Trumbower Randy D, Rajasekaran Sanguthevar, Faghri Pouran D
Sensory Motor Performance Program, Rehabilitation Institute of Chicago and Northwestern University, Storrs, CT, USA.
J Clin Monit Comput. 2006 Jun;20(3):209-20. doi: 10.1007/s10877-006-9023-2. Epub 2006 Jun 15.
Functional electrical stimulation-induced leg cycle ergometry (FES-LCE) provides therapeutic exercise for persons with spinal cord injury (SCI). However, there exists no systematic approach to predict whether an individual has sufficient thigh muscle strength necessary for FES-LCE exercise.
To develop and test a Probably Approximately Correct (PAC) learning model as a predictor of thigh muscle strengths sufficient for short-duration FES-LCE exercise and compare the model's performance with other well-known statistical methods.
Six healthy male individuals with SCI, having age (32.0 +/- 12.5 years), height (1.8 +/- 0.04 m), and weight (79.12 +/- 10.76 kg), participated in static and dynamic experiments. During static experiments, absolute crank torque measurements were used to estimate thigh muscle strengths in response to maximum FES intensities of 70 mA, 105 mA, and 140 mA at fixed crank positions on an FES-LCE. During dynamic experiments, changes in power output measurements were used to classify rider performance as 'Fatigue' or 'No Fatigue' during short-duration FES-LCE at maximum stimulation intensities of 70 mA, 105 mA, and 140 mA and flywheel resistance levels of 0/8th, 1/8th, and 2/8th kilopounds. A Probably Approximately Correct (PAC) learning model was developed to classify static offline muscle strength observations with online rider performances. PAC's discriminatory power was compared with logistic regression (LR), Fisher's linear discriminant analysis (LDA), and an artificial neural network (ANN) model.
PAC and ANN learning models correctly identified 100% of the training examples. PAC's average performance on the validation set was 93.1%. The ANN and LR performed comparable with 92.8% and 93.1% accuracy, respectively. The LDA method faired well on the validation set at 89.9%.
PAC performed well in identifying muscle strengths associated with the online performance criterion. Although PAC did not perform best during cross-validation, this model has many advantages over the other methods. PAC can adapt to changes in classification schemes and is more amenable to theoretical analyses than the other methods. PAC learning has an intuitive design and may be a practical choice for classifying muscle strength profiles with well-defined performance criteria.
功能性电刺激诱导腿部周期测力计训练（FES-LCE）为脊髓损伤（SCI）患者提供治疗性锻炼。然而，目前尚无系统方法来预测个体是否具备进行FES-LCE锻炼所需的足够大腿肌肉力量。
开发并测试一种可能近似正确（PAC）学习模型，作为短期FES-LCE锻炼足够大腿肌肉力量的预测指标，并将该模型的性能与其他知名统计方法进行比较。
六名年龄（32.0±12.5岁）、身高（1.8±0.04米）、体重（79.12±10.76千克）的健康男性SCI患者参与了静态和动态实验。在静态实验中，使用绝对曲柄扭矩测量来估计在FES-LCE上固定曲柄位置处，对应70毫安、105毫安和140毫安最大FES强度时的大腿肌肉力量。在动态实验中，在70毫安、105毫安和140毫安最大刺激强度以及0/8千磅、1/8千磅和2/8千磅飞轮阻力水平下，进行短期FES-LCE期间，使用功率输出测量的变化将骑行者表现分类为“疲劳”或“无疲劳”。开发了一种可能近似正确（PAC）学习模型，用于将静态离线肌肉力量观察结果与在线骑行者表现进行分类。将PAC的判别能力与逻辑回归（LR）、费舍尔线性判别分析（LDA）和人工神经网络（ANN）模型进行比较。
PAC和ANN学习模型正确识别了100%的训练示例。PAC在验证集上的平均性能为93.1%。ANN和LR的准确率分别为92.8%和93.1%，表现相当。LDA方法在验证集上的准确率为89.9%，表现良好。
PAC在识别与在线性能标准相关的肌肉力量方面表现良好。虽然PAC在交叉验证期间并非表现最佳，但该模型比其他方法具有许多优势。PAC可以适应分类方案的变化，并且比其他方法更适合进行理论分析。PAC学习具有直观的设计，对于根据明确的性能标准对肌肉力量分布进行分类可能是一个实际选择。
