WO2020119319A1 - 一种多模态交互的上肢康复机器人训练*** - Google Patents

一种多模态交互的上肢康复机器人训练*** Download PDF

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Publication number
WO2020119319A1
WO2020119319A1 PCT/CN2019/114915 CN2019114915W WO2020119319A1 WO 2020119319 A1 WO2020119319 A1 WO 2020119319A1 CN 2019114915 W CN2019114915 W CN 2019114915W WO 2020119319 A1 WO2020119319 A1 WO 2020119319A1
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Prior art keywords
training
rehabilitation
patient
module
muscle
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PCT/CN2019/114915
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English (en)
French (fr)
Inventor
谢龙汉
蔡思祺
李国峰
黄双远
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华南理工大学
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Priority to US17/298,031 priority Critical patent/US20220095989A1/en
Publication of WO2020119319A1 publication Critical patent/WO2020119319A1/zh

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Definitions

  • the present invention relates to the field of upper limb rehabilitation, and in particular to a multi-modal interactive upper limb rehabilitation robot training system.
  • the present invention discloses a multimodal interactive upper limb rehabilitation robot training system, including an EEG signal acquisition and processing module, a robot body module, an EMG signal acquisition and processing module, a rehabilitation training evaluation module, and a virtual reality module.
  • the multimodal interactive upper limb rehabilitation robot training method provided by the present invention adopts EEG control, extracts characteristic parameters of EEG signals through the EEG signal collection and processing module, triggers rehabilitation training, and fully reflects the patient's rehabilitation training intention; Analyze the EMG signal, reflect the muscle function status of the affected limb in real time, combine the force feedback to reflect the strength of the muscle, and the machine vision captures the posture of the affected limb.
  • the results of the rehabilitation training are used as the feedback parameters of the robot body to adjust the training actions, and on the one hand, they are input to the virtual reality module and displayed to the patient in real time, realizing the safety of upper limb rehabilitation training , Scientific and data; At the same time using virtual reality technology to improve the fun of the entire rehabilitation training process, mobilize the enthusiasm and initiative of patients.
  • a multi-modal interactive upper limb rehabilitation robot training system includes an EEG signal acquisition and processing module, a robot body module, an integrated data collection and processing module for the affected limb muscle, and a rehabilitation training evaluation module And virtual reality modules,
  • the EEG signal collection and processing module is used to extract the relevant characteristic parameters of the patient's EEG signal to obtain the patient's action intention, and then trigger the rehabilitation training action of the robot body module;
  • the robot ontology module performs training actions under the trigger of the electroencephalogram signal collection and processing module and the quantitative evaluation parameters fed back by the rehabilitation training evaluation module to help the patient's affected limb perform spatial exercises to complete the rehabilitation training process;
  • the integrated data collection and processing module of the affected limb muscle is used to collect and obtain the integrated data index of the affected limb;
  • the rehabilitation training evaluation module is used to process and analyze the muscle comprehensive data indicators of the affected limb.
  • the muscle comprehensive data indicators include muscle function status indicators, muscle strength indicators and muscle coordination indicators.
  • the three indicators are respectively Score and calculate the total score of the three indicators to obtain the quantitative evaluation result of the patient's arm rehabilitation training;
  • the virtual reality module is used to display a virtual environment for rehabilitation training of affected limbs, and stimulates the initiative of patient training through sensory sense, and the parameter input comes from the robot body module and the rehabilitation training evaluation module, respectively.
  • the muscle strength index is reflected by force feedback data; the muscle function index is reflected by characteristic parameters including amplitude of EMG signal, integral EMG value, median frequency and average frequency; muscle coordination index The patient posture and position information captured by machine vision is reflected.
  • scoring the three indicators respectively specifically includes:
  • the score of the muscle function state index is obtained by comparing the characteristic values of the myoelectric signal of the patient and the healthy person, the greater the difference between the two, the lower the score obtained by the patient; the score of the muscle strength index is passed Force feedback data is obtained.
  • the larger the value of force the lower the degree of active training of the patient and the lower the score obtained by the patient; the score of the muscle coordination index is calculated by comparing the exercise data of the patient and the healthy person to calculate the affected limb movement.
  • the magnitude of the error of the moving speed and trajectory the greater the error, the lower the score obtained by the patient; by calculating the total score of the three indicators, a quantitative evaluation result of the patient’s arm rehabilitation exercise training is obtained. The effect is proportional.
  • the integrated data collection and processing module of the affected limb muscles includes an EMG signal collection and processing module, a force feedback device, and a machine vision device,
  • the EMG signal collection and processing module obtains the muscle function state index of the affected limb by extracting the EMG signal characteristics of the patient's upper limb;
  • the force feedback device obtains the muscle strength index of the affected limb through the obtained force feedback data
  • the machine vision device obtains the muscle coordination index of the affected limb through the captured position data of the affected limb.
  • the EEG signal acquisition and processing module includes an EEG signal acquisition part, a multi-channel analog front-end amplifier circuit part, a digital circuit part supporting multi-channel acquisition and an information processing part, the EEG signal acquisition EEG signals of some collected patients are sent to the multi-channel analog front-end circuit for amplification, and the analog quantity is converted into a digital quantity to improve the anti-interference of the signal during transmission; the digital circuit supporting multi-channel acquisition buffers and converts the digital signal Then, it is transferred to the information processing part for storage and restoration. Finally, corresponding processing is performed in the information processing part, including feature extraction and pattern recognition, and the output signal triggers the operation of the robot body module.
  • the robot body module when the robot body module is triggered by the signal output by the electroencephalogram signal collection and processing module, it is used to drive the affected limb of the patient for passive rehabilitation, active rehabilitation, active and passive rehabilitation training, and the training data Transmission to the virtual reality module as input parameters.
  • the EMG signal collection and processing module includes a pre-amplifier circuit part, a notch filter, a filter part and a signal analysis part, the pre-amplifier circuit part is used for the acquired EMG signal Amplify; the notch filter is used to reduce 50Hz power frequency interference; the filter part is used to filter out low-frequency and high-frequency noise in the EMG signal; the signal analysis part is performed by the EMG signal Time domain analysis and frequency domain analysis, extracting the characteristic parameters including amplitude, integral EMG value, median frequency and average frequency to reflect the muscle function state of the patient's upper limbs.
  • the output data of the rehabilitation training evaluation module is input as a feedback parameter of the rehabilitation training to the robot body module to adjust the training action, on the one hand, as a quantitative indicator of the training situation. Enter the virtual reality module and display it to the patient intuitively.
  • the virtual reality module includes a training scene display part and a dialogue interaction part, and its input parameters come from the robot body module and the rehabilitation training evaluation module, which stimulates the senses of the patient through vision and voice to stimulate the enthusiasm and initiative of rehabilitation training Sex.
  • the present invention fully reflects the rehabilitation intention of the patient through EEG control, combined with EMG signal feedback, real-time acquisition of muscle function status of the affected limb, coupled with force feedback and posture position information, provides comprehensive rehabilitation training evaluation, while
  • the display interaction through the virtual reality module constitutes a multimodal interactive upper limb rehabilitation robot training system, which realizes the safety, science, data and fun of the rehabilitation process of the affected limb.
  • FIG. 1 is a schematic diagram of a multimodal interactive upper limb rehabilitation robot training system.
  • a multi-modal interactive upper limb rehabilitation robot training system the upper limb rehabilitation robot training system includes an electroencephalogram signal collection and processing module 1, a robot body module 2, and comprehensive data collection of affected limb muscles And processing module, comprehensive data collection and processing module for affected limb muscles, rehabilitation training evaluation module 4 and virtual reality module 5,
  • the EEG signal collection and processing module 1 is used to extract the relevant characteristic parameters of the patient's EEG signal to obtain the patient's action intention, and then trigger the rehabilitation training action of the robot body module 2;
  • the trigger of the robot body module 2 in the EEG signal collection and processing module 1 and the rehabilitation training The evaluation module 4 feeds back the quantified evaluation parameters to perform training actions to help the patient's affected limb perform spatial exercise to complete the rehabilitation training process;
  • the comprehensive muscle data collection and processing module of the affected limb is used to collect and obtain comprehensive data indicators of the affected limb, and the comprehensive muscle data indicators include muscle function status indicators, muscle strength indicators, muscle coordination indicators, and
  • the integrated data collection and processing module of the affected limb muscles includes EMG signal collection and processing module 3, force feedback device, machine vision device,
  • the EMG signal collection and processing module 3 obtains the muscle function state index of the affected limb by extracting the EMG signal characteristics of the patient's upper limb;
  • the force feedback device obtains the muscle strength index of the affected limb through the obtained force feedback data
  • the posture position data of the affected limb captured by the machine vision device is compared with the movement data of the healthy person at the same time, and the muscle coordination index of the affected limb is obtained by analyzing and calculating the error magnitude of the movement speed and trajectory of the affected limb.
  • the rehabilitation training evaluation module 4 is used for processing and analyzing the muscle function state index, muscle strength index, and muscle coordination index of the affected limb, and scoring the three indexes respectively, which specifically includes: the muscle function status
  • the score of the index is obtained by comparing the characteristic values of the EMG signals of the patient and the healthy person. The larger the difference between the two, the lower the score obtained by the patient; the score of the muscle strength index is obtained from the force feedback data, the greater the value of the force Large, indicating that the lower the patient's active training degree, the lower the score obtained by the patient; the score of the muscle coordination index is calculated by comparing the movement data of the patient and the healthy person, and calculating the error size of the movement speed and trajectory of the affected limb.
  • the score is proportional to the exercise capacity of the affected limb and the effect of rehabilitation training, that is: the higher the score, the exercise of the affected limb The better the ability and the effectiveness of rehabilitation training.
  • the virtual reality module 5 is used to display a virtual environment for rehabilitation training of affected limbs, and stimulates the initiative of patient training through sensory, and the parameter input comes from the robot body module 2 and the rehabilitation training evaluation module 4, respectively.
  • the EEG signal acquisition and processing module 1 includes an EEG signal acquisition part, a multi-channel analog front-end amplifier circuit part, a digital circuit part that supports multi-channel acquisition, and an information processing part, and the EEG signal acquisition part acquires
  • the patient's EEG signal is sent to the multi-channel analog front-end circuit for amplification, and the analog quantity is converted into a digital quantity to improve the anti-interference of the signal during transmission;
  • the EEG signal collection and processing module 1 acquires the patient’s action intention by collecting the patient’s EEG signal, analyzing and extracting the relevant characteristic parameters in the patient’s EEG signal, and transmits it to the robot body module 2, triggering
  • the actions of rehabilitation training fully reflect the patient's subjective wishes.
  • the robot body module 2 is connected to the patient’s arm, and when triggered by the signal output by the electroencephalogram signal collection and processing module 1, the patient is assisted in completing the rehabilitation training action, and can provide targeted treatment for patients at different rehabilitation stages
  • the training method drives the affected limb of the patient for passive rehabilitation, active rehabilitation and active-passive rehabilitation training, and transmits the training data to the virtual reality module 5 as an input parameter.
  • the EMG signal collection and processing module 3 includes a pre-amplifier circuit part, a notch filter, a filter part and a signal analysis part, the pre-amplifier circuit part is used to amplify the acquired EMG signal
  • the notch filter is mainly used to reduce 50Hz power frequency interference; the filter part is used to filter out low-frequency and high-frequency noise in the EMG signal; the signal analysis part is carried out on the EMG signal Time domain analysis and frequency domain analysis, extract the characteristic parameters including amplitude, integrated EMG value, median frequency and average frequency to reflect the muscle function status of the patient's upper limbs, and transmit the analysis results to the rehabilitation training evaluation module 4
  • the rehabilitation training evaluation module 4 analyzes and processes the muscle strength index, muscle function index and muscle coordination index, wherein the muscle strength index is reflected by the force feedback data, and the muscle function index includes the amplitude of the EMG signal and the integral muscle The characteristic parameters of electrical value, median frequency and average frequency are reflected, and the muscle coordination index is reflected by the patient's posture and position information captured by machine vision.
  • the three indexes are scored separately to calculate the total score as a comprehensive quantitative evaluation result of the rehabilitation training of the affected limb
  • the evaluation data is input to the robot body module 2 as a feedback parameter of rehabilitation training to adjust the training action, to achieve scientific and safe rehabilitation training, and on the one hand, it is input to the virtual reality module 5 as a quantitative indicator of the training situation and displayed intuitively To patients, to ensure the initiative of patient training and improve patient participation in rehabilitation.
  • the virtual reality module 5 includes a training scene display part and a dialogue interaction part, the input parameters of which come from the robot body module 2 and the rehabilitation training evaluation module 4, which stimulates the patient’s senses through vision and voice to stimulate the enthusiasm and initiative of rehabilitation training Sex.
  • the virtual reality module 5 is used to display the virtual environment of the patient's arm for rehabilitation training, and at the same time combine voice interaction to stimulate the patient's senses at multiple levels, realize the interaction between the system and the patient, and enhance the fun of rehabilitation training.
  • the present invention constitutes a multi-modal interactive upper limb rehabilitation robot training system through an EEG signal collection and processing module, a robot body module, an EMG signal collection and processing module, a rehabilitation training evaluation module, and a virtual reality module, which realizes The safety, science, data and fun of the upper limb rehabilitation training process.
  • the patient’s EEG signal is collected, and then amplified by a multi-channel analog front-end amplifier circuit, and the analog quantity is converted into a digital quantity
  • the electrical signal collection and processing module 3 collects the myoelectric signal of the affected limb in real time, analyzes and obtains the functional status index of the muscle and transmits it to the rehabilitation training evaluation module 4, and the force feedback device collects the force between the affected limb and the robot body module
  • the quantitative evaluation parameter output is input to the robot body module 2 as the feedback parameter of the rehabilitation training to adjust the training action to achieve scientific and safe rehabilitation training; on the one hand, it is input to the virtual reality module 5 as a quantitative indicator of the training situation, intuitively Show to the patient.
  • the virtual reality module 5 displays a virtual scene of the patient's arm movement, enhances the interest of training, and at the same time performs voice interaction with the patient to stimulate the patient's initiative in training.

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Abstract

一种多模态交互的上肢康复机器人训练***，包括脑电信号采集与处理模块（1）、机器人本体模块（2）、患肢肌肉综合数据采集及处理模块、康复训练评估模块（4）和虚拟现实模块（5）；脑电信号采集与处理模块（1）通过脑电信号反映患者的动作意图，触发康复训练；机器人本体模块（2）辅助患肢进行康复运动；患肢肌肉综合数据采集及处理模块采集并得到患肢的综合数据指标；康复训练评估模块（4）用于对患肢的肌肉综合数据指标进行处理分析，得到患者手臂康复运动训练的量化评估参数；虚拟现实模块（5）用于显示康复训练的虚拟环境，通过场景显示和对话与患者互动。该上肢康复机器人训练***提供的多模态交互的上肢康复机器人训练方法实现了上肢康复训练的安全化、科学化和趣味化。

Description

一种多模态交互的上肢康复机器人训练*** 技术领域
[0001] 本发明涉及上肢康复领域领域， 具体涉及一种多模态交互的上肢康复机器人训 练***。
背景技术
[0002] 随着经济发展和生活水平的提高人口老龄化问题日益凸显。 脑卒中发病率逐年 递增， 致死致残率高， 严重危害人类健康， 患者中风后其运动功能的重建和康 复引起了普遍关注。 通过大剂量的运动康复治疗对脑卒中患者的恢复具有关键 作用， 但中风患者数量过于庞大， 康复治疗师严重匮乏， 传统的人工物理疗法 无法满足巨大的康复需求。
[0003] 同时， 5见有康复训练缺乏客观数据的记录， 没有量化的评估机制， 不能进行有 效的反馈和改进训练计划， 影响训练效果。 此外， 由于康复训练动作单一重复 ， 往往使患者感觉枯燥， 导致康复积极性差甚至不配合等情况。
[0004] 因此， 有必要提供一种新的上肢康复机器人训练***， 以实现了上肢康复训练 的安全化、 科学化和数据化； 提高整个康复训练过程的趣味性， 调动患者的积 极性与主动性。
发明概述
技术问题
问题的解决方案
技术解决方案
[0005] 本发明公开了一种多模态交互的上肢康复机器人训练***， 包括脑电信号采集 与处理模块、 机器人本体模块、 肌电信号采集与处理模块、 康复训练评估模块 和虚拟现实模块。 本发明提供的多模态交互的上肢康复机器人训练方法采用脑 电控制， 通过脑电信号采集与处理模块提取脑电信号的特征参数， 触发康复训 练， 充分地反映患者的康复训练意图； 通过采集分析肌电信号， 实时反映患肢 的肌肉功能状态， 结合力反馈反映肌力大小， 机器视觉捕捉患肢的姿态位置反 映患肢肌肉协同情况， 综合进行康复训练评估； 康复训练评估结果一方面作为 机器人本体的反馈参数， 调节训练动作， 一方面输入到虚拟现实模块实时显示 给患者， 实现了上肢康复训练的安全化、 科学化和数据化； 同时利用虚拟现实 技术， 提高整个康复训练过程的趣味性， 调动患者的积极性与主动性。
[0006] 为实现上述目的， 本发明的技术方案如下。
[0007] 一种多模态交互的上肢康复机器人训练***， 所述上肢康复机器人训练***包 括脑电信号采集与处理模块、 机器人本体模块、 患肢肌肉综合数据采集及处理 模块、 康复训练评估模块和虚拟现实模块，
[0008] 所述的脑电信号采集与处理模块用于提取患者脑电信号中的相关特征参量来获 得患者的动作意图， 进而触发所述机器人本体模块的康复训练动作；
[0009] 所述机器人本体模块在所述脑电信号采集与处理模块的触发及所述康复训练评 估模块反馈的量化评估参数下进行训练动作帮助患者的患肢进行空间运动完成 康复训练过程；
[0010] 所述患肢肌肉综合数据采集及处理模块用于采集并得到患肢的综合数据指标；
[0011] 所述康复训练评估模块用于对患肢的肌肉综合数据指标进行处理分析， 所述的 肌肉综合数据指标包括肌肉功能状态指标、 肌力大小指标和肌肉协同指标， 对 三项指标分别进行打分并计算三项指标的总分， 得到患者手臂康复运动训练的 量化评估结果；
[0012] 所述虚拟现实模块用于显示患肢康复训练的虚拟环境， 通过感官刺激患者训练 的主动性， 参数输入分别来自机器人本体模块和康复训练评估模块。
[0013] 进一步地， 所述肌力大小指标由力反馈数据反映； 所述肌肉功能指标由包括肌 电信号的振幅、 积分肌电值、 中值频率和平均频率的特征参数反映； 肌肉协同 指标由机器视觉捕获的患者姿态位置信息反映。
[0014] 进一步地， 所述对三项指标分别进行打分具体包括：
[0015] 所述的肌肉功能状态指标的分数通过对比患者与健康人的肌电信号的特征值获 得， 两者差值越大， 患者所得分数越低； 所述的肌力大小指标的分数通过力反 馈数据获得， 力的数值越大， 说明患者主动训练程度越低， 患者所得分数越低 ; 所述的肌肉协同指标的分数通过对比患者与健康人的运动数据， 计算患肢运 动速度和轨迹的误差大小， 误差越大， 患者所得分数越低； 通过计算三项指标 的总分， 得到患者手臂康复运动训练的量化评估结果， 分数的高低与患肢运动 能力、 康复训练的效果成正比。
[0016] 进一步地， 所述的患肢肌肉综合数据采集及处理模块包括肌电信号采集与处理 模块、 力反馈装置、 机器视觉装置，
[0017] 所述肌电信号采集与处理模块通过提取患者上肢的肌电信号特征， 获得患肢的 所述肌肉功能状态指标；
[0018] 所述的力反馈装置通过获取的力反馈数据得到患肢的所述肌力大小指标；
[0019] 所述的机器视觉装置通过捕获的患肢姿态位置数据得到患肢的所述肌肉协同指 标。
[0020] 进一步地， 所述的脑电信号采集与处理模块包括脑电信号采集部分、 多通道模 拟前端放大电路部分、 支持多通道采集的数字电路部分和信息处理部分， 所述 脑电信号采集部分采集患者的脑电信号输送至多通道模拟前端电路放大， 并且 将模拟量转换为数字量， 提高信号在传输中的抗干扰性； 所述支持多通道采集 的数字电路对数字信号进行缓存和转换， 然后传输至信息处理部分进行储存和 还原； 最后在信息处理部分中进行相应处理， 包括特征提取与模式识别， 输出 信号触发机器人本体模块的运行。
[0021] 进一步地， 所述的机器人本体模块由脑电信号采集与处理模块输出的信号触发 时， 用于带动患者的患肢进行被动康复、 主动康复及主被动康复训练， 并将训 练的数据传输至虚拟现实模块作为输入参数。
[0022] 进一步地， 所述的肌电信号采集与处理模块包括前置放大电路部分、 陷波器、 滤波器部分和信号分析部分， 所述前置放大电路部分用于对获取的肌电信号进 行放大； 所述的陷波器用于降低 50Hz工频干扰； 所述的滤波器部分用于来滤除 肌电信号中的低频和高频噪声； 所述的信号分析部分通过对肌电信号进行时域 分析和频域分析， 提取包括振幅、 积分肌电值、 中值频率和平均频率的特征参 数反映患者上肢的肌肉功能状态。
[0023] 进一步地， 所述的康复训练评估模块的输出数据一方面作为康复训练的反馈参 数输入到机器人本体模块调节训练的动作， 一方面作为训练情况的量化指标输 入到虚拟现实模块， 直观地显示给患者。
[0024] 进一步地， 所述的虚拟现实模块包括训练场景显示部分和对话交互部分， 其输 入参数来自机器人本体模块和康复训练评估模块， 通过视觉和语音刺激患者感 官， 激发康复训练的积极性与主动性。
发明的有益效果
有益效果
[0025] 与现有技术比较， 本发明具有如下优点和技术效果：
[0026] 本发明通过脑电控制， 充分体现了患者的康复意图， 结合肌电信号反馈， 实时 获取患肢肌肉功能状态， 加上力反馈和姿态位置信息， 提供了综合的康复训练 评估， 同时通过虚拟现实模块进行显示互动， 构成一种多模态交互的上肢康复 机器人训练***， 实现了患肢康复训练过程的安全化、 科学化、 数据化和趣味 化。
对附图的简要说明
附图说明
[0027] 图 1为一种多模态交互的上肢康复机器人训练***的示意图。
[0028] 图中所示： 1-脑电信号采集与处理模块、 2 -机器人本体模块、 3 -肌电信号采集 与处理模块、 4 -康复训练评估模块、 5 -虚拟现实模块。
发明实施例
本发明的实施方式
[0029] 下面将结合具体实施例对本发明作进一步说明， 但不限于此。
[0030] 如图 1所示， 一种多模态交互的上肢康复机器人训练***， 所述上肢康复机器 人训练***包括脑电信号采集与处理模块 1、 机器人本体模块 2、 患肢肌肉综合 数据采集及处理模块， 患肢肌肉综合数据采集及处理模块、 康复训练评估模块 4 和虚拟现实模块 5，
[0031] 所述的脑电信号采集与处理模块 1用于提取患者脑电信号中的相关特征参量来 获得患者的动作意图， 进而触发所述机器人本体模块 2的康复训练动作；
[0032] 所述机器人本体模块 2在所述脑电信号采集与处理模块 1的触发及所述康复训练 评估模块 4反馈的量化评估参数下进行训练动作帮助患者的患肢进行空间运动完 成康复训练过程；
[0033] 所述患肢肌肉综合数据采集及处理模块用于采集并得到患肢的综合数据指标， 所述的肌肉综合数据指标包括肌肉功能状态指标、 肌力大小指标、 肌肉协同指 标， 所述的患肢肌肉综合数据采集及处理模块包括肌电信号采集与处理模块 3、 力反馈装置、 机器视觉装置，
[0034] 所述肌电信号采集与处理模块 3通过提取患者上肢的肌电信号特征， 获得患肢 的所述肌肉功能状态指标；
[0035] 所述的力反馈装置通过获取的力反馈数据得到患肢的所述肌力大小指标；
[0036] 所述的机器视觉装置捕获的患肢姿态位置数据， 同时与健康人的运动数据进行 对比， 通过分析计算患肢运动速度和轨迹的误差大小， 获得患肢的所述肌肉协 同指标。
[0037] 所述康复训练评估模块 4用于对患肢的肌肉功能状态指标、 肌力大小指标、 肌 肉协同指标进行处理分析， 对三项指标分别进行打分， 具体包括： 所述的肌肉 功能状态指标的分数通过对比患者与健康人的肌电信号的特征值获得， 两者差 值越大， 患者所得分数越低； 所述的肌力大小指标的分数通过力反馈数据获得 ， 力的数值越大， 说明患者主动训练程度越低， 患者所得分数越低； 所述的肌 肉协同指标的分数通过对比患者与健康人的运动数据， 计算患肢运动速度和轨 迹的误差大小， 误差越大， 患者所得分数越低； 通过计算三项指标的总分， 得 到患者手臂康复运动训练的量化评估结果， 分数的高低与患肢运动能力、 康复 训练的效果成正比， 即： 分数越高， 患肢运动能力、 康复训练的效果就越好。
[0038] 所述虚拟现实模块 5用于显示患肢康复训练的虚拟环境， 通过感官刺激患者训 练的主动性， 参数输入分别来自机器人本体模块 2和康复训练评估模块 4。
[0039] 所述的脑电信号采集与处理模块 1包括脑电信号采集部分、 多通道模拟前端放 大电路部分、 支持多通道采集的数字电路部分和信息处理部分， 所述脑电信号 采集部分采集患者的脑电信号输送至多通道模拟前端电路放大， 并且将模拟量 转换为数字量， 提高信号在传输中的抗干扰性； 所述支持多通道采集的数字电 路对数字信号进行缓存和转换， 然后传输至信息处理部分进行储存和还原； 最 后在信息处理部分中进行相应处理， 包括特征提取与模式识别， 输出信号触发 机器人本体模块 2的运行。
[0040] 所述的脑电信号采集与处理模块 1通过采集患者的脑电信号， 分析、 提取患者 脑电信号中的相关特征参量来获得患者的动作意图， 并且传输到机器人本体模 块 2, 触发康复训练的动作， 充分反映患者的主观意愿。
[0041] 所述的机器人本体模块 2患者手臂相连接， 由脑电信号采集与处理模块 1输出的 信号触发时， 辅助患者完成康复训练的动作， 并且可以为不同康复阶段的病人 提供有针对性的训练方式， 带动患者的患肢进行被动康复、 主动康复及主被动 康复训练， 并将训练的数据传输至虚拟现实模块 5作为输入参数。
[0042] 所述的肌电信号采集与处理模块 3包括前置放大电路部分、 陷波器、 滤波器部 分和信号分析部分， 所述前置放大电路部分用于对获取的肌电信号进行放大； 所述的陷波器主要用于降低 50Hz工频干扰； 所述的滤波器部分用于来滤除肌电 信号中的低频和高频噪声； 所述的信号分析部分通过对肌电信号进行时域分析 和频域分析， 提取包括振幅、 积分肌电值、 中值频率和平均频率的特征参数反 映患者上肢的肌肉功能状态， 并将分析结果传输至康复训练评估模块 4
[0043] 康复训练评估模块 4将肌力大小指标、 肌肉功能指标和肌肉协同指标进行分析 与处理， 其中肌力大小指标由力反馈数据反映， 肌肉功能指标由包括肌电信号 的振幅、 积分肌电值、 中值频率和平均频率的特征参数反映， 肌肉协同指标由 机器视觉捕获的患者姿态位置信息反映， 三项指标各自打分， 计算总分， 作为 对患肢康复训练情况的综合量化评估结果， 评估数据一方面作为康复训练的反 馈参数输入到机器人本体模块 2调节训练的动作， 实现康复训练的科学化与安全 化， 一方面作为训练情况的量化指标输入到虚拟现实模块 5中直观地显示给患者 ， 保证患者训练的主动性， 提高患者康复的参与度。
[0044] 所述的虚拟现实模块 5包括训练场景显示部分和对话交互部分， 其输入参数来 自机器人本体模块 2和康复训练评估模块 4, 通过视觉和语音刺激患者感官， 激 发康复训练的积极性与主动性。 所述虚拟现实模块 5用于显示患者手臂进行康复 训练的虚拟环境， 同时结合语音交互， 多层次刺激患者的感官， 实现***与患 者的互动， 增强康复训练的趣味性。 [0045] 本发明通过脑电信号采集与处理模块、 机器人本体模块、 肌电信号采集与处理 模块、 康复训练评估模块和虚拟现实模块构成一种多模态交互的上肢康复机器 人训练***， 实现了上肢康复训练过程的安全化、 科学化、 数据化和趣味化。
[0046] 本实施例的使用流程如下：
[0047] 在一个可行的实施例中， 脑电信号采集与处理模块 1开始运行后， 采集患者的 脑电信号， 然后通过多通道模拟前端放大电路进行放大， 并且将模拟量转换为 数字量， 支持多通道采集的数字电路对数字信号进行缓存和转换， 然后传输至 电脑进行信息处理， 包括特征提取与模式识别， 最后输出信号触发机器人本体 模块 2运行； 所述机器人本体模块 2末端与患者手臂相连， 带动患者的手臂进行 被动康复、 主动康复或主被动康复训练， 其训练的数据作为输入参数传输至虚 拟现实模块 5 ; 在机器人本体模块 2辅助患肢进行康复训练的过程中， 所述肌电 信号采集与处理模块 3实时的采集患肢的肌电信号， 分析得到肌肉的功能状态指 标并传输给康复训练评估模块 4, 同时力反馈装置采集患肢与机器人本体模块 2 之间的作用力数据， 分析得到肌力大小指标， 所述机器视觉装置捕获患肢的姿 态位置数据， 分析得到肌肉协同指标， 三个指标在康复训练评估模块 4进行处理 分析， 得到患者手臂康复运动训练的量化评估参数。 量化评估参数输出一方面 作为康复训练的反馈参数输入到机器人本体模块 2调节训练的动作， 实现康复训 练的科学化与安全化， 一方面作为训练情况的量化指标输入到虚拟现实模块 5， 直观地显示给患者。 虚拟现实模块 5显示患者手臂运动的虚拟场景， 提升训练的 趣味性， 同时与患者进行语音交互， 刺激患者训练的主动性。
[0048] 根据上述说明书的揭示和教导， 本发明所属领域的技术人员还可以对上述实施 方式进行变更和修改。 因此， 本发明并不局限于上面揭示和描述的具体实施方 式， 对本发明的一些修改和变更也应当落入本发明的权利要求的保护范围内。 根据上述说明书的揭示和教导， 本发明所属领域的技术人员还可以对上述实施 方式进行变更和修改。 因此， 本发明并不局限于上面揭示和描述的具体实施方 式， 对本发明的一些修改和变更也应当落入本发明的权利要求的保护范围内。

Claims

权利要求书
[权利要求 i] 一种多模态交互的上肢康复机器人训练***， 其特征在于， 所述上肢 康复机器人训练***包括脑电信号采集与处理模块 a） 、 机器人本 体模块 （2） 、 患肢肌肉综合数据采集及处理模块， 患肢肌肉综合数 据采集及处理模块、 康复训练评估模块 （4） 和虚拟现实模块 （5） ， 所述的脑电信号采集与处理模块 （1） 用于提取患者脑电信号中的相 关特征参量来获得患者的动作意图， 进而触发所述机器人本体模块 （ 2） 的康复训练动作；
所述机器人本体模块 （2） 在所述脑电信号采集与处理模块 （1） 的触 发及所述康复训练评估模块 （4） 反馈的量化评估参数下进行训练动 作帮助患者的患肢进行空间运动完成康复训练过程； 所述患肢肌肉综合数据采集及处理模块用于采集并得到患肢的综合数 据指标；
所述康复训练评估模块 （4） 用于对患肢的肌肉综合数据指标进行处 理分析， 所述的肌肉综合数据指标包括肌肉功能状态指标、 肌力大小 指标和肌肉协同指标， 对三项指标分别进行打分并计算三项指标的总 分， 得到患者手臂康复运动训练的量化评估结果； 所述虚拟现实模块 （5） 用于显示患肢康复训练的虚拟环境， 通过感 官刺激患者训练的主动性， 参数输入分别来自机器人本体模块 （2） 和康复训练评估模块 （4） 。
[权利要求 2] 根据权利要求 1所述的多模态交互的上肢康复机器人训练***， 其特 征在于， 所述肌力大小指标由力反馈数据反映； 所述肌肉功能指标由 包括肌电信号的振幅、 积分肌电值、 中值频率和平均频率的特征参数 反映； 肌肉协同指标由机器视觉捕获的患者姿态位置信息反映。
[权利要求 3] 根据权利要求 1所述的多模态交互的上肢康复机器人训练***， 其特 征在于， 所述对三项指标分别进行打分具体包括： 所述的肌肉功能状态指标的分数通过对比患者与健康人的肌电信号的 特征值获得， 两者差值越大， 患者所得分数越低； 所述的肌力大小指 标的分数通过力反馈数据获得， 力的数值越大， 说明患者主动训练程 度越低， 患者所得分数越低； 所述的肌肉协同指标的分数通过对比患 者与健康人的运动数据， 计算患肢运动速度和轨迹的误差大小， 误差 越大， 则患者所得分数越低； 通过计算三项指标的总分， 得到患者手 臂康复运动训练的量化评估结果， 分数的高低与患肢运动能力、 康复 训练的效果成正比。
[权利要求 4] 根据权利要求 1所述的多模态交互的上肢康复机器人训练***， 其特 征在于， 所述的患肢肌肉综合数据采集及处理模块包括肌电信号采集 与处理模块 （3） 、 力反馈装置、 机器视觉装置， 所述肌电信号采集与处理模块 （3） 通过提取患者上肢的肌电信号特 征， 获得患肢的所述肌肉功能状态指标；
所述的力反馈装置通过获取的力反馈数据得到患肢的所述肌力大小指 标；
所述的机器视觉装置通过捕获的患肢姿态位置数据得到患肢的所述肌 肉协同指标。
[权利要求 5] 根据权利要求 4所述的多模态交互的上肢康复机器人训练***， 其特 征在于， 所述的机器视觉装置具体通过捕获的患肢姿态位置数据， 同 时与健康人的运动数据进行对比， 通过分析计算患肢运动速度和轨迹 的误差大小， 获得患肢的所述肌肉协同指标。
[权利要求 6] 根据权利要求 1所述的多模态交互的上肢康复机器人训练***， 其特 征在于， 所述的脑电信号采集与处理模块 （1） 包括脑电信号采集部 分、 多通道模拟前端放大电路部分、 支持多通道采集的数字电路部分 和信息处理部分， 所述脑电信号采集部分采集患者的脑电信号输送至 多通道模拟前端电路放大， 并且将模拟量转换为数字量， 提高信号在 传输中的抗干扰性； 所述支持多通道采集的数字电路对数字信号进行 缓存和转换， 然后传输至信息处理部分进行储存和还原； 最后在信息 处理部分中进行相应处理， 包括特征提取与模式识别， 输出信号触发 机器人本体模块 （2） 的运行。
[权利要求 7] 根据权利要求 1所述的多模态交互的上肢康复机器人训练***， 其特 征在于， 所述的机器人本体模块 （2） 由脑电信号采集与处理模块 （1 ） 输出的信号触发时， 用于带动患者的患肢进行被动康复、 主动康复 及主被动康复训练， 并将训练的数据传输至虚拟现实模块 （5） 作为 输入参数。
[权利要求 8] 根据权利要求 1所述的多模态交互的上肢康复机器人训练***， 其特 征在于， 所述的肌电信号采集与处理模块 （3） 包括前置放大电路部 分、 陷波器、 滤波器部分和信号分析部分， 所述前置放大电路部分用 于对获取的肌电信号进行放大； 所述的陷波器用于降低 50Hz工频干 扰； 所述的滤波器部分用于来滤除肌电信号中的低频和高频噪声； 所 述的信号分析部分通过对肌电信号进行时域分析和频域分析， 提取包 括振幅、 积分肌电值、 中值频率和平均频率的特征参数反映患者上肢 的肌肉功能状态。
[权利要求 9] 根据权利要求 1所述的多模态交互的上肢康复机器人训练***， 其特 征在于， 所述的康复训练评估模块 （4） 的输出数据一方面作为康复 训练的反馈参数输入到机器人本体模块 （2） 调节训练的动作， 一方 面作为训练情况的量化指标输入到虚拟现实模块 （5） ， 直观地显示 给患者。
[权利要求 10] 根据权利要求 1所述的多模态交互的上肢康复机器人训练***， 其特 征在于， 所述的虚拟现实模块 （5） 包括训练场景显示部分和对话交 互部分， 其输入参数来自机器人本体模块 （2） 和康复训练评估模块 （4） ， 通过视觉和语音刺激患者感官， 激发康复训练的积极性与主 动性。
PCT/CN2019/114915 2018-12-15 2019-10-31 一种多模态交互的上肢康复机器人训练*** WO2020119319A1 (zh)

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