Marker-Less Monitoring Protocol to Analyze Biomechanical Joint Metrics During Pedaling
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| Titel: | Marker-Less Monitoring Protocol to Analyze Biomechanical Joint Metrics During Pedaling |
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| Autoren: | Serrancolí, Gil, Bogatikov, Peter, Palés Huix, Joana, Forcada, Ainoa, Sánchez Egea, Antonio José, Torner Ribé, Jordi, Kanaan Izquierdo, Samir, Susín Sánchez, Antonio |
| Weitere Verfasser: | Universitat Politècnica de Catalunya. Departament d'Enginyeria Mecànica, Universitat Politècnica de Catalunya. Departament d’Enginyeria Gràfica i de Disseny, Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. Departament de Matemàtiques, Universitat Politècnica de Catalunya. LAM - Laboratori d'Aplicacions Multimèdia i TIC, Universitat Politècnica de Catalunya. B2SLab - Bioinformatics and Biomedical Signals Laboratory, Universitat Politècnica de Catalunya. ViRVIG - Grup de Recerca en Visualització, Realitat Virtual i Interacció Gràfica |
| Quelle: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) IEEE Access, Vol 8, Pp 122782-122790 (2020) |
| Verlagsinformationen: | Institute of Electrical and Electronics Engineers (IEEE), 2020. |
| Publikationsjahr: | 2020 |
| Schlagwörter: | Biomecànica, TK1-9971, Cycling joint power, Informàtica::Aplicacions de la informàtica::Aplicacions informàtiques a la física i l‘enginyeria [Àrees temàtiques de la UPC], Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Aplicacions informàtiques a la física i l‘enginyeria, 03 medical and health sciences, 0302 clinical medicine, Marker-less, Cycling joint moments, motion capture, cycling joint moments, Enginyeria biomèdica::Biomecànica [Àrees temàtiques de la UPC], Biomechanics, Electrical engineering. Electronics. Nuclear engineering, Àrees temàtiques de la UPC::Enginyeria biomèdica::Biomecànica, cycling joint power, Motion capture |
| Beschreibung: | Marker-less systems are becoming popular to detect a human skeleton in an image automatically. However, these systems have difficulties in tracking points when part of the body is hidden, or there is an artifact that does not belong to the subject (e.g., a bicycle).We present a low-cost tracking system combined with economic force-measurement sensors that allows the calculation of individual joint moments and powers affordable for anybody. The system integrates OpenPose (deep-learning based CCC library to detect human skeletons in an image) in a system of two webcams, to record videos of a cyclist, and seven resistive sensors to measure forces at the pedals and the saddle. OpenPose identifies the skeleton candidate using a convolution neural network. A corrective algorithm was written to automatically detect the hip, knee, ankle, metatarsal and heel points from webcam-recorded motions, which overcomes the limitations of the marker-less system. Then, with the information of external forces, an inverse dynamics analysis is applied in OpenSim to calculate the joint moments and powers at the hip, knee, and ankle joints. The results show that the obtained moments have similar shapes and trends compared to the literature values. Therefore, this represents a low-cost method that could be used to estimate relevant joint kinematics and dynamics, and consequently follow up or improve cycling training plans. |
| Publikationsart: | Article |
| Dateibeschreibung: | application/pdf |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.3006423 |
| Zugangs-URL: | https://ieeexplore.ieee.org/ielx7/6287639/8948470/09131774.pdf https://doaj.org/article/2eb50d899ced4368aea3d6aa8c7f47bc https://upcommons.upc.edu/bitstream/2117/328416/1/ACCESS3006423_finalversion.pdf https://ieeexplore.ieee.org/document/9131774 https://upcommons.upc.edu/handle/2117/328416 https://dblp.uni-trier.de/db/journals/access/access8.html#SerrancoliBHBER20 https://hdl.handle.net/2117/328416 https://doi.org/10.1109/access.2020.3006423 |
| Rights: | CC BY CC BY NC ND |
| Dokumentencode: | edsair.doi.dedup.....22b82d5e280d9b0661164859aba0496e |
| Datenbank: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | Marker-less systems are becoming popular to detect a human skeleton in an image automatically. However, these systems have difficulties in tracking points when part of the body is hidden, or there is an artifact that does not belong to the subject (e.g., a bicycle).We present a low-cost tracking system combined with economic force-measurement sensors that allows the calculation of individual joint moments and powers affordable for anybody. The system integrates OpenPose (deep-learning based CCC library to detect human skeletons in an image) in a system of two webcams, to record videos of a cyclist, and seven resistive sensors to measure forces at the pedals and the saddle. OpenPose identifies the skeleton candidate using a convolution neural network. A corrective algorithm was written to automatically detect the hip, knee, ankle, metatarsal and heel points from webcam-recorded motions, which overcomes the limitations of the marker-less system. Then, with the information of external forces, an inverse dynamics analysis is applied in OpenSim to calculate the joint moments and powers at the hip, knee, and ankle joints. The results show that the obtained moments have similar shapes and trends compared to the literature values. Therefore, this represents a low-cost method that could be used to estimate relevant joint kinematics and dynamics, and consequently follow up or improve cycling training plans. |
|---|---|
| ISSN: | 21693536 |
| DOI: | 10.1109/access.2020.3006423 |