Classification of Spatiotemporal Features of Time Series in Post-Fatigue Gait

EasyChair Preprint 11329

Abstract

Muscle fatigue affects gait to the point of causing changes in your patterns. People with muscle training have more endurance and better recovery time from muscle fatigue than people without training. The study compared three classi- fication algorithms in the analysis of gait data under normal conditions and different levels of muscle fatigue. Spatio-tem- poral data from a group of people who do and do not do weight training were analyzed. The result showed that the classification accuracy of the k-nearest neighbor algorithm had the best result with 86.78% accuracy. The results indicated by the classifica- tion algorithms show a difference in the muscle fatigue recovery process between the groups, similar to the clinical results dis- cussed in the literature.

Keyphrases: Classification, KNN, Random Forest, SVM, muscle fatigue

@booklet{EasyChair:11329,
  author    = {Rodrigo Gomide and Eduardo Mesquita and Guilherme Villa and Marcus Vieira},
  title     = {Classification of Spatiotemporal Features of Time Series in Post-Fatigue Gait},
  howpublished = {EasyChair Preprint 11329},
  year      = {EasyChair, 2023}}