for extracting human body bounding boxes and segmentation masks of the image data and the video data; the enhancement module is used for extracting image features of the human body bounding box and the segmentation mask and enhancing the image features; the second extraction module is used for extracting signal features of the preprocessed physiological signal data and voice signal data based on a deep neural network and enhancing the signal features to obtain text features; the updating module is used for performing feature updating; the fusion module is used for carrying out feature fusion on the updated image features and text features; and the evaluation module is used for obtaining a motion function evaluation result of the to-be-evaluated target. According to the scheme, the accuracy and efficiency of the dyskinesia assessment result of the patient are effectively improved.

A human body assisting apparatus and an accessory motion testing platform, comprising: a mobile motion platform (2), a multi-degree-of-freedom robotic arm (5), a motion capture system (8), a ground acting force measurement table (1), and a controller. By controlling the mobile motion platform (2) and the multi-degree-of-freedom robotic arm (5), the testing platform reproduces three-dimensional trajectories and postures of some limbs of a human body in a motion process and an interaction force between the limbs of the human body, so as to quickly verify some motion functions of an artificial limb, and shorten a research and development period of a human body assisting apparatus (7) comprising the artificial limb. In addition, the motion capture system (8) is used for measuring three-dimensional trajectories and postures of the multi-degree-of-freedom robotic arm (5) and the human body assisting apparatus (7), and the ground acting force measurement table (1) is used for measuring an interaction force between the human body assisting apparatus (7) and the ground. The motion testing platform can accurately reproduce motion trajectories and motion postures of limbs of a human body and an interaction force between the limbs, thereby improving the motion testing efficiency and accuracy of the human body assisting apparatus, and significantly reducing the human body testing cost. Further provided is an operation method for a human body assisting apparatus and an accessory motion testing platform.

The invention discloses an upper limb rehabilitation training system and method for a stroke patient. Relates to the technical field of medical rehabilitation. According to the stroke upper limb rehabilitation training system and method, the limbs are driven by a machine to do repeated movement, a nervous system for controlling limb movement is stimulated and reconstructed, and therefore the movement function of the limbs is recovered, and the rehabilitation training effect of the limbs is improved. On the basis of an existing motion tracking and feedback technology, the system and the method are improved, real-time data analysis is carried out based on a sensor, a brain-computer interface technology and a cloud platform, influences of operation environment information and motion performance are considered in individualized requirements of patients, individual differences and environment variables are comprehensively considered in the rehabilitation process, and the rehabilitation effect is improved. A patient can perform rehabilitation training at any place, a rehabilitation physician, the patient and family members can check the rehabilitation condition of the patient at any time, and the precise training system and method suitable for various environments and changeable rehabilitation requirements are provided.

The invention discloses a nervous system function evaluation method and device, computer equipment and a medium, and relates to the technical field of intelligent medical treatment. The specific implementation mode comprises the following steps: acquiring sensor data of a plurality of body parts of daily activities of a user, wherein the sensor data is acquired through a sensor arranged in a home environment; obtaining a plurality of motion function evaluation parameters according to the sensor data; obtaining a nervous system function evaluation result based on the whole-age group normal population data model according to the multiple motion function evaluation parameters; and outputting a neurological function risk index and a risk index of the user according to a nervous system function evaluation result. According to the scheme, the nervous system function is evaluated by obtaining multiple evaluation parameters in daily life, and the accuracy and reliability of the evaluation result and the use flexibility of the evaluation method are improved.

Methods and systems are provided to assess physical movements of a patient based on a video or consecutive images taken of the patient performing a particular physical activity. The assessment can be used for determining a medical disorder or a stage of a medical disorder that the patient suffers.

The invention discloses a rehabilitation nursing management system, and particularly relates to the technical field of medical rehabilitation, and the system comprises a patient intelligent evaluation module which is used for comprehensively evaluating the physiological, psychological and motor function states of a patient through multi-dimensional data collection and intelligent analysis. Physiological, psychological and motion function data of a patient are collected in a multi-dimensional mode through the patient intelligent evaluation module, rehabilitation requirements are predicted based on the data, the personalized rehabilitation plan module formulates a personalized rehabilitation plan according to an evaluation result, immersion rehabilitation guidance is provided through a virtual rehabilitation coach and AR auxiliary training, and the rehabilitation effect is improved. Meanwhile, the training intensity and effect are dynamically optimized, real-time monitoring and risk early warning are achieved through a remote and cooperation module, efficient cooperation among doctors, patients and family members is ensured, the rehabilitation progress is continuously recorded and analyzed through a data driving optimization module, optimization suggestions are provided for the doctors, rehabilitation knowledge is continuously accumulated and updated, and therefore the rehabilitation effect is comprehensively improved; and the safety and effectiveness of training are ensured.

The application discloses a rehabilitation robot control method based on multi-source information perception and electronic equipment, and the method comprises the following steps: acquiring upper limb modal signals of a patient without motor function, wherein the upper limb modal signals comprise electroencephalogram signals, and eye movement signals and / or electromyogram signals; for each kind of upper limb modal signal, using an intention recognition model to perform motor intention recognition on the upper limb modal signal to obtain a corresponding motor intention recognition result; acquiring a confidence degree corresponding to each kind of upper limb modal signal; determining the motor intention of the patient according to the motor intention recognition result corresponding to each kind of upper limb modal signal and the corresponding confidence degree; determining a motor control signal of an upper limb rehabilitation exoskeleton according to the motor intention; and controlling the upper limb rehabilitation exoskeleton to move according to the motor control signal. The application can improve the motor intention recognition accuracy of the patient without motor function in the upper limb rehabilitation training process.

The utility model discloses a joint torque measuring device, which comprises a base provided with a slide rail and used for bearing a force measuring unit and abutting against a static limb joint of a joint to be measured; the force measuring unit is mounted on the sliding rail in a sliding manner and is used for receiving and measuring the acting force of a movable limb joint of the joint to be measured; and the displacement measuring unit is arranged on the base and is used for measuring the displacement of the force measuring unit. The device can be used for measuring torques of joints of all positions of a human body, such as joints of upper and lower limbs or fingers, the muscle strength and motion function conditions of the joints can be evaluated only by adjusting the sizes of all parts of the device to be matched with the sizes of the corresponding joints and limbs, and basis is provided for formulation and adjustment of rehabilitation treatment when the device is applied to the rehabilitation treatment; accordingly, a corresponding rehabilitation training scheme is designed, and the patient is helped to recover muscle strength and motion functions.