Identifying key gait parameters in gender recognition and classification performance analysis using machine learning algorithms
by Neha Sathe; Anil Hiwale; Archana Ranade
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 16, No. 3, 2024

Abstract: Different gait parameters retrieved through pressure sensors, classification spatial, statistical, temporal and demographic (SSTD) model is suggested and tested for gender recognition and classification. Combination of spatial, temporal and demographic features along with performed descriptive statistics is used to train the model. Support vector machine, logistic regression and k-nearest neighbour classification results are tested and analysed for precision and recall. Step length and stride length with weight and height provides great performance in achieving accuracy. Classification results within range of 80% to 90% for selected dataset of 80 healthy subjects were achieved. Influence of stride length in female and step length in male recognition along with single support time is observed. Contribution of weight is also recognisable in classification accuracy. Behaviour of female recognition and classification provides clear results on selected features using SSTD model while precision and recall values whereas male recognition values are on lower end.

Online publication date: Wed, 01-May-2024

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