On Exploring the Role of Feature Processing in Gait-based Gender Identification

Objective A small electrocardiograph and Holter electrocardiograph can record an electrocardiogram for 24 h or more. We examined whether gender could be verified from such an electrocardiogram and, if possible, how accurate it would be. Results Ten dimensional statistics were extracted from the heart rate data of more than 420,000 people, and gender identification was performed by various major identification methods. Lasso, linear regression, SVM, random forest, logistic regression, k-means, Elastic Net were compared, for Age < 50 and Age ≥ 50. The best Accuracy was 0.681927 for Random Forest for Age < 50. There are no consistent difference between Age < 50 and Age ≥ 50. Although the discrimination results based on these statistics are statistically significant, it was confirmed that they are not accurate enough to determine the gender of an individual.

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How can gender be identified from heart rate data? Evaluation using ALLSTAR heart rate variability big data analysis

Kaneko

Hayano

Yuda

2023

BMC Res Notes

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On Exploring the Role of Feature Processing in Gait-based Gender Identification

Cited by 1 publication

References 12 publications

How can gender be identified from heart rate data? Evaluation using ALLSTAR heart rate variability big data analysis

How can gender be identified from heart rate data? Evaluation using ALLSTAR heart rate variability big data analysis

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