Giyilebilir Yürüyüş Analiz Sensörü ile Kişi Sınıflandırmasının Öznitelik Seçme Algoritmaları ile İyileştirilmesi

Yıl 2019, , 463 - 471, 27.09.2019

Abdülkadir Gümüşçü

https://doi.org/10.35234/fumbd.554789

Cited By: 5

Öz

Yürüme, canlıların bilinen en eski aktivitelerinden biridir. Konum değiştirmek amacı ile insanların kas ve kemik sistemlerinin koordineli bir şekilde hareket etmesine yürüme denir. Yürüme biyometrik bir ölçüt olarak kabul edilmektedir. Bu yüzden yürüyüş analizi ile kişi tanıma, yaş belirleme, cinsiyet belirleme, nörolojik ve ortopedik hastalık tespiti gibi çalışmalar yapılabilmektedir. Bu çalışmada da giyilebilir yürüyüş analizi sensörü ile kişilerin sınıflandırılmasında sarmal modelli öznitelik seçme yöntemleri kullanılarak daha başarılı sınıflandırma başarı parametrelerinin elde edilmesi amaçlanmıştır. 7’si bayan 9’u bay olmak üzere toplam 16 farklı gönüllü kişinin yürüyüş parametreleri hesaplanarak yürüyüş veri seti oluşturulmuştur. Her gönüllüden 3 kez yürümeleri istenmiş olup toplam 48 yürüyüş ele alınmıştır. Kişi sınıflandırma başarı parametreleri k-en yakın komşuluk yöntemi kullanılarak hesaplanmış olup birini dışarıda bırak çapraz doğrulama yöntemi ile doğrulanmıştır. Sınıflandırma sonuçları ele alındığında 0,979 doğruluk oranı elde edilmiştir. Sonuçlar sınıflandırma başarı parametreleri ve sınıflandırma işlem süresi bakımından incelenmiş olup elde edilen sonuçlar çalışmanın sınıflandırma başarı parametreleri ve sınıflandırma işlem süresi bakımından ciddi oranda iyileştirmeler sağladığı gözlemlenmiştir.

Anahtar Kelimeler

Yürüyüş analizi, sarmal modelli öznitelik seçme, sınıflandırma, kişi tanıma

Improvement of Wearable Gait Analysis Sensor based Human Classification using Feature Selection Algorithms

Öz

Gait is one of the oldest known activity. It is called gait in the coordinated manner of muscle and bone systems of people with the purpose of changing position. Gait is considered a biometric criterion. Therefore, studies such as human identification, age determination, gender identification, detection of neurological and orthopedic diseases can be done by gait analysis. In this study, it is aimed to obtain more successful classification success parameters by using wrapper model feature selection methods in the human classification with wearable gait analysis sensor. Gait data set was created by calculating the gait parameters of a total of 16 different volunteers including 7 female and 9 male. For each volunteer, 3 different gait parameters were calculated and a total of 48 gait were discussed. Human classification success parameters were calculated by using k-nearest neighborhood method and the results was verified by leave one out cross-validation method. The results were examined in terms of the classification success parameters and the processing time, the results obtained showed significant improvements in the classification success parameters and the classification process time.

Anahtar Kelimeler

Gait analysis, wrapper feature selection methods, classification, human recognition

Kaynakça

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