CN111816309A - Rehabilitation training prescription self-adaptive recommendation method and system based on deep reinforcement learning - Google Patents
Rehabilitation training prescription self-adaptive recommendation method and system based on deep reinforcement learning Download PDFInfo
- Publication number
- CN111816309A CN111816309A CN202010670625.4A CN202010670625A CN111816309A CN 111816309 A CN111816309 A CN 111816309A CN 202010670625 A CN202010670625 A CN 202010670625A CN 111816309 A CN111816309 A CN 111816309A
- Authority
- CN
- China
- Prior art keywords
- patient
- rehabilitation training
- prescription
- brain
- function
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1118—Determining activity level
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
- A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
- A61B5/14553—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases specially adapted for cerebral tissue
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/22—Ergometry; Measuring muscular strength or the force of a muscular blow
- A61B5/224—Measuring muscular strength
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4058—Detecting, measuring or recording for evaluating the nervous system for evaluating the central nervous system
- A61B5/4064—Evaluating the brain
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
- A61B5/4519—Muscles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Physics & Mathematics (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Molecular Biology (AREA)
- Veterinary Medicine (AREA)
- Heart & Thoracic Surgery (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- Neurology (AREA)
- Epidemiology (AREA)
- Data Mining & Analysis (AREA)
- Primary Health Care (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Databases & Information Systems (AREA)
- Dentistry (AREA)
- Physical Education & Sports Medicine (AREA)
- Physiology (AREA)
- Theoretical Computer Science (AREA)
- Computational Linguistics (AREA)
- General Engineering & Computer Science (AREA)
- Rheumatology (AREA)
- Artificial Intelligence (AREA)
- Neurosurgery (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Orthopedic Medicine & Surgery (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Psychology (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Optics & Photonics (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention provides a rehabilitation training prescription self-adaptive recommendation method and system based on deep reinforcement learning. The method comprises the following steps: 1) collecting basic information and medical record information of a patient; 2) acquiring cerebral cortex blood oxygen data of different brain areas of a patient and movement and myoelectric data of an affected limb of the patient; 3) calculating a brain function evaluation index in the exercise rehabilitation training process of the patient by utilizing the cerebral blood oxygen data, and calculating a motor function evaluation index and a muscle function evaluation index in the exercise rehabilitation training process of the patient by utilizing the exercise data and the myoelectric data so as to dynamically evaluate the brain function, the motor function and the muscle function of the patient; 4) inputting the evaluation indexes of the brain function, the motor function and the muscle function obtained in the step 3) into a pre-established deep reinforcement learning model so as to train the deep reinforcement learning model and automatically generate a rehabilitation training prescription; 5) feeding back the rehabilitation training prescription generated in the step 4) to the doctor and the patient for rehabilitation training. By utilizing the method and the system, the self-adaptive adjustment of the training prescription can be realized.
Description
Technical Field
The invention relates to the field of limb exercise rehabilitation training, in particular to a rehabilitation training prescription self-adaptive recommendation method and system based on deep reinforcement learning.
Background
More than 200 million stroke patients are newly added in China every year, and the trend of increasing year by year is shown, wherein 55-75% of stroke patients show dyskinesia. Meanwhile, the brain function damage caused by cerebral palsy, brain trauma and the like can also cause limb movement dysfunction, and heavy burden is brought to patients and families and society thereof. Rehabilitation training is the most important means for recovering the motor function of a patient. However, no matter the traditional artificial rehabilitation training or the rehabilitation training based on the rehabilitation training robot, the establishment of personalized rehabilitation training prescriptions aiming at different conditions of patients is an important condition for guaranteeing the training effect. But at present, the rehabilitation training prescription can only be made by a doctor according to the evaluation result of a patient, and the rehabilitation training prescription depends on the experience of the doctor to a great extent. Moreover, the functional evaluation of the patient is generally performed only several times of regular evaluations at different rehabilitation stages, so the updating of the training prescription also depends on the evaluation period, the updating of the training prescription may not follow the rehabilitation process of the patient, and the improvement of the rehabilitation efficiency is difficult. The development of artificial intelligence enables the rehabilitation training robot to utilize various sensing information to perform real-time function evaluation in the training process of a patient, the problem of long artificial evaluation period is solved, but a doctor is still required to adjust a prescription according to an evaluation result, and the workload of the doctor is undoubtedly greatly increased by frequently adjusting the prescription. On the other hand, active participation, fatigue degree and the like of a patient in rehabilitation training also have important influence on the training effect, the training efficiency under low active participation degree and fatigue state is often low, and manual adjustment of a training prescription is difficult to achieve timely adjustment according to the state of the patient in a single training process, so that the waste of training and treatment resources is caused to a certain extent.
Disclosure of Invention
Based on the above problems, the present invention aims to provide a rehabilitation training prescription adaptive recommendation method and system based on deep reinforcement learning, which performs real-time evaluation of brain function and motor function according to information of patient such as near infrared brain oxygen, motor, myoelectricity, etc., inputs various medical record information and function evaluation indexes into a pre-established deep reinforcement learning model for learning, and adaptively recommends a rehabilitation training prescription according to the functional state of the patient.
One aspect of the invention provides a rehabilitation training prescription self-adaptive recommendation method based on deep reinforcement learning, wherein the method comprises the following steps:
1) collecting basic information and medical record information of a patient;
2) acquiring cerebral cortex blood oxygen data of different brain areas of a patient by using near-infrared cerebral blood oxygen monitoring equipment, and acquiring motion data and myoelectric data of an affected limb of the patient;
3) calculating to obtain a brain function evaluation index in the exercise rehabilitation training process of the patient by utilizing the cerebral blood oxygen data, and calculating to obtain a motor function evaluation index and a muscle function evaluation index in the exercise rehabilitation training process of the patient by utilizing the motor data and the myoelectric data so as to dynamically evaluate the brain function, the motor function and the muscle function of the patient;
4) inputting the evaluation indexes of the brain function, the motor function and the muscle function obtained in the step 3) into a pre-established deep reinforcement learning model so as to train the deep reinforcement learning model and automatically generate a rehabilitation training prescription;
5) feeding back the rehabilitation training prescription generated in the step 4) to the doctor and the patient for rehabilitation training.
According to one embodiment, the training of the depth-enhanced learning model in the step 4) above includes training the depth-enhanced learning model by using the brain function, motor function and muscle function evaluation indexes as states, using the rehabilitation training prescription as an action, and using the function improvement condition after training by using the current rehabilitation training prescription as a reward according to the basic information and medical history information of the patient, and introducing the prior knowledge in the rehabilitation training prescription knowledge base in the training process to accelerate the training of the learning model.
According to an embodiment, the deep reinforcement learning model in the step 4) above may include a rehabilitation training prescription knowledge base based on a large amount of pre-input patient medical record information, a function evaluation index, and a training prescription issued by a doctor, and the model training is assisted by using prior knowledge in the knowledge base.
According to another embodiment, the deep reinforcement learning model in the above step 4) may perform reinforcement learning with the brain function, motor function, and muscle function evaluation indexes of the patient as states, with a rehabilitation training prescription as an action, and with a function improvement situation after training with the current prescription as a reward.
In one embodiment, the evaluation indexes of brain function, motor function and muscle function generated in the step 3) and the rehabilitation training prescription generated in the step 4) can be added to the knowledge base of the deep reinforcement learning model in real time, and the knowledge base is continuously expanded.
In one embodiment, the brain function evaluation index, the motor function evaluation index and the muscle function evaluation index obtained in the step 3) are input into a trained deep reinforcement learning model, Q values of different types or grades contained in each rehabilitation training prescription are output through model calculation, the prescription items with the highest Q values are combined, and the rehabilitation training prescription is automatically generated, wherein the Q values are numerical representations of the corresponding action advantages and disadvantages.
In another embodiment, the brain blood oxygen parameter data and the myoelectric data obtained in the step 2) above can be further analyzed to obtain the active participation and fatigue degree of the brain and the muscle in the training process of the patient, and the deep reinforcement learning model in the step 4) can adjust the training prescription according to the active participation and fatigue degree of the patient.
According to a preferred embodiment, the active participation degree of the brain and the muscle can be reflected by the activation degree of different brain areas and the amplitude information of different muscle electromyographic signals, and the fatigue degree of the muscle of the patient can be reflected by frequency domain information such as the average power frequency, the median frequency and the like of the electromyographic signals.
According to another preferred embodiment, the training of the deep reinforcement learning model in the step 4) above may include first initializing an experience pool and network weights, inputting medical records and state parameters of each function evaluation index, and if the current state cannot match the feature state in the prior knowledge base, selecting a training prescription according to the selected learning strategy; if the current-stage state can be matched with the characteristic state in the prior knowledge base, a training prescription is comprehensively judged and output according to the prior action Q value and the estimated action Q value, the information is stored in an experience pool, the steps are repeated to train the network, the network weight is automatically updated after each training to correct the network, and the selected learning strategy is a-greedy strategy.
According to another embodiment, the evaluation index generated in the step 3) and the rehabilitation training prescription generated in the step 4) can be added to the knowledge base of the deep reinforcement learning model in real time to expand the knowledge base.
According to another aspect of the present invention, there is provided a rehabilitation training prescription adaptive recommendation system based on deep reinforcement learning, including:
the human-computer interaction module is used for receiving the basic information and the case information of the patient and managing a pre-stored rehabilitation training knowledge base;
the near-infrared brain blood oxygen information acquisition module is used for acquiring near-infrared brain blood oxygen signals of a corresponding brain area of a patient and transmitting the acquired near-infrared brain blood oxygen signals to the evaluation analysis module;
the motion and physiological data acquisition module is used for acquiring motion signals and surface electromyographic signals of the limb of the patient in the motion process and transmitting the signals to the evaluation analysis module;
the evaluation analysis module is used for calculating to obtain brain function evaluation indexes and motion function evaluation indexes of different brain areas of the patient according to the near-infrared cerebral blood oxygen signals transmitted from the near-infrared cerebral blood oxygen acquisition module and the motion signals and surface electromyogram signals transmitted from the motion and physiological data acquisition module; and
and the intelligent learning and prescription recommending module is used for intelligently learning according to the patient medical record information in the human-computer interaction module and the evaluation indexes of the brain function and the motor function of the patient obtained by the evaluation and analysis module so as to output the rehabilitation training prescription and feeding back the rehabilitation training prescription to the doctor and the patient through the human-computer interaction module.
According to one embodiment, the intelligent learning and prescription recommendation module may comprise a pre-established rehabilitation training prescription knowledge base and a deep reinforcement learning model, wherein the rehabilitation training prescription knowledge base allows for modification and content addition through a knowledge base management module included in the human-computer interaction module.
According to another embodiment, the evaluation and analysis module can reflect the active participation degree of the brain and the muscle according to the activation degree of different brain areas and the amplitude information of different muscle electromyographic signals, and reflect the fatigue degree of the muscle of the patient by calculating frequency domain information such as average power frequency, median frequency and the like of the electromyographic signals.
The invention also provides a rehabilitation training prescription self-adaptive recommendation method based on deep reinforcement learning, which comprises the following steps:
1) basic information such as sex, age and medical history of a patient and medical record information such as etiology, brain injury condition, disease onset time, initial function level and disease course are input, and the medical record information comprises original image, medical examination data such as assay and initial evaluation data.
2) The near-infrared cerebral blood oxygen monitoring equipment is used for acquiring cerebral cortex blood oxygen data of different cerebral areas such as a movement area, a forehead leaf and the like in the movement training process of a patient, wherein the cerebral cortex blood oxygen data comprises local oxyhemoglobin concentration, deoxyhemoglobin concentration, blood oxygen saturation and the like. The inertia sensor, the surface electromyography sensor and the like are utilized to obtain the motion data of the affected limb of the patient, such as the acceleration, the angular velocity and the like, and the surface electromyography data of the relevant muscle.
3) In the exercise rehabilitation training process of the patient, brain function evaluation indexes such as the activation degrees, the activation modes, the functional connection between the brain areas, the laterality and the like of different brain areas are obtained by utilizing the brain blood oxygen data calculation, and the brain function of the patient is dynamically evaluated; calculating motion function evaluation indexes such as joint activity degree, motion smoothness and motion trajectory deviation by using motion data such as acceleration and angular velocity, and dynamically evaluating the motion function of the patient; obtaining muscle function indexes such as muscle strength, muscle tension and the like by utilizing the surface electromyography data; the active participation degree of the brain and the muscle is reflected by the activation degree of different brain areas and the amplitude information of different muscle electromyographic signals, and the fatigue degree of the muscle of a patient is reflected by frequency domain information such as the average power frequency, the median frequency and the like of the electromyographic signals.
4) A deep reinforcement learning model comprising a rehabilitation training prescription knowledge base based on a large amount of patient medical record information, function evaluation indexes and a doctor prescription is established in advance. Inputting the evaluation indexes of the brain function, the motor function and the muscle function obtained in the step 3) into a pre-established deep reinforcement learning model, and automatically generating a rehabilitation training prescription, which comprises a training task, a training scheme, a motor training mode, a training frequency, a training intensity and the like.
Specifically, the deep reinforcement learning model performs reinforcement learning by using the brain function and motor function evaluation indexes of the patient as states, using a rehabilitation training prescription as an action, and using a function improvement condition after training by using a current prescription as a reward. The training scheme in the rehabilitation training prescription comprises unilateral exercise training, limb linkage exercise training, exercise training + functional electrical stimulation, exercise training + transcranial magnetic stimulation, exercise training + virtual reality feedback and the like, the exercise training mode comprises active, passive, power-assisted, resistance and the like, the training frequency comprises the training times of each week and the training times of a single task in each training process, and the training intensity comprises the training time length of each training, the training task difficulty, the position, the intensity, the frequency and the like of the magnetoelectric stimulation.
Further, the deep reinforcement learning model adjusts the training prescription according to the active participation and fatigue degree of the patient obtained in the step 3).
5) Feeding back the rehabilitation training prescription generated in the step 4) to the doctor and the patient for rehabilitation training, and repeating the step 2).
Further, the evaluation index generated in the step 3) and the rehabilitation training prescription generated in the step 4) are added to the knowledge base of the deep reinforcement learning model in real time, and the knowledge base is continuously expanded.
The invention also provides a rehabilitation training prescription self-adaptive recommendation system based on deep reinforcement learning, which comprises:
and the human-computer interaction module comprises a medical record information input module, a rehabilitation training knowledge base management module and a recommended prescription display module.
Further, the medical record information input module is used for inputting basic information such as sex, age and medical history of the patient and medical record information such as etiology, brain injury condition, disease onset time, initial function level and course, and comprises original image, medical examination data such as assay and initial evaluation data.
The near-infrared brain blood oxygen information acquisition module comprises a near-infrared light source, a probe, a fixing device, an optical fiber, a data acquisition system and the like, is used for acquiring brain blood oxygen signals such as local oxyhemoglobin concentration, deoxyhemoglobin concentration, blood oxygen saturation and the like of a corresponding brain area of a patient, and transmits the acquired brain blood oxygen signals to the evaluation analysis module.
The motion and physiological data acquisition module comprises inertial sensors, myoelectric sensors and a data acquisition circuit which are distributed on different parts of the limb, and is used for acquiring acceleration, angular velocity and surface myoelectric signals of motion-related muscles of the limb of a patient in the motion process and transmitting the signals to the evaluation analysis module.
And the evaluation analysis module is used for calculating and obtaining brain function evaluation indexes such as activation degrees, activation modes, functional connections and laterality among the brain areas, joint activity degrees, motion smoothness, track deviation degrees and other motion function evaluation indexes and muscle function indexes such as muscle strength and muscle tension of the patient according to the brain blood oxygen signals such as the local oxyhemoglobin concentration, the deoxyhemoglobin concentration and the blood oxygen saturation transmitted by the near-infrared brain blood oxygen acquisition module and the acceleration, the angular velocity and the surface myoelectric signals transmitted by the motion and physiological data acquisition module. The active participation degree of the brain and the muscle is reflected by the activation degree of different brain areas and the amplitude information of different muscle electromyographic signals, and the fatigue degree of the muscle of a patient is reflected by calculating frequency domain information such as the average power frequency, the median frequency and the like of the electromyographic signals.
The intelligent learning and prescription recommending module comprises a rehabilitation training prescription knowledge base and a deep reinforcement learning model which are pre-established by utilizing information such as a large amount of patient medical record information, function evaluation indexes, training prescriptions issued by doctors and the like. The medical record information input module is used for inputting patient medical record information and the evaluation indexes of the brain function and the motor function of the patient, which are obtained by the evaluation and analysis module, so that the patient can learn intelligently, a rehabilitation training prescription based on the state of illness and the current functional state of the patient is output, and the rehabilitation training prescription is fed back to a doctor and the patient through the recommended prescription display module of the human-computer interaction module.
Specifically, the rehabilitation training prescription knowledge base in the intelligent learning and prescription recommending module allows a user with authority to modify the rehabilitation training prescription knowledge base through the knowledge base management module of the human-computer interaction module, so that the content is increased.
The invention has the beneficial effects that: by utilizing the method and the system, the training prescription can be adjusted in a real-time and self-adaptive manner according to the state of illness, motion function, training state and the like of the patient in the rehabilitation training process, the workload of a doctor for manually evaluating and adjusting the prescription is reduced, the adjustment is more dynamic and accurate compared with the prescription adjustment in a fixed period, and the efficiency of rehabilitation training is improved.
The foregoing summary is provided for the purpose of description only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the drawings and following detailed description.
Drawings
In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
Fig. 1 is a general configuration diagram of a rehabilitation training prescription adaptive recommendation system based on deep reinforcement learning according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a rehabilitation training prescription adaptive recommendation system based on deep reinforcement learning according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating an application of a rehabilitation training prescription adaptive recommendation method based on deep reinforcement learning according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating deep reinforcement learning model calculation according to an embodiment of the present invention.
Detailed Description
In the following, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.
As shown in fig. 1 and fig. 2, the rehabilitation training prescription adaptive recommendation system based on deep reinforcement learning generally comprises a human-computer interaction module 1, a near-infrared brain blood oxygen information acquisition module 2, a motion and physiological data acquisition module 3, an evaluation analysis module 4 and an intelligent learning and prescription recommendation module 5.
The human-computer interaction module 1 is used for receiving basic information and case information of a patient and managing a pre-stored rehabilitation training knowledge base, and comprises a medical record information entry module 11, a rehabilitation training knowledge base management module 12 and a recommended prescription display module 13.
The medical record information input module 11 is used for inputting basic information such as sex, age, medical history and the like of a patient and medical record information such as etiology, brain injury condition, disease onset time, initial function level, disease course and the like, and comprises original medical examination data such as images and tests and initial evaluation data.
The near-infrared cerebral blood oxygen information acquisition module 2 is used for acquiring near-infrared cerebral blood oxygen signals of a corresponding cerebral region of a patient, transmitting the acquired near-infrared cerebral blood oxygen signals to the evaluation analysis module 4, and mainly comprises a near-infrared light source 21, a probe 22, a fixing device (head cap) 23, an optical fiber 24 and a data acquisition system 25.
The near infrared light source 21 and the probe 22 are arranged at a fixed distance and fixed at corresponding positions of different brain regions of the head of the patient by a fixing device (in the figure, a head cap) 23. The near infrared light signals collected by the probe 22 are transmitted to the data collecting system 25 through the optical fiber 24, the cerebral blood oxygen signals such as the local oxyhemoglobin concentration, the deoxyhemoglobin concentration, the blood oxygen saturation and the like of the corresponding cerebral area of the patient are obtained through calculation according to the intensity of the light signals at different probe positions, and the collected cerebral blood oxygen information is transmitted to the evaluation and analysis module 4.
The movement and physiological data acquisition module 3 is used for acquiring movement signals and surface electromyogram signals of a patient in the limb movement process, transmitting the signals to the evaluation and analysis module 4, and comprises an inertial sensor 31, an electromyogram sensor 32 and a data acquisition circuit 33 which are distributed at different parts of the limb.
The inertial sensor 31 is used for acquiring information such as acceleration, angular velocity and the like of the patient in the limb movement process, the electromyographic sensor 32 is used for acquiring surface electromyographic signals of muscles related to movement of the patient in the limb movement process, and the information is synchronously acquired and transmitted to the evaluation analysis module 4 through the data acquisition circuit 33.
The evaluation analysis module 4 is configured to calculate, according to the cerebral blood oxygen signals, such as the local oxygenated hemoglobin concentration, the deoxygenated hemoglobin concentration, and the blood oxygen saturation, transmitted by the near-infrared cerebral blood oxygen collection module 2, and the acceleration, the angular velocity, and the surface myoelectric signal transmitted by the motion and physiological data collection module 3, brain function evaluation indexes, such as the activation degree, the activation pattern, the functional connection between the brain regions, and the laterality of the patient, the motion function evaluation indexes, such as the joint activity degree, the motion smoothness, and the trajectory deviation, and the muscle function indexes, such as the muscle strength and the muscle tension, of the patient in different brain regions. The active participation degree of the brain and the muscle is reflected by the activation degree of different brain areas and the amplitude information of different muscle electromyographic signals, and the fatigue degree of the muscle of a patient is reflected by calculating frequency domain information such as the average power frequency, the median frequency and the like of the electromyographic signals.
The intelligent learning and prescription recommending module 5 comprises a rehabilitation training prescription knowledge base and a deep reinforcement learning model which are pre-established by utilizing information such as a large amount of patient medical record information, function evaluation indexes, training prescriptions issued by doctors and the like. The system is used for intelligently learning according to the patient medical record information input by the medical record information input module 11 and the evaluation indexes of the brain function, the motor function and the muscle function of the patient obtained by the evaluation analysis module 4, outputting a rehabilitation training prescription based on the state of illness and the current functional state of the patient, and feeding back the rehabilitation training prescription to a doctor and the patient through the recommended prescription display module 13 of the human-computer interaction module, wherein the rehabilitation training prescription knowledge base in the intelligent learning and prescription recommendation module 5 allows a user with authority to modify through the knowledge base management module 12 of the human-computer interaction module, and content is added.
As shown in fig. 3, the rehabilitation training prescription self-adaptive recommendation method based on deep reinforcement learning of the present invention includes the following steps:
and S1, entering basic information and medical record information of the patient.
The basic information includes sex, age, medical history, etc., and the medical record information includes etiology, brain damage condition, disease onset time, initial function level, disease course, and original image, test, etc. medical examination data and initial evaluation data, etc.
And S2, acquiring near infrared cerebral blood oxygen, motion and physiological information.
The near-infrared brain blood oxygen acquisition module is used for acquiring cerebral cortex blood oxygen parameters of different brain areas such as a movement area, a forehead leaf and the like in the movement training process of a patient, wherein the cerebral cortex blood oxygen parameters comprise local oxyhemoglobin concentration, deoxyhemoglobin concentration, blood oxygen saturation and the like. The inertia sensor, the surface electromyography sensor and the like are utilized to obtain the motion data of the affected limb of the patient, such as the acceleration, the angular velocity and the like, and the surface electromyography data of the relevant muscle.
And S3, evaluating the brain function and the motor function of the patient.
In the exercise rehabilitation training process of the patient, brain function evaluation indexes such as the activation degrees, the activation modes, the functional connection between the brain areas, the laterality and the like of different brain areas are obtained by utilizing the brain blood oxygen data calculation, and the brain function of the patient is dynamically evaluated.
Specifically, the method comprises the following steps: performing continuous complex wavelet transform on the near-infrared cerebral blood oxygen signals acquired by each acquisition channel, and representing the brain activation degree by using a wavelet amplitude; obtaining a frequency domain wavelet phase matrix through calculation, and performing wavelet phase coherence calculation on each two-channel near-infrared cerebral blood oxygen signal to obtain cerebral function connection indexes including cerebral function connection strength and effect connection strength; and dividing the difference of the brain function indexes of a certain hemisphere and the contralateral hemisphere by the sum of the brain function indexes of the certain hemisphere and the contralateral hemisphere to calculate the lateral deviation coefficient.
And calculating motion functions and muscle function evaluation indexes such as joint activity, motion coordination, muscle strength and muscle tension by using the motion data such as acceleration and angular velocity and the surface myoelectric data, and dynamically evaluating the motion functions and muscle functions of the patient.
Specifically, the method comprises the following steps: and calculating the muscle force and the muscle tension of corresponding muscles by using the amplitude of the electromyographic signals according to the approximate linear relation between the electromyography and the muscle force. By establishing a human body dynamics model, calculating the joint angle and the motion trail by using the acceleration and the angular velocity data of different segments of limbs, taking the maximum joint angle in motion as the joint activity degree, and reflecting the motion coordination by using the motion trail, the method comprises the following steps: deviation degree of the motion track from the target track, smoothness of the motion track and the like.
The active participation degree of the brain and the muscle is reflected by the activation degree of different brain areas and the amplitude information of different muscle electromyographic signals, and the fatigue degree of the muscle of a patient is reflected by frequency domain information such as the average power frequency, the median frequency and the like of the electromyographic signals.
And S4, recommending the prescription by using a deep reinforcement learning model based on the medical record information and the function evaluation indexes.
A rehabilitation training prescription knowledge base containing a large amount of patient medical record information, function evaluation indexes and a training prescription mapping relation prescribed by a doctor is established in advance, and the mapping relation of the medical records, the function indexes and the training prescription is used as prior knowledge. A Deep reinforcement Learning model is constructed by adopting a DQN (Deep Q-Learning) algorithm, a rehabilitation training prescription is used as an action according to the basic information and medical record information of a patient and evaluation indexes of brain function, motor function and muscle function as states, the function improvement condition after training is carried out by adopting the current prescription is used as a reward, the Learning model is trained, and the priori knowledge in a rehabilitation training prescription knowledge base is introduced in the training process to accelerate the training of the model. The brain function evaluation indexes comprise the activation degrees, the activation modes, the functional connection among brain areas, the laterality and the like of different brain areas. The motor function evaluation indexes include joint mobility, motor coordination, muscle strength, muscle tension and the like.
Inputting the brain function evaluation indexes of activation degree, activation mode, functional connection and laterality between brain areas and the like, the motion function evaluation indexes of joint activity degree, motion smoothness, trajectory deviation degree and the like and the muscle function evaluation indexes of muscle strength, muscle tension and the like of various different brain areas obtained in the step S3 into a trained deep reinforcement learning model, calculating and outputting Q values of different types or grades contained in rehabilitation training prescription items of a training task, a training scheme, a motion training mode, training frequency, training intensity and the like through the model, combining the prescription items with the highest Q value, and automatically generating a rehabilitation training prescription. Wherein: the training scheme comprises different categories of unilateral exercise training, limb linkage exercise training, exercise training + functional electrical stimulation, exercise training + transcranial magnetic stimulation, exercise training + virtual reality feedback and the like; the exercise training mode comprises different categories of active, passive, power-assisted, resistance and the like; the training frequency comprises the training times per week, the training times of a single task in each training process and the like, the training times per week comprise different grades of 1-7 times and the like, and the training times of a single task in each training process comprise different grades of 1-5 times and the like; the training intensity comprises the training time, the training task difficulty, the magnetoelectric stimulation position, the strength, the frequency and the like, the training time comprises different grades such as 10 minutes, 20 minutes, 30 minutes, 40 minutes, 50 minutes, 60 minutes and the like, the task difficulty comprises different grades such as simple, medium, difficult and difficult, the magnetoelectric stimulation position comprises different classes, and the magnetoelectric stimulation strength and the frequency comprise different grades.
In the using process, the evaluation index generated in the step S3 and the rehabilitation training prescription generated in the step S4 are added to the knowledge base of the deep reinforcement learning model in real time, the knowledge base is continuously expanded, and the model is automatically corrected according to the comparison condition of the function evaluation index before and after the training by adopting the recommended prescription.
The specific calculation method of the deep reinforcement training model is shown in fig. 4: firstly, initializing an experience pool and network weight, inputting state parameters such as medical records, function evaluation indexes and the like, and selecting an action (a training prescription) according to a greedy strategy if the current state cannot be matched with the characteristic state in a priori knowledge base; and if the current-stage state can be matched with the characteristic state in the prior knowledge base, comprehensively judging the output action (training prescription) according to the prior action Q value and the estimated action Q value. Obtaining a reward value and the state of the next step according to the function improvement condition of the patient after the action (the training prescription) is adopted, storing the information into an experience pool, repeating the steps to train the network, and automatically updating the network weight after each training to correct the network.
On the other hand, the prior knowledge base of the rehabilitation training prescription contains the state characteristics of the active participation degree, the fatigue degree and the like of the patient, the deep reinforcement learning model adjusts the training prescription according to the active participation degree and the fatigue degree of the patient obtained in the step S3, and the training mode is changed or the training intensity is reduced under the condition that the active participation degree is reduced or the fatigue is generated after the patient is trained for a period of time by using the current training prescription is detected.
And S5, rehabilitation training prescription feedback.
Specifically, the method comprises the following steps: and (5) feeding back the rehabilitation training prescription generated in the step (S4) to the doctor and the patient for rehabilitation training, and repeating the step (S2).
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A rehabilitation training prescription self-adaptive recommendation method based on deep reinforcement learning comprises the following steps:
1) collecting basic information and medical record information of a patient;
2) acquiring cerebral cortex blood oxygen data of different brain areas of a patient, and acquiring motion data and myoelectric data of an affected limb of the patient;
3) calculating to obtain a brain function evaluation index in the exercise rehabilitation training process of the patient by utilizing the cerebral blood oxygen data, and calculating to obtain a motor function evaluation index and a muscle function evaluation index in the exercise rehabilitation training process of the patient by utilizing the motor data and the myoelectric data so as to dynamically evaluate the brain function, the motor function and the muscle function of the patient;
4) inputting the evaluation indexes of the brain function, the motor function and the muscle function obtained in the step 3) into a pre-established deep reinforcement learning model so as to train the deep reinforcement learning model and automatically generate a rehabilitation training prescription;
5) feeding back the rehabilitation training prescription generated in the step 4) to the doctor and the patient for rehabilitation training.
2. The rehabilitation training prescription self-adaptive recommendation method based on the depth-enhanced learning of claim 1, wherein the training of the depth-enhanced learning model in the step 4) comprises training the depth-enhanced learning model by taking the evaluation indexes of the brain function, the motor function and the muscle function as states, taking the rehabilitation training prescription as an action, taking the function improvement condition after training by adopting the current rehabilitation training prescription as a reward according to the basic information and the medical history information of the patient, and introducing the prior knowledge in the rehabilitation training prescription knowledge base in the training process to accelerate the training of the learning model.
3. The rehabilitation training prescription self-adaptive recommendation method based on deep reinforcement learning of claim 1, wherein the brain function evaluation index, the motor function evaluation index and the muscle function evaluation index obtained in step 3) are input into a trained deep reinforcement learning model, Q values of different categories or levels contained in each rehabilitation training prescription are output through model calculation, the prescription items with the highest Q values are combined, and the rehabilitation training prescription is automatically generated, wherein the Q values are numerical representations of the superiority and inferiority of corresponding actions.
4. The adaptive deep reinforcement learning-based rehabilitation training prescription recommendation method according to claim 1, wherein the brain blood oxygen parameter data and the myoelectric data obtained in step 2) are further analyzed to obtain the active participation degree and the fatigue degree of the brain and the muscle in the training process of the patient, and the deep reinforcement learning model in step 4) can adjust the training prescription according to the active participation degree and the fatigue degree of the patient.
5. The adaptive recommendation method for rehabilitation training prescription based on deep reinforcement learning of claim 4, wherein the degree of activation of different brain areas and the amplitude information of different muscle electromyographic signals are used for reflecting the active participation degree of the brain and the muscle, and the frequency domain information of the electromyographic signals is used for reflecting the fatigue degree of the muscle of the patient.
6. The rehabilitation training prescription self-adaptive recommendation method based on deep reinforcement learning of claim 1, wherein the training of the deep reinforcement learning model in the step 4) comprises the steps of firstly initializing an experience pool and network weights, inputting medical records and state parameters of each function evaluation index, and selecting a training prescription according to a selected learning strategy if the current state cannot be matched with the feature state in the prior knowledge base; if the current-stage state can be matched with the characteristic state in the prior knowledge base, the training prescription is comprehensively judged and output according to the prior action Q value and the estimated action Q value, the information is stored in an experience pool, the steps are repeated to train the network, and the network weight is automatically updated after each training to correct the network.
7. A rehabilitation training prescription self-adaptive recommendation system based on deep reinforcement learning comprises:
the human-computer interaction module is used for receiving the basic information and the case information of the patient and managing a pre-stored rehabilitation training knowledge base;
the near-infrared brain blood oxygen information acquisition module is used for acquiring near-infrared brain blood oxygen signals of a corresponding brain area of a patient and transmitting the acquired near-infrared brain blood oxygen signals to the evaluation analysis module;
the motion and physiological data acquisition module is used for acquiring motion signals and surface electromyographic signals of the limb of the patient in the motion process and transmitting the signals to the evaluation analysis module;
the evaluation analysis module is used for calculating and obtaining evaluation indexes of brain functions, motor functions and muscle functions of different brain areas of the patient according to the near-infrared cerebral blood oxygen signals transmitted from the near-infrared cerebral blood oxygen acquisition module and the motor signals and surface electromyographic signals transmitted from the motor and physiological data acquisition module; and
and the intelligent learning and prescription recommending module is used for intelligently learning according to the patient medical record information in the human-computer interaction module and the evaluation indexes of the brain function, the motor function and the muscle function of the patient, which are obtained by the evaluation and analysis module, so as to output a rehabilitation training prescription, and feeding the rehabilitation training prescription back to the doctor and the patient through the human-computer interaction module.
8. The adaptive deep reinforcement learning-based rehabilitation training prescription recommendation system according to claim 7, wherein the intelligent learning and prescription recommendation module comprises a pre-established rehabilitation training prescription knowledge base and a deep reinforcement learning model, wherein the rehabilitation training prescription knowledge base allows modification and content addition through a knowledge base management module included in the human-computer interaction module.
9. The adaptive recommendation system for rehabilitation training prescriptions based on deep reinforcement learning of claim 7, wherein the evaluation and analysis module reflects active participation of the brain and muscles according to the activation degrees of different brain areas and the amplitude information of different muscle electromyographic signals, and reflects the fatigue degree of the muscle of the patient by calculating the frequency domain information of the electromyographic signals.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010670625.4A CN111816309B (en) | 2020-07-13 | 2020-07-13 | Rehabilitation training prescription self-adaptive recommendation method and system based on deep reinforcement learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010670625.4A CN111816309B (en) | 2020-07-13 | 2020-07-13 | Rehabilitation training prescription self-adaptive recommendation method and system based on deep reinforcement learning |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111816309A true CN111816309A (en) | 2020-10-23 |
| CN111816309B CN111816309B (en) | 2022-02-01 |
Family
ID=72843157
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010670625.4A Active CN111816309B (en) | 2020-07-13 | 2020-07-13 | Rehabilitation training prescription self-adaptive recommendation method and system based on deep reinforcement learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111816309B (en) |
Cited By (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112274779A (en) * | 2020-10-28 | 2021-01-29 | 国家康复辅具研究中心 | Functional near-infrared guidance-based transcranial magnetic stimulation system and method |
| CN112684711A (en) * | 2020-12-24 | 2021-04-20 | 青岛理工大学 | Interactive identification method for human behavior and intention |
| CN112735585A (en) * | 2021-04-02 | 2021-04-30 | 四川京炜数字科技有限公司 | Arthritis rehabilitation diagnosis and treatment method and system based on neural network and machine learning |
| CN112883262A (en) * | 2021-02-04 | 2021-06-01 | 西南交通大学 | Schedule arrangement recommendation method based on deep reinforcement learning |
| CN112932898A (en) * | 2021-01-28 | 2021-06-11 | 东南大学 | On-demand auxiliary rehabilitation robot training method based on Bayesian optimization |
| CN112932474A (en) * | 2021-01-26 | 2021-06-11 | 国家康复辅具研究中心 | Rehabilitation training method and system based on cerebral blood oxygen and electromyographic signals |
| CN113077866A (en) * | 2021-03-19 | 2021-07-06 | 国家康复辅具研究中心 | Automatic recommendation method and system for limb exercise training scheme |
| CN113081671A (en) * | 2021-03-31 | 2021-07-09 | 东南大学 | Method for improving on-demand auxiliary rehabilitation training participation degree based on Bayesian optimization |
| CN113229831A (en) * | 2021-05-10 | 2021-08-10 | 燕山大学 | Movement function monitoring and management method based on myoelectricity and myooxygen signals |
| CN113255735A (en) * | 2021-04-29 | 2021-08-13 | 平安科技（深圳）有限公司 | Method and device for determining medication scheme of patient |
| CN113257416A (en) * | 2020-12-09 | 2021-08-13 | 浙江大学 | COPD patient personalized management and tuning method, device and equipment based on deep learning |
| CN113488178A (en) * | 2021-07-20 | 2021-10-08 | 上海弗莱特智能医疗科技有限公司 | Information generation method and device, storage medium and electronic equipment |
| CN113593670A (en) * | 2021-08-05 | 2021-11-02 | 江西省科学院应用物理研究所 | Prescription generation method and system for household direct current stimulation medical equipment |
| CN113647938A (en) * | 2021-08-18 | 2021-11-16 | 苏州大学 | Method and system for advanced detection of motion state change based on physiological signals |
| CN113786194A (en) * | 2021-08-31 | 2021-12-14 | 佛山科学技术学院 | Cerebral apoplexy motor function evaluation model construction method and motor function evaluation method |
| CN114202772A (en) * | 2021-12-07 | 2022-03-18 | 沈阳海昊大数据科技有限公司 | Reference information generation system and method based on artificial intelligence and intelligent medical treatment |
| CN114366557A (en) * | 2021-12-31 | 2022-04-19 | 华南理工大学 | Man-machine interaction system and method for lower limb rehabilitation robot |
| CN114431856A (en) * | 2022-01-28 | 2022-05-06 | 上海乾康医疗设备股份有限公司 | Neural feedback rehabilitation training system |
| CN114582463A (en) * | 2022-05-05 | 2022-06-03 | 成都尚医信息科技有限公司 | Personalized motion guidance system and method based on machine learning |
| CN114587387A (en) * | 2022-02-18 | 2022-06-07 | 金华送变电工程有限公司三为金东电力分公司 | Method and device for evaluating use fatigue of live working insulating operating rod |
| CN115153554A (en) * | 2022-08-17 | 2022-10-11 | 国家康复辅具研究中心 | Cognitive load evaluation method and system |
| CN115206484A (en) * | 2022-07-12 | 2022-10-18 | 国家康复辅具研究中心 | Cerebral apoplexy rehabilitation training system |
| CN115376694A (en) * | 2022-10-24 | 2022-11-22 | 佛山科学技术学院 | Multi-mode self-adaptive assessment system and method for limb dysfunction |
| CN115687898A (en) * | 2022-12-30 | 2023-02-03 | 苏州大学 | Gait parameter adaptive fitting method based on multi-mode signals |
| CN117409930A (en) * | 2023-12-13 | 2024-01-16 | 江西为易科技有限公司 | Medical rehabilitation data processing method and system based on AI technology |
| CN117912634A (en) * | 2024-03-20 | 2024-04-19 | 中国人民解放军总医院第八医学中心 | Postoperative rehabilitation training recommendation method for neurosurgery patients |
| CN118098507A (en) * | 2024-04-25 | 2024-05-28 | 山东大学 | Self-adaptive upper limb rehabilitation training control method and system based on multi-source data |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102813998A (en) * | 2012-08-01 | 2012-12-12 | 上海交通大学 | Multifunctional composite rehabilitation system for patient suffering from central nerve injury |
| CN105054927A (en) * | 2015-07-16 | 2015-11-18 | 西安交通大学 | Biological quantitative assessment method for active participation degree in lower limb rehabilitation system |
| WO2018010644A1 (en) * | 2016-07-12 | 2018-01-18 | 王春宝 | Autonomous training method and system |
| CN107961135A (en) * | 2016-10-19 | 2018-04-27 | 精工爱普生株式会社 | Rehabilitation training system |
| CN109222969A (en) * | 2018-10-31 | 2019-01-18 | 郑州大学 | A kind of wearable human upper limb muscular movement fatigue detecting and training system based on Fusion |
| CN110236876A (en) * | 2019-05-31 | 2019-09-17 | 西北工业大学 | A kind of upper limb ectoskeleton mechanical arm and the control method of rehabilitation training |
-
2020
- 2020-07-13 CN CN202010670625.4A patent/CN111816309B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102813998A (en) * | 2012-08-01 | 2012-12-12 | 上海交通大学 | Multifunctional composite rehabilitation system for patient suffering from central nerve injury |
| CN105054927A (en) * | 2015-07-16 | 2015-11-18 | 西安交通大学 | Biological quantitative assessment method for active participation degree in lower limb rehabilitation system |
| WO2018010644A1 (en) * | 2016-07-12 | 2018-01-18 | 王春宝 | Autonomous training method and system |
| CN107961135A (en) * | 2016-10-19 | 2018-04-27 | 精工爱普生株式会社 | Rehabilitation training system |
| CN109222969A (en) * | 2018-10-31 | 2019-01-18 | 郑州大学 | A kind of wearable human upper limb muscular movement fatigue detecting and training system based on Fusion |
| CN110236876A (en) * | 2019-05-31 | 2019-09-17 | 西北工业大学 | A kind of upper limb ectoskeleton mechanical arm and the control method of rehabilitation training |
Cited By (37)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112274779A (en) * | 2020-10-28 | 2021-01-29 | 国家康复辅具研究中心 | Functional near-infrared guidance-based transcranial magnetic stimulation system and method |
| CN113257416A (en) * | 2020-12-09 | 2021-08-13 | 浙江大学 | COPD patient personalized management and tuning method, device and equipment based on deep learning |
| CN113257416B (en) * | 2020-12-09 | 2022-09-13 | 浙江大学 | COPD patient personalized management and optimization device and equipment based on deep learning |
| CN112684711A (en) * | 2020-12-24 | 2021-04-20 | 青岛理工大学 | Interactive identification method for human behavior and intention |
| CN112932474A (en) * | 2021-01-26 | 2021-06-11 | 国家康复辅具研究中心 | Rehabilitation training method and system based on cerebral blood oxygen and electromyographic signals |
| CN112932474B (en) * | 2021-01-26 | 2022-04-01 | 国家康复辅具研究中心 | Rehabilitation training method and system based on cerebral blood oxygen and electromyographic signals |
| CN112932898A (en) * | 2021-01-28 | 2021-06-11 | 东南大学 | On-demand auxiliary rehabilitation robot training method based on Bayesian optimization |
| CN112883262A (en) * | 2021-02-04 | 2021-06-01 | 西南交通大学 | Schedule arrangement recommendation method based on deep reinforcement learning |
| CN113077866A (en) * | 2021-03-19 | 2021-07-06 | 国家康复辅具研究中心 | Automatic recommendation method and system for limb exercise training scheme |
| CN113081671A (en) * | 2021-03-31 | 2021-07-09 | 东南大学 | Method for improving on-demand auxiliary rehabilitation training participation degree based on Bayesian optimization |
| CN112735585A (en) * | 2021-04-02 | 2021-04-30 | 四川京炜数字科技有限公司 | Arthritis rehabilitation diagnosis and treatment method and system based on neural network and machine learning |
| CN113255735A (en) * | 2021-04-29 | 2021-08-13 | 平安科技（深圳）有限公司 | Method and device for determining medication scheme of patient |
| CN113255735B (en) * | 2021-04-29 | 2024-04-09 | 平安科技（深圳）有限公司 | Method and device for determining medication scheme of patient |
| WO2022227198A1 (en) * | 2021-04-29 | 2022-11-03 | 平安科技（深圳）有限公司 | Method and device for determining drug regimen of patient |
| CN113229831B (en) * | 2021-05-10 | 2022-02-01 | 燕山大学 | Movement function monitoring and management method based on myoelectricity and myooxygen signals |
| CN113229831A (en) * | 2021-05-10 | 2021-08-10 | 燕山大学 | Movement function monitoring and management method based on myoelectricity and myooxygen signals |
| CN113488178A (en) * | 2021-07-20 | 2021-10-08 | 上海弗莱特智能医疗科技有限公司 | Information generation method and device, storage medium and electronic equipment |
| CN113488178B (en) * | 2021-07-20 | 2022-07-12 | 上海弗莱特智能医疗科技有限公司 | Information generation method and device, storage medium and electronic equipment |
| CN113593670A (en) * | 2021-08-05 | 2021-11-02 | 江西省科学院应用物理研究所 | Prescription generation method and system for household direct current stimulation medical equipment |
| CN113647938A (en) * | 2021-08-18 | 2021-11-16 | 苏州大学 | Method and system for advanced detection of motion state change based on physiological signals |
| CN113786194A (en) * | 2021-08-31 | 2021-12-14 | 佛山科学技术学院 | Cerebral apoplexy motor function evaluation model construction method and motor function evaluation method |
| CN114202772A (en) * | 2021-12-07 | 2022-03-18 | 沈阳海昊大数据科技有限公司 | Reference information generation system and method based on artificial intelligence and intelligent medical treatment |
| CN114366557A (en) * | 2021-12-31 | 2022-04-19 | 华南理工大学 | Man-machine interaction system and method for lower limb rehabilitation robot |
| CN114431856A (en) * | 2022-01-28 | 2022-05-06 | 上海乾康医疗设备股份有限公司 | Neural feedback rehabilitation training system |
| CN114587387A (en) * | 2022-02-18 | 2022-06-07 | 金华送变电工程有限公司三为金东电力分公司 | Method and device for evaluating use fatigue of live working insulating operating rod |
| CN114587387B (en) * | 2022-02-18 | 2024-05-28 | 金华送变电工程有限公司三为金东电力分公司 | Live working insulating operation rod use fatigue evaluation method and device |
| CN114582463A (en) * | 2022-05-05 | 2022-06-03 | 成都尚医信息科技有限公司 | Personalized motion guidance system and method based on machine learning |
| CN115206484A (en) * | 2022-07-12 | 2022-10-18 | 国家康复辅具研究中心 | Cerebral apoplexy rehabilitation training system |
| CN115153554A (en) * | 2022-08-17 | 2022-10-11 | 国家康复辅具研究中心 | Cognitive load evaluation method and system |
| CN115376694A (en) * | 2022-10-24 | 2022-11-22 | 佛山科学技术学院 | Multi-mode self-adaptive assessment system and method for limb dysfunction |
| CN115687898A (en) * | 2022-12-30 | 2023-02-03 | 苏州大学 | Gait parameter adaptive fitting method based on multi-mode signals |
| CN115687898B (en) * | 2022-12-30 | 2023-07-11 | 苏州大学 | Gait parameter self-adaptive fitting method based on multi-mode signals |
| CN117409930A (en) * | 2023-12-13 | 2024-01-16 | 江西为易科技有限公司 | Medical rehabilitation data processing method and system based on AI technology |
| CN117409930B (en) * | 2023-12-13 | 2024-02-13 | 江西为易科技有限公司 | Medical rehabilitation data processing method and system based on AI technology |
| CN117912634A (en) * | 2024-03-20 | 2024-04-19 | 中国人民解放军总医院第八医学中心 | Postoperative rehabilitation training recommendation method for neurosurgery patients |
| CN117912634B (en) * | 2024-03-20 | 2024-05-24 | 中国人民解放军总医院第八医学中心 | Postoperative rehabilitation training recommendation method for neurosurgery patients |
| CN118098507A (en) * | 2024-04-25 | 2024-05-28 | 山东大学 | Self-adaptive upper limb rehabilitation training control method and system based on multi-source data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111816309B (en) | 2022-02-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111816309B (en) | Rehabilitation training prescription self-adaptive recommendation method and system based on deep reinforcement learning | |
| KR102014176B1 (en) | Brain training simulation system based on behavior modeling | |
| Biddiss et al. | Upper limb prosthesis use and abandonment: a survey of the last 25 years | |
| JP5320543B2 (en) | Brain function enhancement support device and brain function enhancement support method | |
| CN113101134B (en) | Child lower limb movement auxiliary rehabilitation system based on power exoskeleton | |
| CN113647939B (en) | Artificial intelligence rehabilitation evaluation and training system for spinal degenerative diseases | |
| CN110720908B (en) | Muscle injury rehabilitation training system based on vision-myoelectricity biofeedback and rehabilitation training method using system | |
| CN110931104A (en) | Upper limb rehabilitation robot intelligent training system and method based on machine learning | |
| CN106821308A (en) | The method and apparatus of contraction movement assessment under a kind of skeletal muscle stimulating | |
| JP5870465B2 (en) | Brain function training apparatus and brain function training program | |
| CN116312951B (en) | Exercise function assessment method and system based on multi-modal coupling analysis | |
| KR102235716B1 (en) | Learning disorder diagnosing/cure apparatus and method using virtual reality | |
| JP6029236B2 (en) | Biological function diagnostic apparatus and program | |
| CN116570283A (en) | Perioperative patient emotion monitoring system and method | |
| CN115762708A (en) | Lower limb rehabilitation scheme design method and device using multi-source information and case reasoning | |
| Rajgure et al. | A Novel Approach on Yoga Posture Identification Using Machine Learning | |
| JP2023549317A (en) | Method and system for sensor signal dependent interaction generation during medical imaging process | |
| CN110363242B (en) | Brain consciousness multi-classification method and system based on support vector machine | |
| Lu et al. | A hybrid deep learning framework for estimation of elbow flexion force via electromyography | |
| Lu et al. | Intelligent monitoring of Taijiquan exercise based on fuzzy control theory | |
| Klaassen et al. | The development and evaluation of an arm usage coach for stroke survivors | |
| Vartak et al. | Cognitive state estimation for adaptive learning systems using wearable physiological sensors | |
| Shan et al. | Artificial neural networks and center-of-pressure modeling: a practical method for sensorimotor-degradation assessment | |
| Jasuja et al. | Development of an extensible, wireless framework for personalized muscle rehabilitation | |
| CN116531663B (en) | Respiratory function monitoring, regulating and intervening method, device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20220809 Address after: 212300 Danyang hi tech Pioneer Park Phase I, South Third Ring Road, Danyang City, Zhenjiang City, Jiangsu Province Patentee after: DANYANG HUICHUANG MEDICAL EQUIPMENT Co.,Ltd. Address before: 100176 1 ronghua Middle Road, Daxing District economic and Technological Development Zone, Beijing Patentee before: NATIONAL RESEARCH CENTER FOR REHABILITATION TECHNICAL AIDS |