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Gait based human identification: a comparative analysis

Year 2021, Volume: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Issue: Special, 116 - 125, 20.10.2021
https://doi.org/10.53070/bbd.989226

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.

References

  • Açıcı K., Erdaş Ç.B., Aşuroğlu T., Toprak M.K., Erdem H., Oğul H. (2017) A Random Forest Method to Detect Parkinson’s Disease via Gait Analysis. In: Boracchi G., Iliadis L., Jayne C., Likas A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_51
  • Ahad, M. A. R., Ngo, T. T., Antar, A. D., Ahmed, M., Hossain, T., Muramatsu, D., ... & Yagi, Y. (2020). Wearable sensor-based gait analysis for age and gender estimation. Sensors, 20(8), 2424. https://doi.org/10.3390/s20082424
  • Alaskar H., Jaafar Hussain A. (2018) Data Mining to Support the Discrimination of Amyotrophic Lateral Sclerosis Diseases Based on Gait Analysis. In: Huang DS., Gromiha M., Han K., Hussain A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science, vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_80
  • Alkan, A., & Günay, M. (2012). Identification of EMG signals using discriminant analysis and SVM classifier, Expert Systems with Applications, 39(1), 44–47. https://doi.org/10.1016/j.eswa.2011.06.043
  • Arivazhagan S, Induja P. Gait Recognition-Based Human Identification and Gender Classification. Proceedings of International Conference on Computer Vision and Image Processing; 2017; pp. 533-544.
  • Caldas, R., Mundt, M., Potthast, W., Lima Neto, F. B., Markert, B. (2017). A systematic review of gait analysis methods based on inertial sensors andadaptive algorithms. Gait & Posture, 57, 204-210. https://doi.org/10.1016/j.gaitpost.2017.06.019
  • Dataset, https://archive.ics.uci.edu/ml/datasets/Gait+Classification, Gait Classification Data Set, Dr. Abdulkadir Gumuscu,
  • Del Din, S., Elshehabi, M., Galna, B., Hobert, M. A., Warmerdam, E., Suenkel, U., Brockmann, K., Metzger, F., Hansen, C., Berg, D., Rochester, L., Maetzler, W. (2019). Gait Analysis with Wearables Predicts Conversion to Parkinson Disease. Annals of Neurology, 86(3), 357-367. https://doi.org/10.1002/ana.25548
  • Gümüşçü, A. (2019). Giyilebilir Yürüyüş Analiz Sensörü ile Kişi Sınıflandırmasının Öznitelik Seçme Algoritmaları ile İyileştirilmesi. Fırat Üniversitesi Müh. Bil. Dergisi, 31(2), 463-471.
  • Lu JW, Tan YP. Gait-Based Human Age Estimation. Ieee Transactions on Information Forensics and Security 2010; 5(4): 761-770.
  • Pathan R.K., Uddin M.A., Nahar N., Ara F., Hossain M.S., Andersson K. (2021) Human Age Estimation Using Deep Learning from Gait Data. In: Mahmud M., Kaiser M.S., Kasabov N., Iftekharuddin K., Zhong N. (eds) Applied Intelligence and Informatics. AII 2021. Communications in Computer and Information Science, vol 1435. Springer, Cham. https://doi.org/10.1007/978-3-030-82269-9_22
  • Recenti, M., Ricciardi, C., Aubonnet, R., Esposito, L., Jónsson, H., & Gargiulo, P. (2020, June). A regression approach to assess bone mineral density of patients undergoing total hip arthroplasty through gait analysis. In 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (pp. 1-6). IEEE.
  • Ricciardi, C., Amboni, M., De Santis, C., Improta, G., Volpe, G., Iuppariello, L., ... & Unit, T. B. E. (2019). Using gait analysis’ parameters to classify Parkinsonism: A data mining approach. Computer methods and programs in biomedicine, 180, 105033. https://doi.org/10.1016/j.cmpb.2019.105033
  • Solmaz, R., Günay, M., Alkan (2013). Uzman Sistemlerin Tiroit Teşhisinde Kullanılması, Akademik Bilişim, 919–922. http://ab.org.tr/ab13/kitap/olmaz_gunay_AB13.pdf
  • Zhou, Z.-H. (2009). Ensemble Learning, Encyclopedia of Biometrics, 270–273. https://doi.org/10.1007/978-0-387-73003-5_293

Yürüyüşe dayalı insan tanımlaması: karşılaştırmalı bir analiz

Year 2021, Volume: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Issue: Special, 116 - 125, 20.10.2021
https://doi.org/10.53070/bbd.989226

Abstract

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.

References

  • Açıcı K., Erdaş Ç.B., Aşuroğlu T., Toprak M.K., Erdem H., Oğul H. (2017) A Random Forest Method to Detect Parkinson’s Disease via Gait Analysis. In: Boracchi G., Iliadis L., Jayne C., Likas A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_51
  • Ahad, M. A. R., Ngo, T. T., Antar, A. D., Ahmed, M., Hossain, T., Muramatsu, D., ... & Yagi, Y. (2020). Wearable sensor-based gait analysis for age and gender estimation. Sensors, 20(8), 2424. https://doi.org/10.3390/s20082424
  • Alaskar H., Jaafar Hussain A. (2018) Data Mining to Support the Discrimination of Amyotrophic Lateral Sclerosis Diseases Based on Gait Analysis. In: Huang DS., Gromiha M., Han K., Hussain A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science, vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_80
  • Alkan, A., & Günay, M. (2012). Identification of EMG signals using discriminant analysis and SVM classifier, Expert Systems with Applications, 39(1), 44–47. https://doi.org/10.1016/j.eswa.2011.06.043
  • Arivazhagan S, Induja P. Gait Recognition-Based Human Identification and Gender Classification. Proceedings of International Conference on Computer Vision and Image Processing; 2017; pp. 533-544.
  • Caldas, R., Mundt, M., Potthast, W., Lima Neto, F. B., Markert, B. (2017). A systematic review of gait analysis methods based on inertial sensors andadaptive algorithms. Gait & Posture, 57, 204-210. https://doi.org/10.1016/j.gaitpost.2017.06.019
  • Dataset, https://archive.ics.uci.edu/ml/datasets/Gait+Classification, Gait Classification Data Set, Dr. Abdulkadir Gumuscu,
  • Del Din, S., Elshehabi, M., Galna, B., Hobert, M. A., Warmerdam, E., Suenkel, U., Brockmann, K., Metzger, F., Hansen, C., Berg, D., Rochester, L., Maetzler, W. (2019). Gait Analysis with Wearables Predicts Conversion to Parkinson Disease. Annals of Neurology, 86(3), 357-367. https://doi.org/10.1002/ana.25548
  • Gümüşçü, A. (2019). Giyilebilir Yürüyüş Analiz Sensörü ile Kişi Sınıflandırmasının Öznitelik Seçme Algoritmaları ile İyileştirilmesi. Fırat Üniversitesi Müh. Bil. Dergisi, 31(2), 463-471.
  • Lu JW, Tan YP. Gait-Based Human Age Estimation. Ieee Transactions on Information Forensics and Security 2010; 5(4): 761-770.
  • Pathan R.K., Uddin M.A., Nahar N., Ara F., Hossain M.S., Andersson K. (2021) Human Age Estimation Using Deep Learning from Gait Data. In: Mahmud M., Kaiser M.S., Kasabov N., Iftekharuddin K., Zhong N. (eds) Applied Intelligence and Informatics. AII 2021. Communications in Computer and Information Science, vol 1435. Springer, Cham. https://doi.org/10.1007/978-3-030-82269-9_22
  • Recenti, M., Ricciardi, C., Aubonnet, R., Esposito, L., Jónsson, H., & Gargiulo, P. (2020, June). A regression approach to assess bone mineral density of patients undergoing total hip arthroplasty through gait analysis. In 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (pp. 1-6). IEEE.
  • Ricciardi, C., Amboni, M., De Santis, C., Improta, G., Volpe, G., Iuppariello, L., ... & Unit, T. B. E. (2019). Using gait analysis’ parameters to classify Parkinsonism: A data mining approach. Computer methods and programs in biomedicine, 180, 105033. https://doi.org/10.1016/j.cmpb.2019.105033
  • Solmaz, R., Günay, M., Alkan (2013). Uzman Sistemlerin Tiroit Teşhisinde Kullanılması, Akademik Bilişim, 919–922. http://ab.org.tr/ab13/kitap/olmaz_gunay_AB13.pdf
  • Zhou, Z.-H. (2009). Ensemble Learning, Encyclopedia of Biometrics, 270–273. https://doi.org/10.1007/978-0-387-73003-5_293
There are 15 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section PAPERS
Authors

Kubilay Muhammed Sünnetci 0000-0002-3500-5640

Muhammed Ordu 0000-0003-4764-9379

Ahmet Alkan 0000-0003-0857-0764

Publication Date October 20, 2021
Submission Date August 31, 2021
Acceptance Date September 16, 2021
Published in Issue Year 2021 Volume: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Issue: Special

Cite

APA Sünnetci, K. M., Ordu, M., & Alkan, A. (2021). Gait based human identification: a comparative analysis. Computer Science, IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium(Special), 116-125. https://doi.org/10.53070/bbd.989226

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