基于遗传算法和极限学习机的Fugl-Meyer量表自动评估

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。

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

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.