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Journals / Bilgisayar Bilimleri / 2022 / Cilt: Vol:7 Sayı: Issue:2

Gait based human identification: a comparative analysis

Yürüyüşe dayalı insan tanımlaması: karşılaştırmalı bir analiz

Kubilay Muhammed SÜNNETCİ
Muhammed ORDU
Ahmet ALKAN
Pages
116–125
Publication Date
2021-10-20
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

From the results, it is seen that the Accuracy (%), Error (%), Sensitivity (%), Specificity (%), Precision (%), F1 Score (%), and Matthews Correlation Coefficient (MCC) of Optimizable Ensemble-2 that is the most successful classifier are equal to 97.92, 2.08, 97.92, 99.86, 98.44, 97.86, and 0.9790, respectively.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Thanks to gait analysis, many examinations such as person identification, disease detection, and evaluation of neuromusculoskeletal system functions can be performed.
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Key Findings & Data:In the study, the used dataset includes three different gait parameters obtained from 16 different individuals (7 females and 9 males) using wearable gait analysis sensors, and here there are 321 parameters for one gait of each person.
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Conclusion & Impact:From the results, it is seen that the Accuracy (%), Error (%), Sensitivity (%), Specificity (%), Precision (%), F1 Score (%), and Matthews Correlation Coefficient (MCC) of Optimizable Ensemble-2 that is the most successful classifier are equal to 97.92, 2.08, 97.92, 99.86, 98.44, 97.86, and 0.9790, respectively.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Sınıflandırma · lineer diskriminant · topluluk sınıflandırması

Abstract

Thanks to gait analysis, many examinations such as person identification, disease detection, and evaluation of neuromusculoskeletal system functions can be performed. In the study, the used dataset includes three different gait parameters obtained from 16 different individuals (7 females and 9 males) using wearable gait analysis sensors, and here there are 321 parameters for one gait of each person. In addition, we classify this data using Linear Discriminant, Ensemble Subspace Discriminant, Ensemble Bagged Trees, Optimizable Ensemble-1, and Optimizable Ensemble-2 classifiers. Two different optimization techniques were employed to increase the performance metrics of the classifiers. From the results, it is seen that the Accuracy (%), Error (%), Sensitivity (%), Specificity (%), Precision (%), F1 Score (%), and Matthews Correlation Coefficient (MCC) of Optimizable Ensemble-2 that is the most successful classifier are equal to 97.92, 2.08, 97.92, 99.86, 98.44, 97.86, and 0.9790, respectively.

Özet

Yürüyüş analizi sayesinde kişi tespiti, hastalık tespiti, sinir-kas-iskelet sistemi fonksiyonlarının değerlendirilmesi gibi birçok tetkik yapılabilmektedir. Çalışmada kullanılan veri seti, giyilebilir yürüyüş analiz sensörleri kullanılarak 16 farklı kişiden (7 kadın ve 9 erkek) elde edilen üç farklı yürüyüş parametresini içermektedir ve burada her bir kişinin bir yürüyüşü için 321 parametre bulunmaktadır. Ayrıca bu verileri Linear Discriminant, Ensemble Subspace Discriminant, Ensemble Baggged Trees, Optimizable Ensemble-1 ve Optimizable Ensemble-2 sınıflandırıcılarını kullanarak sınıflandırıyoruz. Sınıflandırıcıların performans ölçütlerini artırmak için iki farklı optimizasyon tekniği kullanılmıştır. Sonuçlardan, en başarılı sınıflandırıcının Doğruluk (%), Hata (%), Duyarlılık (%), Özgüllük (%), Kesinlik (%), F1 Puanı (%) ve Matthews Korelasyon Katsayısı (MCC) değerlerinin sırasıyla 97.92, 2.08, 97.92, 99.86, 98.44, 97.86 ve 0.9790'a eşit olduğu görülmektedir.

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Cite This Article

SÜNNETCİ, K. M., ORDU, M., ALKAN, A. (2021). Gait based human identification: a comparative analysis. Bilgisayar Bilimleri, Cilt: Vol:7 Sayı: Issue:2, 116–125. https://ajindex.com/articles/743157

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