Abstract
Classification based on gait biomarkers is an area of study that includes approaches aimed at monitoring (vigilance), education and health. A correct classification is achieved depending on algorithms that serve that purpose, however, an accuracy must be available during the data acquisition of gait. In this study, a sensor network is proposed that allows to capture, in children, data of knee and right ankle. Results shows acceptable percentages of correct classification when implementing various machine learning algorithms, especially, combining the LogitBoost+Random Forest algorithms.
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Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Calhoun, M., Longworth, M., Chester, V.L.: Gait patterns in children with autism. Clin. Biomech. 26(2), 200–206 (2011)
Clark, C.C., Barnes, C.M., Holton, M., Summers, H.D., Stratton, G.: Profiling movement quality and gait characteristics according to body mass index in children 9–11 years. Hum. Mov. Sci. 49, 291–300 (2016)
Fergus, P., Hussain, A.J., Hearty, J., Fairclough, S., Boddy, L., Mackintosh, K., Stratton, G., Ridgers, N., Al-Jumeily, D., Aljaaf, A.J., Lunn, J.: A machine learning approach to measure and monitor physical activity in children. Neurocomputing 228, 220–230 (2017). Advanced Intelligent Computing, Theory and Applications
Jamil, N., Khir, N.H.M., Ismail, M., Razak, F.H.A.: Gait-based emotion detection of children with autism spectrum disorders: a preliminary investigation. Procedia Comput. Sci. 76, 342–348 (2015). 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IEEE IRIS2015)
Mannini, A., Martinez-Manzanera, O., Lawerman, T.F., Trojaniello, D., Croce, U.D., Sival, D.A., Maurits, N.M., Sabatini, A.M.: Automatic classification of gait in children with early-onset ataxia or developmental coordination disorder and controls using inertial sensors. Gait Posture 52, 287–292 (2017)
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 14(2), 3362–3394 (2014)
Nam, Y., Park, J.W.: Child activity recognition based on cooperative fusion model of a triaxial accelerometer and a barometric pressure sensor. IEEE J. Biomed. Health Inform. 17(2), 420–426 (2013)
Otero, J., Sánchez, L.: Induction of descriptive fuzzy classifiers with the logitboost algorithm. Soft Comput. Fusion Found. Methodol. Appl. 10(9), 825–835 (2006)
Taborri, J., Scalona, E., Rossi, S., Palermo, E., Patané, F., Cappa, P.: Real-time gait detection based on hidden Markov model: is it possible to avoid training procedure? In: 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings, pp. 141–145, May 2015
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The authors are very grateful to the National Council of Science and Technology (CONACYT) for supporting this work.
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Monrraga Bernardino, F., Sánchez-DelaCruz, E., Meza Ruíz, I.V. (2018). Knee-Ankle Sensor for Gait Characterization: Gender Identification Case. In: Brito-Loeza, C., Espinosa-Romero, A. (eds) Intelligent Computing Systems. ISICS 2018. Communications in Computer and Information Science, vol 820. Springer, Cham. https://doi.org/10.1007/978-3-319-76261-6_3
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DOI: https://doi.org/10.1007/978-3-319-76261-6_3
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