Kurtosis Rejection-Based Person Identification from Photoplethysmography Signals Recorded During a Step Exercise
Abstract
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.
Keywords
Person identification, Kurtosis, Photoplethysmography, Wavelet selection, Sequential forward, Feature extraction, Classification
References
- 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.
