Posture Estimation of Curve Running Motion Using Nano-Biosensor and Machine Learning
Autor:
Wu, Xiaoming
; Cao, Yu
; Wang, Yu
; Li, Bing
; Yang, Haitao
; Raja, S.P.
Fecha:
07/2024Palabra clave:
Revista / editorial:
International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)Citación:
X. Wu, Y. Cao, Y. Wang, B. Li, H. Yang, S. P. Raja. Posture Estimation of Curve Running Motion Using Nano-Biosensor and Machine Learning, International Journal of Interactive Multimedia and Artificial Intelligence, (2024), http://dx.doi.org/10.9781/ijimai.2024.07.001Tipo de Ítem:
article![Open Access Open Access](/themes/Mirage2/images/reunir/openaccess.png)
Resumen:
Curve running is a common form of training and competition. Conducting research on posture estimation during curve running can provide more accurate training and competition data for athletes. However, due to the unique nature of curve running, traditional posture estimation methods neglect the temporal changes in athlete posture, resulting in a decrease in estimation accuracy. Therefore, a posture estimation method for curve running motion using nano-biosensor and machine learning is proposed. First, the motion parameters of humans are collected by nano-biosensor, and the posture coordinates are obtained preliminarily. Second, the posture coordinates are established according to the human motion parameters, and the curve running posture data is obtained and filtered to obtain more accurate data. Finally, the Bayesian network in machine learning is used to continuously track the posture, and a nonlinear equation is established to fuse the posture angle obtained by the sensor and the posture tracked by the Bayesian network, to realize the posture estimation of curve running motion. The results show that the proposed estimation method has a good motion posture estimation effect, and the hip joint estimation error, knee joint estimation error and ankle joint estimation error are all less than 5°, and the endpoint displacement estimation offset rate is less than 2%. It can realize accurate motion posture estimation of curve running motion, and has important application value in the field of track training.
Ficheros en el ítem
![icon](/themes/Mirage2/images/reunir/icons/pdf.png)
Nombre: Posture Estimation of Curve Running Motion Using.pdf
Tamaño: 1.508Mb
Formato: application/pdf
Este ítem aparece en la(s) siguiente(s) colección(es)
Estadísticas de uso
Año |
2012 |
2013 |
2014 |
2015 |
2016 |
2017 |
2018 |
2019 |
2020 |
2021 |
2022 |
2023 |
2024 |
Vistas |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
15 |
Descargas |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
16 |
Ítems relacionados
Mostrando ítems relacionados por Título, autor o materia.
-
Twelve-crystal prototype of Li2MoO4 scintillating bolometers for CUPID and CROSS experiments
Alfonso, K.; Armatol, A.; Augier, C.; Avignone III, F. T.; Azzolini, O.; Balata, M.; Bandac, I.C.; Barabash, A. S.; Bari, G.; Barresi, A.; Baudin, D.; Bellini, F.; Benato, G.; Berest, V.; Beretta, M.; Bettelli, M.; Biassoni, M.; Billard, J.; Boldrini, V.; Branca, A.; Brofferio, C.; Bucci, C.; Calvo-Mozota, José María; Camilleri, J.; Campani, A.; Capelli, C.; Capelli, S.; Cappelli, L.; Cardani, L.; Carniti, P.; Casali, N.; Celi, E.; Chang, C.; Chiesa, D.; Clemenza, M.; Colantoni, I.; Copello, S.; Craft, E.; Cremonesi, O.; Creswick, R. J.; Cruciani, A.; D'Addabbo, A.; D'Imperio, G.; Dabagov, S.; Dafinei, I.; Danevich, F. A.; De Jesus, M.; de Marcillac, P.; Dell'Oro, S.; Di Domizio, S.; Di Lorenzo, S.; Dixon, T.; Dompé, V.; Drobizhev, A.; Dumoulin, L.; Fantini, G.; Faverzani, M.; Ferri, E.; Ferri, F.; Ferroni, F.; Figueroa-Feliciano, E.; Foggetta, L.; Formaggio, J.; Franceschi, A.; Fu, C.; Fu, S.; Fujikawa, B. K.; Gallas, A.; Gascon, J.; Ghislandi, S.; Giachero, A.; Gianvecchio, A.; Girola, M.; Gironi, L.; Giuliani, A.; Gorla, P.; Gotti, C.; Grant, C.; Gras, P.; Guillaumon, P. V.; Gutierrez, T. D.; Han, K.; Hansen, E. V.; Heeger, K. M.; Helis, D. L.; Huang, H. Z.; Ianni, A.; Imbert, L.; Johnston, J.; Juillard, A.; Karapetrov, G.; Keppel, G.; Khalife, H.; Kobychev, V. V.; Kolomensky, Yu. G.; Konovalov, S.I.; Kowalski, R.; Langford, T.; Lefevre, M.; Liu, R.; Liu, Y.; Loaiza, P.; Ma, L.; Madhukuttan, M.; Mancarella, F.; Marrache-Kikuchi, C. A.; Marini, L.; Marnieros, S.; Martinez, M.; Maruyama, R. H.; Ph. Mas; Mayer, D.; Mazzitelli, G.; Mei, Y.; Milana, S.; Morganti, S.; Napolitano, T.; Nastasi, M.; Nikkel, J.; Nisi, S.; Nones, C.; Norman, E. B.; Novosad, V.; Nutini, I.; O'Donnell, T.; Olivieri, E.; Olmi, M.; Ouellet, J. L.; Pagan, S.; Pagliarone, C.; Pagnanini, L.; Pattavina, L.; Pavan, M.; Peng, H.; Pessina, G.; Pettinacci, V.; Pira, C.; Pirro, S.; Poda, D. V.; Polischuk, O. G.; Ponce, I.; Pozzi, S.; Previtali, E.; Puiu, A.; Quitadamo, S.; Ressa, A.; Rizzoli, R.; Rosenfeld, C.; Rosier, P.; Scarpaci, J. A.; Schmidt, B.; Sharma, V.; Shlegel, V. N.; Singh, V.; Sisti, M.; Slocum, P.; Speller, D.; Surukuchi, P. T.; Taffarello, L.; Tomei, C.; Torres, J. A.; Tretyak, V. I.; Tsymbaliuk, A.; Velazquez, M.; Vetter, K. J.; Wagaarachchi, S. L.; Wang, G.; Wang, L.; Wang, R.; Welliver, B.; Wilson, J.; Wilson, K.; Winslow, L. A.; Xue, M.; Yan, L.; Yang, J; Yefremenko, V.; Umatov, V. I.; Zarytskyy, M. M.; Zhang, J.; Zolotarova, A.; Zucchelli, S. (Journal of Instrumentation, 2023)An array of twelve 0.28 kg lithium molybdate (LMO) low-temperature bolometers equipped with 16 bolometric Ge light detectors, aiming at optimization of detector structure for CROSS and CUPID double-beta decay experiments, ... -
Emergence of the Online-Merge-Offline (OMO) Learning Wave in the Post-COVID-19 Era: A Pilot Study
Huang, Ronghuai; Tlili, Ahmed; Wang, Huanhuan; Shi, Yihong; Bonk, Curtis J.; Yang, Junfeng; Burgos, Daniel (Sustainability (Switzerland), 2021)The COVID-19 pandemic revealed the need for new innovative methods to effectively maintain education in times of crisis and uncertainty. This study first presents the Online-Merge- Offline (OMO) learning approach, a way ... -
Integration of Genetic Programming and TABU Search Mechanism for Automatic Detection of Magnetic Resonance Imaging in Cervical Spondylosis
Juan, Chun-Jung; Wang, Chen-Shu; Lee, Bo-Yi; Chiang, Shang-Yu; Yeh, Chun-Chang; Cho, Der-Yang; Shen, Wu-Chung (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 09/2021)Cervical spondylosis is a kind of degenerative disease which not only occurs in elder patients. The age distribution of patients is unfortunately decreasing gradually. Magnetic Resonance Imaging (MRI) is the best tool to ...