Propuesta para el análisis de la marcha mediante fusión de sensores inerciales-magnéticos y ópticos

Se presenta una propuesta para el desarrollo de un protocolo de medición para el análisis del movimiento de las extremidades inferiores durante la marcha, con el uso de un sistema de medición basado en unidades de procesamiento de movimiento inercial-magnético y un sistema óptico. Inicialmente, se p...

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Autores:: Cuervo, Mauro Callejas
Vélez-Guerrero , Manuel A.
Alarcón-Aldana, Andrea C.

Tipo de recurso:: Article of journal

Fecha de publicación:: 2020

Institución:: Universidad EIA .

Repositorio:: Repositorio EIA .

Idioma:: eng

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dc.title.spa.fl_str_mv	Propuesta para el análisis de la marcha mediante fusión de sensores inerciales-magnéticos y ópticos
dc.title.translated.eng.fl_str_mv	Proposal for Gait Analysis Using Fusion of Inertial-Magnetic and Optical Sensors
title	Propuesta para el análisis de la marcha mediante fusión de sensores inerciales-magnéticos y ópticos
spellingShingle	Propuesta para el análisis de la marcha mediante fusión de sensores inerciales-magnéticos y ópticos Inertial magnetic sensor gait analysis human motion depth cameras sensor fusion Sensor magnético inercial análisis de la marcha movimiento humano cámaras de profundidad fusión de sensores
title_short	Propuesta para el análisis de la marcha mediante fusión de sensores inerciales-magnéticos y ópticos
title_full	Propuesta para el análisis de la marcha mediante fusión de sensores inerciales-magnéticos y ópticos
title_fullStr	Propuesta para el análisis de la marcha mediante fusión de sensores inerciales-magnéticos y ópticos
title_full_unstemmed	Propuesta para el análisis de la marcha mediante fusión de sensores inerciales-magnéticos y ópticos
title_sort	Propuesta para el análisis de la marcha mediante fusión de sensores inerciales-magnéticos y ópticos
dc.creator.fl_str_mv	Cuervo, Mauro Callejas Vélez-Guerrero , Manuel A. Alarcón-Aldana, Andrea C.
dc.contributor.author.spa.fl_str_mv	Cuervo, Mauro Callejas Vélez-Guerrero , Manuel A. Alarcón-Aldana, Andrea C.
dc.subject.eng.fl_str_mv	Inertial magnetic sensor gait analysis human motion depth cameras sensor fusion
topic	Inertial magnetic sensor gait analysis human motion depth cameras sensor fusion Sensor magnético inercial análisis de la marcha movimiento humano cámaras de profundidad fusión de sensores
dc.subject.spa.fl_str_mv	Sensor magnético inercial análisis de la marcha movimiento humano cámaras de profundidad fusión de sensores
description	Se presenta una propuesta para el desarrollo de un protocolo de medición para el análisis del movimiento de las extremidades inferiores durante la marcha, con el uso de un sistema de medición basado en unidades de procesamiento de movimiento inercial-magnético y un sistema óptico. Inicialmente, se presenta el estado del arte en términos de métodos y herramientas para la captura biomecánica de movimientos, para finalmente explorar los protocolos utilizados en las ciencias de la salud para el análisis de la marcha. La propuesta de medición realizada en este documento utiliza características robustas de la tecnología inercial-magnética y óptica que puede ser usado en el diagnóstico médico. La aplicación de ésta propuesta puede generar herramientas que impactan positivamente en los campos de la salud y la medicina.
publishDate	2020
dc.date.accessioned.none.fl_str_mv	2020-06-21 00:00:00 2022-06-17T20:21:04Z
dc.date.available.none.fl_str_mv	2020-06-21 00:00:00 2022-06-17T20:21:04Z
dc.date.issued.none.fl_str_mv	2020-06-21
dc.type.spa.fl_str_mv	Artículo de revista
dc.type.eng.fl_str_mv	Journal article
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dc.relation.references.eng.fl_str_mv	Adamová B., Kutilek P., Cakrt O., Svoboda Z., Viteckova S., Smrcka P. Quantifying postural stability of patients with cerebellar disorder during quiet stance using three-axis accelerometer. Biomed. Signal Process. Control, vol. 40. pp. 378–384, 2018. Azman A.M, Kuga H., Sagawa K., Nagai, C. Fastest Gait Parameters Estimation Precision Comparison Utilizing High-Sensitivity and Low-Sensitivity Inertial Sensor. Springer, Singapore. pp. 79–84, 2018. Baek S., Kim M. Real-Time Tracking IDs and Joints of Users. VII International Conference on Network, Communication and Computing, pp. 221-226, 2018. Bevilacqua V. et al. A comprehensive approach for physical rehabilitation assessment in multiple sclerosis patients based on gait analysis BT. Advances in Intelligent Systems and Computing, vol. 590. Springer Verlag, Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy. pp. 119–128, 2018. Bilesan A., Behzadipour S., Tsujita T., Komizunai S., Konno A. Markerless Human Motion Tracking Using Microsoft Kinect SDK and Inverse Kinematics. 12th Asian Control Conference, ASCC 2019, vol. 12, p. 149757, 2019. Budzyńska A., Jagielski M., Żyliński M., Cybulski G., Niewiadomski W. Verification of Selected Gait Parameters Derived from Inertial Sensors Using Simple Smartphone Based Optical System. Advances in Intelligent Systems and Computing, vol. 1044, pp. 87-94, 2020. Callejas-Cuervo M., Gutierrez R.M., Hernandez A.I. Joint amplitude MEMS based measurement platform for low cost and high accessibility telerehabilitation: Elbow case study. J. Bodyw. Mov. Ther., vol. 21, no. 3. pp. 574–581, 2017. Charlton J., Xia H., Shull P., Hunt M. Validity and reliability of a shoe-embedded sensor module for measuring foot progression angle during over-ground walking. Journal of Biomechanics, vol. 89, pp. 123-127, 2019. Cuervo M.C., Olaya A.F., Salamanca R.M. Biomechanical motion capture methods focused on tele-physiotherapy. 2013 Pan American Health Care Exchanges PAHCE. pp. 1–6, 2013. Dawe R., et al. Expanding instrumented gait testing in the community setting: A portable, depth-sensing camera captures joint motion in older adults. PLOS ONE, vol. 14, no. 5, p. e0215995, 2019. Deligianni F., Wong C., Lo B., Yang, G.-Z. A fusion framework to estimate plantar ground force distributions and ankle dynamics. Inf. Fusion, vol. 41. pp. 255–263, 2018. El Maachi I., Bilodeau G., Bouachir W. Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait. Expert Systems with Applications, vol. 143, p. 113075, 2020. Fleron M.K., Ubbesen N.C.H., Battistella F., Dejtiar D.L., Oliveira A.S. Accuracy between optical and inertial motion capture systems for assessing trunk speed during preferred gait and transition periods, Sport. Biomech. pp. 1–12, 2018. Hanawa H., et al. Validity of inertial measurement units in assessing segment angles and mechanical energies of elderly persons during sit-to-stand motion. 58th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), 2019. Iosa M., Picerno P., Paolucci S., Morone G. Wearable inertial sensors for human movement analysis. Expert Rev. Med. Devices, vol. 4444. pp. 1–19, 2017. Kim M., Lee D. Wearable inertial sensor based parametric calibration of lower-limb kinematics. Sensors Actuators, A Phys., vol. 265. pp. 280–296, 2017. LeMoyne R., Mastroianni T. The rise of inertial measurement units. Smart Sensors, Measurement and Instrumentation, vol. 27. Springer International Publishing, Department of Biological Sciences, Center for Bioengineering Innovation, Northern Arizona University, Flagstaff, AZ, United States. pp. 45–58, 2018. McGrath T., Fineman R., Stirling L. An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors. Sensors, vol. 18, no. 6. p. 1882, 2018. Marxreiter F., et al. Sensor-based gait analysis of individualized improvement during apomorphine titration in Parkinson’s disease. Journal of Neurology. vol. 265, no. 11. pp. 2656-2665, 2019. Park S., Ho Y., Chun M., Choi J., Moon Y. Measurement and Analysis of Gait Pattern during Stair Walk for Improvement of Robotic Locomotion Rehabilitation System. Applied Bionics and Biomechanics, vol. 2019, pp. 1-12, 2019. Petraglia F., Scarcella L., Pedrazzi G., Brancato L., Puers R., Costantino C. Inertial sensors versus standard systems in gait analysis: a systematic review and meta-analysis. European Journal of Physical and Rehabilitation Medicine, vol. 55, no. 2, 2019. Qiu S., Wang Z., Zhao H., Qin K., Li Z., Hu H. Inertial/magnetic sensors based pedestrian dead reckoning by means of multi-sensor fusion. Inf. Fusion, vol. 39. pp. 108–119, 2018. Rana S., Dey M., Ghavami M., Dudley S. Non-Contact Human Gait Identification through IR-UWB Edge-Based Monitoring Sensor. IEEE Sensors Journal, vol. 19, no. 20, pp. 9282-9293, 2019. Ren P., et al. Movement Symmetry Assessment by Bilateral Motion Data Fusion. IEEE Transactions on Biomedical Engineering, vol. 66, no. 1, pp. 225-236, 2019. Schwartz M.H., Rozumalski A. The gait deviation index: A new comprehensive index of gait pathology. Gait Posture. vol. 28, no. 3. pp. 351–357, 2008. Shiotani M., Watanabe T., Murakami K., Kuge N. Research on detection method for abnormal gait using three-dimensional thigh motion analysis with inertial sensor. Transactions of Japanese Society for Medical and Biological Engineering, vol. 57, no. 1, pp. 1-7, 2019. Sprager S., Juric M.B. Inertial Sensor-Based Gait Recognition: A Review. Sensors., vol. 15, no. 9. pp. 22089–22127, 2015. Sun C., Wang C., Lai W. Gait analysis and recognition prediction of the human skeleton based on migration learning. Physica A: Statistical Mechanics and its Applications, vol. 532, p. 121812, 2019. Tjhai C., Steward J., Lichti D., O’Keefe K. Using a mobile range-camera motion capture system to evaluate the performance of integration of multiple low-cost wearable sensors and gait kinematics for pedestrian navigation in realistic environments. 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS. pp. 294–300, 2018. Vo N., Tuan A., Van T.V., Vu N., Hau D., Thang N.D. Abnormal Gait Detection and Classification Using Depth Camera. 6th Int.Conf. Dev. Biomed. Eng. Vietnam (BME6), IFMBE Proc. pp. 1–6, 2018. Wang Y., Cang S., Yu H. A survey on wearable sensor modality centred human activity recognition in health care. Expert Systems with Applications, vol. 137, pp. 167-190, 2019. Wagner J., et al. Comparison of two techniques for monitoring of human movements. Meas. J. Int. Meas. Confed., vol. 111. pp. 420–431, 2017. Watanabe T., Tadano T. An Examination of Stimulation Timing Patterns for Mobile FES Cycling Under Closed-Loop Control of Low Cycling Speed. Converging Clinical and Engineering Research on Neurorehabilitation III, vol. 21, pp. 1106-1110, 2018. Wolosker N., Nakano L., Rosoky R.A., Puech-Leao P. Evaluation of walking capacity over time in 500 patients with intermittent claudication who underwent clinical treatment. Arch. Intern. Med., vol. 163. pp. 2296–300, 2003. Wouda F.J., et al. Estimation of Vertical Ground Reaction Forces and Sagittal Knee Kinematics During Running Using Three Inertial Sensors, Front. Physiol., vol. 9. p. 218, 2018. Whittle M.W. An Introduction to Gait Analysis. 4th ed. Oxford: Butterworth-Heinemann, 2007. Xie N., Mok P.Y. Investigation on human body movements and the resulting body measurement variations. AHFE 2017 International Conference on Physical Ergonomics and Human Factors, vol. 602, Springer Verlag, The Hong Kong Polytechnic University, Kowloon, Hong Kong. pp. 387–399, 2018. Xu C., He J., Zhang X., Yao C., Tseng P.-H. Geometrical kinematic modeling on human motion using method of multi-sensor fusion. Inf. Fusion, vol. 41. pp. 243–254, 2018. Zago M. et al. Gait evaluation using inertial measurement units in subjects with Parkinson’s disease. J. Electromyogr. Kinesiol., vol. 42, pp. 44–48, 2018.
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spelling	Cuervo, Mauro Callejas344fda061f0d1293715b4ba95a6edab5300Vélez-Guerrero , Manuel A.7aeacf8ad38dba70d82fbf9586d7c094300Alarcón-Aldana, Andrea C.257322c17926b9be8db6970f8b7efd8a3002020-06-21 00:00:002022-06-17T20:21:04Z2020-06-21 00:00:002022-06-17T20:21:04Z2020-06-211794-1237https://repository.eia.edu.co/handle/11190/513710.24050/reia.v17i34.14722463-0950https://doi.org/10.24050/reia.v17i34.1472Se presenta una propuesta para el desarrollo de un protocolo de medición para el análisis del movimiento de las extremidades inferiores durante la marcha, con el uso de un sistema de medición basado en unidades de procesamiento de movimiento inercial-magnético y un sistema óptico. Inicialmente, se presenta el estado del arte en términos de métodos y herramientas para la captura biomecánica de movimientos, para finalmente explorar los protocolos utilizados en las ciencias de la salud para el análisis de la marcha. La propuesta de medición realizada en este documento utiliza características robustas de la tecnología inercial-magnética y óptica que puede ser usado en el diagnóstico médico. La aplicación de ésta propuesta puede generar herramientas que impactan positivamente en los campos de la salud y la medicina.A proposed measurement protocol for the lower limbs movement analysis during walking is presented, with the use of a measurement system based on inertial-magnetic motion processing units and an optical system. Initially, the state of the art in terms of methods and tools for the biomechanical capture of movements is shown, to finally explore the protocols used in the health sciences for the gait analysis. The measurement proposal made in this document uses robust features of inertial-magnetic and optical technology that can be used in medical diagnosis. The application of this proposal can generate tools that have a positive impact in the fields of health and medicine.application/pdfengFondo Editorial EIA - Universidad EIARevista EIA - 2020https://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistas.eia.edu.co/index.php/reveia/article/view/1472Inertial magnetic sensorgait analysishuman motiondepth camerassensor fusionSensor magnético inercialanálisis de la marchamovimiento humanocámaras de profundidadfusión de sensoresPropuesta para el análisis de la marcha mediante fusión de sensores inerciales-magnéticos y ópticosProposal for Gait Analysis Using Fusion of Inertial-Magnetic and Optical SensorsArtículo de revistaJournal articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/redcol/resource_type/ARTREFhttp://purl.org/coar/version/c_970fb48d4fbd8a85Adamová B., Kutilek P., Cakrt O., Svoboda Z., Viteckova S., Smrcka P. Quantifying postural stability of patients with cerebellar disorder during quiet stance using three-axis accelerometer. Biomed. Signal Process. Control, vol. 40. pp. 378–384, 2018.Azman A.M, Kuga H., Sagawa K., Nagai, C. Fastest Gait Parameters Estimation Precision Comparison Utilizing High-Sensitivity and Low-Sensitivity Inertial Sensor. Springer, Singapore. pp. 79–84, 2018.Baek S., Kim M. Real-Time Tracking IDs and Joints of Users. VII International Conference on Network, Communication and Computing, pp. 221-226, 2018.Bevilacqua V. et al. A comprehensive approach for physical rehabilitation assessment in multiple sclerosis patients based on gait analysis BT. Advances in Intelligent Systems and Computing, vol. 590. Springer Verlag, Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy. pp. 119–128, 2018.Bilesan A., Behzadipour S., Tsujita T., Komizunai S., Konno A. Markerless Human Motion Tracking Using Microsoft Kinect SDK and Inverse Kinematics. 12th Asian Control Conference, ASCC 2019, vol. 12, p. 149757, 2019.Budzyńska A., Jagielski M., Żyliński M., Cybulski G., Niewiadomski W. Verification of Selected Gait Parameters Derived from Inertial Sensors Using Simple Smartphone Based Optical System. Advances in Intelligent Systems and Computing, vol. 1044, pp. 87-94, 2020.Callejas-Cuervo M., Gutierrez R.M., Hernandez A.I. Joint amplitude MEMS based measurement platform for low cost and high accessibility telerehabilitation: Elbow case study. J. Bodyw. Mov. Ther., vol. 21, no. 3. pp. 574–581, 2017.Charlton J., Xia H., Shull P., Hunt M. Validity and reliability of a shoe-embedded sensor module for measuring foot progression angle during over-ground walking. Journal of Biomechanics, vol. 89, pp. 123-127, 2019.Cuervo M.C., Olaya A.F., Salamanca R.M. Biomechanical motion capture methods focused on tele-physiotherapy. 2013 Pan American Health Care Exchanges PAHCE. pp. 1–6, 2013.Dawe R., et al. Expanding instrumented gait testing in the community setting: A portable, depth-sensing camera captures joint motion in older adults. PLOS ONE, vol. 14, no. 5, p. e0215995, 2019.Deligianni F., Wong C., Lo B., Yang, G.-Z. A fusion framework to estimate plantar ground force distributions and ankle dynamics. Inf. Fusion, vol. 41. pp. 255–263, 2018.El Maachi I., Bilodeau G., Bouachir W. Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait. Expert Systems with Applications, vol. 143, p. 113075, 2020.Fleron M.K., Ubbesen N.C.H., Battistella F., Dejtiar D.L., Oliveira A.S. Accuracy between optical and inertial motion capture systems for assessing trunk speed during preferred gait and transition periods, Sport. Biomech. pp. 1–12, 2018.Hanawa H., et al. Validity of inertial measurement units in assessing segment angles and mechanical energies of elderly persons during sit-to-stand motion. 58th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), 2019.Iosa M., Picerno P., Paolucci S., Morone G. Wearable inertial sensors for human movement analysis. Expert Rev. Med. Devices, vol. 4444. pp. 1–19, 2017.Kim M., Lee D. Wearable inertial sensor based parametric calibration of lower-limb kinematics. Sensors Actuators, A Phys., vol. 265. pp. 280–296, 2017.LeMoyne R., Mastroianni T. The rise of inertial measurement units. Smart Sensors, Measurement and Instrumentation, vol. 27. Springer International Publishing, Department of Biological Sciences, Center for Bioengineering Innovation, Northern Arizona University, Flagstaff, AZ, United States. pp. 45–58, 2018.McGrath T., Fineman R., Stirling L. An Auto-Calibrating Knee Flexion-Extension Axis Estimator Using Principal Component Analysis with Inertial Sensors. Sensors, vol. 18, no. 6. p. 1882, 2018.Marxreiter F., et al. Sensor-based gait analysis of individualized improvement during apomorphine titration in Parkinson’s disease. Journal of Neurology. vol. 265, no. 11. pp. 2656-2665, 2019.Park S., Ho Y., Chun M., Choi J., Moon Y. Measurement and Analysis of Gait Pattern during Stair Walk for Improvement of Robotic Locomotion Rehabilitation System. Applied Bionics and Biomechanics, vol. 2019, pp. 1-12, 2019.Petraglia F., Scarcella L., Pedrazzi G., Brancato L., Puers R., Costantino C. Inertial sensors versus standard systems in gait analysis: a systematic review and meta-analysis. European Journal of Physical and Rehabilitation Medicine, vol. 55, no. 2, 2019.Qiu S., Wang Z., Zhao H., Qin K., Li Z., Hu H. Inertial/magnetic sensors based pedestrian dead reckoning by means of multi-sensor fusion. Inf. Fusion, vol. 39. pp. 108–119, 2018.Rana S., Dey M., Ghavami M., Dudley S. Non-Contact Human Gait Identification through IR-UWB Edge-Based Monitoring Sensor. IEEE Sensors Journal, vol. 19, no. 20, pp. 9282-9293, 2019.Ren P., et al. Movement Symmetry Assessment by Bilateral Motion Data Fusion. IEEE Transactions on Biomedical Engineering, vol. 66, no. 1, pp. 225-236, 2019.Schwartz M.H., Rozumalski A. The gait deviation index: A new comprehensive index of gait pathology. Gait Posture. vol. 28, no. 3. pp. 351–357, 2008.Shiotani M., Watanabe T., Murakami K., Kuge N. Research on detection method for abnormal gait using three-dimensional thigh motion analysis with inertial sensor. Transactions of Japanese Society for Medical and Biological Engineering, vol. 57, no. 1, pp. 1-7, 2019.Sprager S., Juric M.B. Inertial Sensor-Based Gait Recognition: A Review. Sensors., vol. 15, no. 9. pp. 22089–22127, 2015.Sun C., Wang C., Lai W. Gait analysis and recognition prediction of the human skeleton based on migration learning. Physica A: Statistical Mechanics and its Applications, vol. 532, p. 121812, 2019.Tjhai C., Steward J., Lichti D., O’Keefe K. Using a mobile range-camera motion capture system to evaluate the performance of integration of multiple low-cost wearable sensors and gait kinematics for pedestrian navigation in realistic environments. 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS. pp. 294–300, 2018.Vo N., Tuan A., Van T.V., Vu N., Hau D., Thang N.D. Abnormal Gait Detection and Classification Using Depth Camera. 6th Int.Conf. Dev. Biomed. Eng. Vietnam (BME6), IFMBE Proc. pp. 1–6, 2018.Wang Y., Cang S., Yu H. A survey on wearable sensor modality centred human activity recognition in health care. Expert Systems with Applications, vol. 137, pp. 167-190, 2019.Wagner J., et al. Comparison of two techniques for monitoring of human movements. Meas. J. Int. Meas. Confed., vol. 111. pp. 420–431, 2017.Watanabe T., Tadano T. An Examination of Stimulation Timing Patterns for Mobile FES Cycling Under Closed-Loop Control of Low Cycling Speed. Converging Clinical and Engineering Research on Neurorehabilitation III, vol. 21, pp. 1106-1110, 2018.Wolosker N., Nakano L., Rosoky R.A., Puech-Leao P. Evaluation of walking capacity over time in 500 patients with intermittent claudication who underwent clinical treatment. Arch. Intern. Med., vol. 163. pp. 2296–300, 2003.Wouda F.J., et al. Estimation of Vertical Ground Reaction Forces and Sagittal Knee Kinematics During Running Using Three Inertial Sensors, Front. Physiol., vol. 9. p. 218, 2018.Whittle M.W. An Introduction to Gait Analysis. 4th ed. Oxford: Butterworth-Heinemann, 2007.Xie N., Mok P.Y. Investigation on human body movements and the resulting body measurement variations. AHFE 2017 International Conference on Physical Ergonomics and Human Factors, vol. 602, Springer Verlag, The Hong Kong Polytechnic University, Kowloon, Hong Kong. pp. 387–399, 2018.Xu C., He J., Zhang X., Yao C., Tseng P.-H. Geometrical kinematic modeling on human motion using method of multi-sensor fusion. Inf. Fusion, vol. 41. pp. 243–254, 2018.Zago M. et al. Gait evaluation using inertial measurement units in subjects with Parkinson’s disease. J. Electromyogr. Kinesiol., vol. 42, pp. 44–48, 2018.https://revistas.eia.edu.co/index.php/reveia/article/download/1472/1364Núm. 34 , Año 20201134117Revista EIAPublicationOREORE.xmltext/xml2685https://repository.eia.edu.co/bitstreams/02e13165-7245-480e-91dd-40e443596481/downloadf02c790cd577b7752727b7be36e0e7f9MD5111190/5137oai:repository.eia.edu.co:11190/51372023-07-25 16:57:42.271https://creativecommons.org/licenses/by-nc-nd/4.0Revista EIA - 2020metadata.onlyhttps://repository.eia.edu.coRepositorio Institucional Universidad EIAbdigital@metabiblioteca.com

Propuesta para el análisis de la marcha mediante fusión de sensores inerciales-magnéticos y ópticos

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