Adım Egzersizi Sırasında Kaydedilen Fotopletismografi Sinyallerinden Basıklık Reddine Dayalı Kişi Tanımlaması

Yıl 2024, Cilt: 14 Sayı: 4, 1825 - 1835, 15.12.2024

Tuğba Aydemir , Mehmet Şahin

https://doi.org/10.31466/kfbd.1492325

Öz

Bilgisayarlı görü tabanlı yöntemlerin yanı sıra, fizyolojik sinyal tabanlı kişi tanımlama (KT) uygulamaları da çeşitli avantajlarıyla son yıllarda büyük ilgi görmektedir. Fiziksel aktiviteler fizyolojik sinyalleri önemli ölçüde kirletebildiğinden, KT modellerinin çoğu, dikkate alınan sinyali dinlenme durumu sırasında elde etmek için önerilmiştir. Bu çalışmada, bir adım egzersizi sırasında kaydedilen fotopletismografi (PPG) sinyallerinden basıklık reddine dayalı bir KT önerilmiştir. Ön işleme aşamasında basıklık değeri beşten büyük olan ve PPG olmayan olarak etiketlenen PPG denemelerini sınıflandırma sürecinden çıkarılmıştır. Daha sonra sıralı ileri ana dalgacık seçim yöntemiyle öznitelikler çıkarılmış ve k-en yakın komşu algoritması kullanılarak sınıflandırılmıştır. %88.64 KT performansıyla en yüksek sınıflandırma doğruluğu oranına ulaşılmıştır. Elde edilen sonuçlar, adım egzersizi sırasında kaydedilen basıklık reddine dayalı PPG sinyallerinin KT için güvenilir bir şekilde kullanılabileceğini doğrulamıştır.

Anahtar Kelimeler

Kişi tanımlama, Basıklık, Fotopletismografi, Dalgacık seçimi, Sıralı ileri, Özellik çıkarımı, Sınıflandırma

Kurtosis Rejection-Based Person Identification from Photoplethysmography Signals Recorded During a Step Exercise

Öz

In addition to computer vision-based methods, physiological signal-based person identification (PI) applications have attracted great attention in recent years with various kinds of advantages. Because physical activities can significantly contaminate physiological signals, most of the PI models were proposed to acquire the considered signal during the resting state. In this study, we proposed a kurtosis rejection-based PI from photoplethysmography (PPG) signals recorded during a step exercise. In the preprocessing stage, we rejected the PPG trials, which have a kurtosis value greater than five and are labeled as non-PPG, from the classification process. Afterward, the features were extracted by the sequential forward mother wavelet selection method and classified using the k-nearest neighbor algorithm. We achieved the highest classification accuracy rate of 88.64% PI performance. The obtained results proved that the kurtosis rejection-based PPG signals recorded during the step exercise can be reliably used for PI.

Anahtar Kelimeler

Person identification, Kurtosis, Photoplethysmography, Wavelet selection, Sequential forward, Feature extraction, Classification

Kaynakça

• Alyasseri, Z. A. A., Khader, A. T., Al-Betar, M. A., & Alomari, O. A. (2020). Person identification using EEG channel selection with hybrid flower pollination algorithm. Pattern Recognition, 105, 107393.
• Aydemir, O. (2020). Odor and Subject Identification Using Electroencephalography Reaction to Olfactory. Traitement du Signal, 37(5), 799-805.
• Aydemir, O. (2017). Olfactory recognition based on EEG gamma-band activity. Neural computation, 29(6), 1667-1680.
• Aydemir, T., Şahin, M., & Aydemir, O. (2021). Sequential Forward Mother Wavelet Selection Method for Mental Workload Assessment on N-back Task Using Photoplethysmography Signals. Infrared Physics & Technology, 103966.
• Aydemir, T., Şahin, M., & Aydemir, O. (2020). A New Method for Activity Monitoring Using Photoplethysmography Signals Recorded by Wireless Sensor. Journal of Medical and Biological Engineering, 40(6), 934-942.
• Bedagkar-Gala, A., & Shah, S. K. (2014). A survey of approaches and trends in person re-identification. Image and vision computing, 32(4), 270-286.
• Biagetti, G., Crippa, P., Falaschetti, L., Saraceni, L., Tiranti, A., & Turchetti, C. (2020). Dataset from PPG wireless sensor for activity monitoring. Data in brief, 29, 105044.
• Chao, X. U., Xiang, S. U. N., Ziliang, C. H. E. N., & Shoubiao, T. A. N. (2020). Exhaustive hard triplet mining loss for Person Re-Identification. Turkish Journal of Electrical Engineering & Computer Sciences, 28(5).
• Choi, H. J., & Lee, J. Y. (2021). Comparative Study between Healthy Young and Elderly Subjects: Higher-Order Statistical Parameters as Indices of Vocal Aging and Sex. Applied Sciences, 11(15), 6966.
• He, J., & Jiang, N. (2020). Biometric from surface electromyogram (sEMG): Feasibility of user verification and identification based on gesture recognition. Frontiers in bioengineering and biotechnology, 8, 58.
• Jijomon, C. M., & Vinod, A. P. (2021). Person-identification using familiar-name auditory evoked potentials from frontal EEG electrodes. Biomedical Signal Processing and Control, 68, 102739.
• Kavsaoğlu, A. R., Polat, K., & Bozkurt, M. R. (2014). A novel feature ranking algorithm for biometric recognition with PPG signals. Computers in biology and medicine, 49, 1-14.
• Kim, G., Shu, D. W., & Kwon, J. (2021). Robust person re-identification via graph convolution networks. Multimedia Tools and Applications, 1-10.
• Krishnan, S., & Athavale, Y. (2018). Trends in biomedical signal feature extraction. Biomedical Signal Processing and Control, 43, 41-63.
• Lee, H. W., Lee, J. W., Jung, W. G., & Lee, G. K. (2007). The periodic moving average filter for removing motion artifacts from PPG signals. International Journal of Control, Automation, and Systems, 5(6), 701-706.
• Li, Q., Dong, P., & Zheng, J. (2020a). Enhancing the security of pattern unlock with surface EMG-based biometrics. Applied Sciences, 10(2), 541.
• Li, Y., Pang, Y., Wang, K., & Li, X. (2020b). Toward improving ECG biometric identification using cascaded convolutional neural networks. Neurocomputing, 391, 83-95.
• Lu, L., Mao, J., Wang, W., Ding, G., & Zhang, Z. (2020). A study of personal recognition method based on EMG signal. IEEE Transactions on Biomedical Circuits and Systems, 14(4), 681-691.
• Mazaira-Fernandez, L. M., Álvarez-Marquina, A., & Gómez-Vilda, P. (2015). Improving speaker recognition by biometric voice deconstruction. Frontiers in bioengineering and biotechnology, 3, 126.
• Pinto, J. R., Cardoso, J. S., Lourenço, A., & Carreiras, C. (2017). Towards a continuous biometric system based on ECG signals acquired on the steering wheel. Sensors, 17(10), 2228.
• Prasad, D. S., Chanamallu, S. R., & Prasad, K. S. (2021). Mitigation of ocular artifacts for EEG signal using improved earth worm optimization-based neural network and lifting wavelet transform. Computer Methods in Biomechanics and Biomedical Engineering, 24(5), 551-578.
• Rodrigues, D., Silva, G. F., Papa, J. P., Marana, A. N., & Yang, X. S. (2016). EEG-based person identification through binary flower pollination algorithm. Expert Systems with Applications, 62, 81-90.
• Siam, A. I., Abou Elazm, A., El-Bahnasawy, N. A., El Banby, G. M., & Abd El-Samie, F. E. (2021). PPG-based human identification using Mel-frequency cepstral coefficients and neural networks. Multimedia Tools and Applications, 1-19.
• Timus, O. H., & Bolat, E. D. (2017). k-NN-based classification of sleep apnea types using ECG. Turkish Journal of Electrical Engineering & Computer Sciences, 25(4), 3008-3023.
• Xiao, J., Hu, F., Shao, Q., & Li, S. (2019). A low-complexity compressed sensing reconstruction method for heart signal biometric recognition. Sensors, 19(23), 5330.
• Wilaiprasitporn, T., Ditthapron, A., Matchaparn, K., Tongbuasirilai, T., et.al., (2019). Affective EEG-based person identification using the deep learning approach. IEEE Transactions on Cognitive and Developmental Systems, 12(3), 486-496.
• Wu, S. C., Chen, P. T., Swindlehurst, A. L., & Hung, P. L. (2018). Cancelable biometric recognition with ECGs: subspace-based approaches. IEEE Transactions on Information Forensics and Security, 14(5), 1323-1336.
• Zhang, X., & Ding, Q. (2016). Respiratory rate monitoring from the photoplethysmogram via sparse signal reconstruction. Physiological measurement, 37(7), 1105.