计算机应用 ›› 2014, Vol. 34 ›› Issue (3): 907-910.DOI: 10.11772/j.issn.1001-9081.2014.03.0907

• 行业与领域应用 • 上一篇    下一篇

基于遗传算法和极限学习机的Fugl-Meyer量表自动评估

王景丽1,2,3,李亮4,郁磊2,3,王计平2,方强5   

  1. 1. 中国科学院 长春光学精密机械与物理研究所,长春130000;2.中国科学院大学,北京100049;
    2. 中国科学院 苏州生物医学工程技术研究所,江苏 苏州215163
    3. 中国科学院大学,北京100049;
    4. 嘉兴市第二医院 康复医学中心,浙江 嘉兴314000
    5. 皇家墨尔本理工大学 电气与计算机工程学院,澳大利亚 墨尔本3001
  • 收稿日期:2013-08-13 修回日期:2013-10-16 出版日期:2014-03-01 发布日期:2014-04-01
  • 通讯作者: 王景丽
  • 作者简介:王景丽(1987-),女,山东曹县人,硕士研究生,主要研究方向:数据挖掘、智能康复评估;李亮(1981-),男,浙江海盐人,主治医师,硕士,主要研究方向:神经系统、骨关节系统疾病的康复诊断、评定及治疗;郁磊(1986-),男,江苏沭阳人,助理研究员,博士研究生,主要研究方向:人工智能与模式识别、穿戴式传感器网络;王计平(1986-),男,安徽天长人,研究实习员,硕士,主要研究方向:生物医学信号处理;方强(1968-),男,上海人,研究员,博士,主要研究方向:植入式医用电子、穿戴式传感器网络、现代康复适宜技术。

Automated Fugl-Meyer assessment based on genetic algorithm and extreme learning machine

WANGJingli1,2,3,LI Liang4,YU Lei2,3,WANG Jiping2,FANG Qiang5   

  1. 1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun Jilin 130000, China;
    2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou Jiangsu 215163, China;
    3. University of Chinese Academy of Sciences, Beijing 100049, China;
    4. Rehabilitation Medical Center, The Second Hospital of Jiaxing, Jiaxing Zhejiang 314000, China;
    5. School of Electrical and Computer Engineering, Royal Melbourne Institute of Technology University, Melbourne 3001, Australia
  • Received:2013-08-13 Revised:2013-10-16 Online:2014-03-01 Published:2014-04-01
  • Contact: WANGJingli

摘要:

为实现脑卒中上肢居家康复评定的自动化和定量化,针对临床上最常用的Fugl-Meyer运动功能评定(FMA)量表,利用极限学习机(ELM)建立了FMA量表得分自动预测模型。选取FMA肩肘部分中的4个动作,采用固定于偏瘫侧前臂和上臂的两个加速度传感器采集24名患者的运动数据,经预处理和特征提取,基于遗传算法(GA)和ELM进行特征选择,分别建立单个动作ELM预测模型和综合预测模型。结果显示,该模型可对FMA肩肘部分得分进行精确的自动预测,预测均方根误差为2.1849分。该方法突破了传统评定中主观性、耗时性的限制及对康复医师或治疗师的依赖性,可方便用于居家康复的评定。速度传感器采集24名患者的运动数据,经预处理和特征提取,基于遗传算法(Genetic Algorithm, GA)和ELM进行特征选择,分别建立单个动作ELM预测模型和综合预测模型。结果显示,该模型可对FMA肩肘部分得分进行精确的自动预测,预测均方根误差为2.1849分。该方法突破了传统评定中主观性、耗时性的限制及对康复医师或治疗师的依赖性,可方便用于居家康复的评定。

关键词: 脑卒中, 居家康复, Fugl-Meyer评定, 加速度传感器, 遗传算法, 极限学习机

Abstract:

To realize automatic and quantitative assessment in home-based upper extremity rehabilitation for stroke, an Extreme Learning Machine (ELM) based prediction model was proposed to automatically estimate the Fugl-Meyer Assessment (FMA) scale score for shoulder-elbow section. Two accelerometers were utilized for data recording during performance of 4 tasks selected from shoulder-elbow FMA and 24 patients were involved in the study. Accelerometer-based estimation was obtained by preprocessing raw sensor data, extracting data features, selecting features based on Genetic Algorithm and ELM. Then 4 single-task models and a comprehensive model were built individually using the selected features. Results show that it is possible to achieve accurate estimation of shoulder-elbow FMA score from the analysis of accelerometer sensor data with a root mean squared prediction error value of 2.1849 points. This approach breaks through the subjective and time-consuming property of traditional outcome measures which rely on clinicians at hand and can be easily utilized in the home settings.

Key words: stroke, home-based rehabilitation, Fugl-Meyer Assessment, accelerometer sensor, Genetic Algorithm (GA), Extreme Learning Machine (ELM)

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