An Automatic Cycling Performance Classifier System Based on the Crank Arm Force Measurement Data.

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.

Using Antonyan Vardan Transform and Extreme Learning Machines for Accurate sEMG Signal Classification.

In this paper, we present an evaluation of an adaptation of the Antonyan Vardan Transform (AVT) used in combination with an Extreme Learning Machines (ELM) classifier to process surface electromyography (sEMG) data used to classify six finger movements and a rest state. A total of 12 assays formed by three repetitions performed by four volunteers is analyzed. Additionally, a sample-by-sample output label comparison was performed to make a more comprehensive analysis of the system which was tested on a PC and embedded on a Rasp.berry Pi platform. Compared to literature papers, our system was capable to match or outperform similar solutions even using a simpler model, reaching mean accuracy rates above 94.