Pigatto Andre Vieira, Santos Raphael Ruschel Dos, Balbinot Alexandre
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:4237-4240. doi: 10.1109/EMBC.2018.8513403.
This paper describes the development of an automatic cycling performance measurement system with a Fuzzy Logic Controller (FLC), using Mamdani Inference method, to classify the performance of the cyclist. From data of the average power, its standard deviation and the effective force bilateral asymmetry index, a score that represents the cyclist performance is determined. Data are acquired using an experimental crank arm load cell force platform developed with built-in strain gages and conditioning circuit that measure the force that is applied to the bicycle pedal during cycling with a linearity error under 0.6%. A randomized block experiment design was performed with 15 cyclists of 29±5 years with a body mass of 73±9kg and a height of 1.78±0.07m. The average power reached by the subjects was 137.63±59.6W; the mean bilateral asymmetry index, considering all trials, was 67.01±6.23%. The volunteers cycling performance scores were then determined using the developed FLC; the mean score was 25.4% ± 16.9%. ANOVA showed that the subject causes significant variation on the performance score.
本文描述了一种带有模糊逻辑控制器（FLC）的自动骑行性能测量系统的开发，该系统采用Mamdani推理方法来对骑行者的性能进行分类。根据平均功率、其标准偏差以及有效力双侧不对称指数的数据，确定一个代表骑行者性能的分数。数据通过一个实验性的曲柄臂测力传感器力平台采集，该平台配备内置应变片和调节电路，用于测量骑行过程中施加在自行车踏板上的力，线性误差在0.6%以内。对15名年龄在29±5岁、体重73±9kg、身高1.78±0.07m的骑行者进行了随机区组实验设计。受试者达到的平均功率为137.63±59.6W；考虑所有试验的平均双侧不对称指数为67.01±6.23%。然后使用开发的FLC确定志愿者的骑行性能分数；平均分数为25.4%±16.9%。方差分析表明，受试者对性能分数有显著影响。
