Fuzzy Inference System-based Recognition of Slow, Medium and Fast Running Conditions using a Triaxial Accelerometer

摘要

This paper introduces a fuzzy inference system (FIS)-based model for recognizing running conditions using data collected with a triaxial accelerometer. Specifically, data from three axes of a triaxial accelerometer were used as the input, and various running conditions (slow, medium and fast) were considered the output of the FIS. The MATLAB ? fuzzy toolbox, which includes processes such as fuzzification, sets of fuzzy rules, fuzzy inference engine and defuzzification, was used to model the system. Mamdani-type fuzzy modelling was selected for developing the FIS. The structure of the generated fuzzy inference system includes three fuzzy rules (using if-then) and an initial set of membership functions. The performance of the proposed FIS model was assessed using the root mean square error (RMSE), mean absolute error (MAE) and non-dimensional error index (NDEI), which were found to equal 0.059, 0.213 and 0.147, respectively, for the test data. Additionally, the correlation coefficients ( r ) and coefficient of determination (R 2 ) between the FIS-predicted and the actual values were 0.89 and 0.81, respectively. Finally, the model performance accuracy was measured using Variance-Accounted-For (%VAF), which equaled 96.54%. Thus, the assessment of the overall performance suggests that the proposed FIS model has potential to detect slow, medium and fast running conditions.