Khan S, Chang R. Anatomy of the vestibular system: a review. Neurorehabilitation. 2013;32:437–43. https://doi.org/10.3233/NRE-130866.
Abdulhay E, Arunkumar N, Narasimhan K, Vellaiappan E, Venkatraman V. Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease. Future Gener Comput Syst. 2018;83:366–73. https://doi.org/10.1016/j.future.2018.02.009.
Daliri MR. Automatic diagnosis of neuro-degenerative diseases using gait dynamics. Measurement. 2012;45(7):1729–34. https://doi.org/10.1016/j.measurement.2012.04.013.
Sarbaz Y, Banaie M, Pooyan M, Gharibzadeh S, Towhidkhah F, Jafari A. Modeling the gait of normal and Parkinsonian persons for improving the diagnosis. Neurosci Lett. 2012;509(2):72–5. https://doi.org/10.1016/j.neulet.2011.10.002.
Zeng W, Wang C. Classification of neurodegenerative diseases using gait dynamics via deterministic learning. Inf Sci. 2015;317:246–58. https://doi.org/10.1016/j.ins.2015.04.047.
Imai T, Takeda N, Uno A, Morita M, Koizuka I, Kubo T. Three-dimensional eye rotation axis analysis of benign paroxysmal positioning nystagmus. Orl. 2002;64(6):417–23. https://doi.org/10.1159/000067567.
Lang J, Ishikawa K, Hatakeyama K, Wong WH, Yin M, Saito T, Sibata Y. 3D body segment oscillation and gait analysis for vestibular disorders. Auris Nasus Larynx. 2013;40(1):18–24. https://doi.org/10.1016/j.anl.2011.11.007.
Bergeron M, Lortie CL, Guitton MJ. Use of virtual reality tools for vestibular disorders rehabilitation: a comprehensive analysis. Adv Med. 2015. https://doi.org/10.1155/2015/916735.
Sang-I L, Yi-Ju T, Pei-Yun L. Balance performance when responding to visual stimuli in patients with benign paroxysmal positional vertigo (BPPV). J Vestib Res Equilib Orientat. 2020. https://doi.org/10.3233/VES-200709.
Auvinet B, Touzard C, Montestruc F, Elafond A, Goeb V. Gait disorders in the elderly and dual task gait analysis: a new approach for identifying motor phenotypes. J Neuroeng Rehabil. 2017;14:14–7. https://doi.org/10.1186/s12984-017-0218-1.
Muro-De-La-Herran A, Garcia-Zapirain B, Mendez-Zorrilla A. Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications. Sensors. 2014;14(2):3362–94. https://doi.org/10.3390/s140203362.
Caldas R, Mundt M, Potthast W, Neto FB, Markert B. A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms. Gait Posture. 2017;57:204–10. https://doi.org/10.1016/j.gaitpost.2017.06.019.
Qiu S, Wang H, Li J, Zhao H, Wang Z, Wang J, Ru B. Towards wearable-inertial-sensor-based gait posture evaluation for subjects with unbalanced gaits. Sensors. 2020;20(4):1193. https://doi.org/10.3390/s20041193.
Ikizoğlu S, Heydarov S. Accuracy comparison of dimensionality reduction techniques to determine significant features from IMU sensor-based data to diagnose vestibular system disorders. Biomed Signal Process Control. 2020. https://doi.org/10.1016/j.bspc.2020.101963.
Jarchi D, Pope J, Lee TK, Tamjidi L, Mirzaei A, Sanei S. A review on accelerometry-based gait analysis and emerging clinical applications. IEEE Rev Biomed Eng. 2018;11:177–94. https://doi.org/10.1109/rbme.2018.2807182.
Ricciardi C, Amboni M, Santis CD, Improta G, Volpe G, Iuppariello L, Cesarelli M. Using gait analysis’ parameters to classify Parkinsonism: a data mining approach. Comput Methods Programs in Biomed. 2019;180:4561. https://doi.org/10.1016/j.cmpb.2019.105033.
Sama A, Pardo-Ayala DE, Cabestany J, Rodriguez-Molinero A. Time series analysis of inertial-body signals for the extraction of dynamic properties from human gait. In: The 2010 international joint conference on neural networks (IJCNN), 2010, p. 1–5. https://doi.org/10.1109/ijcnn.2010.5596663
Zhao A, Qi L, Dong J, Yu H. Dual channel LSTM based multi-feature extraction in gait for diagnosis of neurodegenerative diseases. Knowl Based Syst. 2018;145:91–7. https://doi.org/10.1016/j.knosys.2018.01.004.
Tao W, Liu T, Zheng R, Feng H. Gait analysis using wearable sensors. Sensors. 2012;12(2):2255–83. https://doi.org/10.3390/s120202255.
Dutta S, Ghosh D, Chatterjee S. Multifractal detrended fluctuation analysis of human gait diseases. Front Physiol. 2013. https://doi.org/10.3389/fphys.2013.00274.
Easwaramoorthy D, Uthayakumar R. Estimating the complexity of biomedical signals by multifractal analysis. In: 2010 IEEE students technology symposium (TechSym) 2010. https://doi.org/10.1109/techsym.2010.5469188
Han C, Wang Y, Xu Y. Efficiency and multifractality analysis of the Chinese stock market: evidence from stock indices before and after the 2015 stock market crash. Sustainability. 2019;11(6):1699. https://doi.org/10.3390/su11061699.
Laib M, Golay J, Telesca L, Kanevski M. Multifractal analysis of the time series of daily means of wind speed in complex regions. Chaos Solitons Fractals. 2018;109:118–27. https://doi.org/10.1016/j.chaos.2018.02.024.
Lopes R, Betrouni N. Fractal and multifractal analysis: a review. Med Image Anal. 2009;13(4):634–49. https://doi.org/10.1016/j.media.2009.05.003.
Peng C, Havlin S, Hausdorff J, Mietus J, Stanley H, Goldberger A. Fractal mechanisms and heart rate dynamics. J Electrocardiol. 1995;28:59–65. https://doi.org/10.1016/s0022-0736(95)80017-4.
Phinyomark A, Larracy R, Scheme E. Fractal analysis of human gait variability via stride ınterval time series. Front Physiol. 2020;11:333.
Shang P, Lu Y, Kamae S. Detecting long-range correlations of traffic time series with multifractal detrended fluctuation analysis. Chaos Solitons Fractals. 2008;36(1):82–90. https://doi.org/10.1016/j.chaos.2006.06.019.
Zhang X, Zhang G, Qiu L, Zhang B, Sun Y, Gui Z, Zhang Q. A modified multifractal detrended fluctuation analysis (MFDFA) approach for multifractal analysis of precipitation in Dongting Lake Basin China. Water. 2019;11(5):891. https://doi.org/10.3390/w11050891.
Hausdorff JM, Ashkenazy Y, Peng C, Ivanov PC, Stanley H, Goldberger AL. When human walking becomes random walking: fractal analysis and modeling of gait rhythm fluctuations. Phys A Stat Mech Appl. 2001;302(1–4):138–47. https://doi.org/10.1016/s0378-4371(01)00460-5.
Muñoz-Diosdado A. Fractal and multifractal analysis of human gait. AIP Conf Proc. 2003. https://doi.org/10.1063/1.1615130.
Heydarov S, İkizoğlu S, Şahin K, Kara E, Çakar T, Ataş A. Performance comparison of ML methods applied to motion sensory information for identification of vestibular system disorders. In: ELECO 2017, Bursa, Turkey, 2017
Ikizoğlu S, Atasoy B. Chaotic approach based feature extraction to implement in gait analysis. In: Chaos and complex systems springer proceedings in complexity, 2020, p. 67–72. https://doi.org/10.1007/978-3-030-35441-1_7
Ikizoğlu S, Şahin K, Atas A, Kara E, Çakar T. IMU acceleration drift compensation for position tracking in ambulatory gait analysis. In: Proceedings of the 14th international conference on informatics in control, automation and robotics (ICINCO 2017), p. 582–589. ISBN: 978–989–758–263–9
Kantelhardt JW, Zschiegner SA, Koscielny-Bunde E, Havlin S, Bunde A, Stanley H. Multifractal detrended fluctuation analysis of nonstationary time series. Physica A Stat Mech Appl. 2002;316(1–4):87–114. https://doi.org/10.1016/s0378-4371(02)01383-3.
Ihlen EA. Introduction to multifractal detrended fluctuation analysis in Matlab. Front Physiol. 2012;3:4561. https://doi.org/10.3389/fphys.2012.00141.
Vieten MM, Sehle A, Jensen RL. A novel approach to quantify time series differences of gait data using attractor attributes. PLoS ONE. 2013. https://doi.org/10.1371/journal.pone.0071824.
Healy A, Burgess-Walker P, Naemi R, Chockalingam N. Repeatability of WalkinSense® in shoe pressure measurement system: a preliminary study. Foot. 2012;22(1):35–9. https://doi.org/10.1016/j.foot.2011.11.001.
Holleczek T, Ruegg A, Harms H, Tro G. Textile pressure sensors for sports applications. IEEE Sens. 2010;2010:732–7. https://doi.org/10.1109/icsens.2010.5690041.
Saito M, Nakajima K, Takano C, Ohta Y, Sugimoto C, Ezoe R, Yamashita K. An in-shoe device to measure plantar pressure during daily human activity. Med Eng Phys. 2011;33(5):638–45. https://doi.org/10.1016/j.medengphy.2011.01.001.
Salpavaara T, Verho J, Lekkala J, Halttunen J. Wireless insole sensor system for plantar force measurements during sport events. In: Proceedings of IMEKO XIX world congress on fundamental and applied metrology, Lisbon, Portugal 2009, p. 2118–2123
Shu L, Hua T, Wang Y, Li Q, Feng DD, Tao X. In-shoe plantar pressure measurement and analysis system based on fabric pressure sensing array. IEEE Trans Inf Technol Biomed. 2010;14(3):767–75. https://doi.org/10.1109/titb.2009.2038904.
Tahir AM, Chowdhury ME, Khandakar A, Al-Hamouz S, Abdalla M, Awadallah S, Al-Emadi N. A systematic approach to the design and characterization of a smart insole for detecting vertical ground reaction force (vGRF) in gait analysis. Sensors. 2020;20(4):957. https://doi.org/10.3390/s20040957.
https://cdn2.hubspot.net/hubfs/3899023/Interlinkelectronics%20November2017/Docs/Datasheet_FSR.pdf
Peterson L. K-nearest neighbor. Scholarpedia. 2009;4(2):1883. https://doi.org/10.4249/scholarpedia.1883.
James G, Witten D, Hastie T, Tibshirani R. Chapter 8: tree-based methods. In: An introduction to statistical learning with applications in R. New York: Springer; 2017.
Geron A. Chapter 5: support vector machines. In: Hands-on machine learning with Scikit-Learn, Keras, and tensorflow: concepts, tools, and techniques to build intelligent systems. Sebastopol: O’Reilly Media Incorporated; 2019.
Bastos ND, Adamatti DF, Billa CZ. Decision tree to analyses EEG signal: a case study using spatial activities. Commun Comput Inf Sci Comput Neurosci. 2017;45:159–69. https://doi.org/10.1007/978-3-319-71011-2_13.
Lin Y, Wang C, Wu T, Jeng S, Chen J. Support vector machine for EEG signal classification during listening to emotional music. In: 2008 IEEE 10th workshop on multimedia signal processing, 2008. p. 127–130. https://doi.org/10.1109/mmsp.2008.4665061
Saccà V, Campolo M, Mirarchi D, Gambardella A, Veltri P, Morabito F. On the classification of EEG signal by using an SVM based algorithm. Multidiscip Approaches Neural Comput. 2018. https://doi.org/10.1007/978-3-319-56904-8_26.
Saini I, Singh D, Khosla A. QRS detection using K-nearest neighbor algorithm (KNN) and evaluation on standard ECG databases. J Adv Res. 2013;4(4):331–44. https://doi.org/10.1016/j.jare.2012.05.007.
Shao M, Bin G, Wu S, Bin G, Huang J, Zhou Z. Detection of atrial fibrillation from ECG recordings using decision tree ensemble with multi-level features. Physiol Meas. 2018. https://doi.org/10.1088/1361-6579/aadf48.
Yean CW, Khairunizam W, Omar MI, Murugappan M, Zheng BS, Bakar SA, Ibrahim Z. Analysis of the distance metrics of KNN classifier for EEG signal in stroke patients. In: 2018 International conference on computational approach in smart systems design and applications (ICASSDA) 2018. https://doi.org/10.1109/icassda.2018.8477601
Zhao A, Li J, Dong J, Qi L, Zhang Q, Li N, Wang X, Zhou H. Multimodal gait recognition for neurodegenerative diseases. Comput Sci J Med IEEE Trans Cybern. 2021;52(9):9439–53.
Slama AB, Mouelhi A, Sahli H, Zeraii A, Marrakchi J, Trabelsi H. A deep convolutional neural network for automated vestibular disorder classification using VNG analysis. Comput Methods Biomech Biomed Eng Imaging Vis. 2020;8(3):334–42.
留言 (0)