Sleeve for Knee Angle Monitoring: An IMU-POF Sensor Fusion System

—The knee flexion-extension angle is an important variable to be monitored in various clinical scenarios, for example, during physical rehabilitation assessment. The purpose of this work is to develop and validate a sensor fusion system based on a knee sleeve for monitoring of physical therapy. The...

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Autores:: Vargas Valencia, Laura Susana
Schneider, Felipe B. A.
Leal Junior, Arnaldo G.
Caicedo Rodríguez, Pablo
Sierra Arévalo, Wilson A.
Rodríguez Cheu, Luis E.
Bastos Filho, Teodiano
Frizera Neto, Anselmo

Tipo de recurso:: Article of journal

Fecha de publicación:: 2021

Institución:: Escuela Colombiana de Ingeniería Julio Garavito

Repositorio:: Repositorio Institucional ECI

Idioma:: eng

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dc.title.eng.fl_str_mv	Sleeve for Knee Angle Monitoring: An IMU-POF Sensor Fusion System
title	Sleeve for Knee Angle Monitoring: An IMU-POF Sensor Fusion System
spellingShingle	Sleeve for Knee Angle Monitoring: An IMU-POF Sensor Fusion System Rehabilitación médica Medical rehabilitation Aparatos fisiológicos Physiological apparatus Fusión de datos multisensor Multisensor data fusion Sensores inerciales Ángulos articulares Movimiento estimación sensores POF Fusión de sensores Sistemas portátiles Inertial sensors Joint angles Motion estimation POF sensors Sensor fusion Wearable systems
title_short	Sleeve for Knee Angle Monitoring: An IMU-POF Sensor Fusion System
title_full	Sleeve for Knee Angle Monitoring: An IMU-POF Sensor Fusion System
title_fullStr	Sleeve for Knee Angle Monitoring: An IMU-POF Sensor Fusion System
title_full_unstemmed	Sleeve for Knee Angle Monitoring: An IMU-POF Sensor Fusion System
title_sort	Sleeve for Knee Angle Monitoring: An IMU-POF Sensor Fusion System
dc.creator.fl_str_mv	Vargas Valencia, Laura Susana Schneider, Felipe B. A. Leal Junior, Arnaldo G. Caicedo Rodríguez, Pablo Sierra Arévalo, Wilson A. Rodríguez Cheu, Luis E. Bastos Filho, Teodiano Frizera Neto, Anselmo
dc.contributor.author.none.fl_str_mv	Vargas Valencia, Laura Susana Schneider, Felipe B. A. Leal Junior, Arnaldo G. Caicedo Rodríguez, Pablo Sierra Arévalo, Wilson A. Rodríguez Cheu, Luis E. Bastos Filho, Teodiano Frizera Neto, Anselmo
dc.contributor.researchgroup.spa.fl_str_mv	GiBiome
dc.subject.armarc.none.fl_str_mv	Rehabilitación médica Medical rehabilitation Aparatos fisiológicos Physiological apparatus Fusión de datos multisensor Multisensor data fusion
topic	Rehabilitación médica Medical rehabilitation Aparatos fisiológicos Physiological apparatus Fusión de datos multisensor Multisensor data fusion Sensores inerciales Ángulos articulares Movimiento estimación sensores POF Fusión de sensores Sistemas portátiles Inertial sensors Joint angles Motion estimation POF sensors Sensor fusion Wearable systems
dc.subject.proposal.spa.fl_str_mv	Sensores inerciales Ángulos articulares Movimiento estimación sensores POF Fusión de sensores Sistemas portátiles
dc.subject.proposal.eng.fl_str_mv	Inertial sensors Joint angles Motion estimation POF sensors Sensor fusion Wearable systems
description	—The knee flexion-extension angle is an important variable to be monitored in various clinical scenarios, for example, during physical rehabilitation assessment. The purpose of this work is to develop and validate a sensor fusion system based on a knee sleeve for monitoring of physical therapy. The system consists of merging data from two inertial measurement units (IMUs) and an intensityvariation based Polymer Optical Fiber (POF) curvature sensor using a quaternion-based Multiplicative Extended Kalman Filter (MEKF). The proposed data fusion method is magnetometer-free and deals with sensors’ uncertainties through reliability intervals defined during gait. Walking trials were performed by twelve healthy participants using our knee sleeve system and results were validated against a gold standard motion capture system. Additionally, a comparison with other three knee angle estimation methods, which are exclusively based on IMUs, was carried out. The proposed system presented better performance (mean RMSE < 3.3◦, LFM coefficients, a1 = 0.99 ± 0.04, a0 = 0.70 ± 2.29, R2 = 0.98 ± 0.01 and ρC > 0.99) when compared to the other evaluated methods. Experimental results demonstrate the usability and feasibility of our system to estimate knee motion with high accuracy, repeatability, and reproducibility. This wearable system may be suitable for motion assessment in rehabilitation labs in future studies.
publishDate	2021
dc.date.issued.none.fl_str_mv	2021-02
dc.date.accessioned.none.fl_str_mv	2024-09-10T17:49:29Z
dc.date.available.none.fl_str_mv	2024-09-10T17:49:29Z
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.instname.spa.fl_str_mv	Universidad Escuela Colombiana de Ingeniería
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dc.relation.citationedition.spa.fl_str_mv	Vol. 25 No. 2 February 2021
dc.relation.citationendpage.spa.fl_str_mv	474
dc.relation.citationissue.spa.fl_str_mv	2
dc.relation.citationstartpage.spa.fl_str_mv	465
dc.relation.citationvolume.spa.fl_str_mv	25
dc.relation.ispartofjournal.eng.fl_str_mv	IEEE Journal of Biomedical and Health Informatics
dc.relation.references.spa.fl_str_mv	A. Muro-De-La-Herran, B. Garcia-Zapirain, and A. Mendez-Zorrilla, “Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications,” Sensors, vol. 14, no. 2, pp. 3362– 3394, 2014. R. Abbasi-Kesbi, A. Nikfarjam, and H. Memarzadeh-Tehran, “A patientcentric sensory system for in-home rehabilitation,” IEEE Sensors J., vol. 17, no. 2, pp. 524–533, Jan. 2016. R. A. Bloomfield, M. C. Fennema, K. A. McIsaac, and M. G. Teeter, “Proposal and validation of a knee measurement system for patients with osteoarthritis,” IEEE Trans. Biomed. Eng., vol. 66, no. 2, pp. 319–326, Feb. 2019. P. B. Shull,W. Jirattigalachote,M. A. Hunt,M. R. Cutkosky, and S. L. Delp, “Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention,” Gait Posture, vol. 40, no. 1, pp. 11–19, 2014. T. Seel, J. Raisch, and T. Schauer, “IMU-based joint angle measurement for gait analysis,” Sensors, vol. 14, no. 4, pp. 6891–6909, 2014. T. McGrath, R. Fineman, and L. Stirling, “An auto-calibrating knee flexion-extension axis estimator using principal component analysis with inertial sensors,” Sensors, vol. 18, no. 6, p. 1882, 2018. E. Allseits et al., “A novel method for estimating knee angle using two leg-mounted gyroscopes for continuous monitoring with mobile health devices,” Sensors, vol. 18, no. 9, p. 2759, 2018. A. Tognetti, F. Lorussi, N. Carbonaro, and D. De Rossi, “Wearable goniometer and accelerometer sensory fusion for knee joint angle measurement in daily life,” Sensors, vol. 15, no. 11, pp. 28 435–28 455, 2015 A. G. Leal-Junior et al., “POF-IMU sensor system: A fusion between inertial measurement units and POF sensors for low-cost and highly reliable systems,” Opt. Fiber Technol., vol. 43, pp. 82–89, 2018. I. Pasciuto et al., “How angular velocity features and different gyroscope noise types interact and determine orientation estimation accuracy,” Sensors, vol. 15, no. 9, pp. 23 983–24 001, 2015. E. Bergamini, G. Ligorio, A. Summa, G. Vannozzi, A. Cappozzo, and A. Sabatini, “Estimating orientation using magnetic and inertial sensors and different sensor fusion approaches: Accuracy assessment in manual and locomotion tasks,” Sensors, vol. 14, no. 10, pp. 18 625–18 649, 2014. C. L. Kendell and E. D. Lemaire, “Effect of mobility devices on orientation sensors that contain magnetometers,” Journal of Rehabilitation Research & Development, vol. 46, no. 7, pp. 957–963, 2009. D. Laidig, T. Schauer, and T. Seel, “Exploiting kinematic constraints to compensate magnetic disturbances when calculating joint angles of approximate hinge joints from orientation estimates of inertial sensors,” in Proc. Int. Conf. Rehabil. Robot., 2017, pp. 971–976. J. K. Lee and T. H. Jeon, “IMU-based but magnetometer-free joint angle estimation of constrained links,” in Proc. IEEE SENSORS, 2018, pp. 1–4 A. G. Leal-Junior et al., “Polymer optical fiber sensors in wearable devices: Toward novel instrumentation approaches for gait assistance devices,” IEEE Sensors J., vol. 18, no. 17, pp. 7085–7092, Sep. 2018. M. Krehel, M. Wolf, L. F. Boesel, R. M. Rossi, G.-L. Bona, and L. J. Scherer, “Development of a luminous textile for reflective pulse oximetry measurements,” Biomed. Opt. Express, vol. 5, no. 8, pp. 2537–2547, 2014 D. Lunni, G. Giordano, E. Sinibaldi, M. Cianchetti, and B. Mazzolai, “Shape estimation based on kalman filtering: Towards fully soft proprioception,” in Proc. IEEE Int. Conf. Soft Robot., 2018, pp. 541–546. L. Bilro, N. Alberto, J. L. Pinto, and R. Nogueira, “Optical sensors based on plastic fibers,” Sensors, vol. 12, no. 9, pp. 12 184–12 207, 2012. C. Massaroni, P. Saccomandi, and E. Schena, “Medical smart textiles based on fiber optic technology: an overview,” J. Functional Biomaterials, vol. 6, no. 2, pp. 204–221, 2015 A. M. Sabatini, “Estimating three-dimensional orientation of human body parts by inertial/magnetic sensing,” Sensors, vol. 11, no. 2, pp. 1489–1525, 2011 M. Ghobadi, P. Singla, and E. T. Esfahani, “Robust attitude estimation from uncertain observations of inertial sensors using covariance inflated multiplicative extended kalman filter,” IEEE Trans. Instrum. Meas., vol. 67, no. 1, pp. 209–217, Jan. 2017. H. G. Kortier, J. Antonsson, H. M. Schepers, F. Gustafsson, and P. H. Veltink, “Hand pose estimation by fusion of inertial and magnetic sensing aided by a permanent magnet,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 23, no. 5, pp. 796–806, Sep. 2015 J. N. Gross, Y. Gu, M. B. Rhudy, S. Gururajan, and M. R. Napolitano, “Flight-test evaluation of sensor fusion algorithms for attitude estimation,” IEEE Trans. Aerosp. Electron. Syst., vol. 48, no. 3, pp. 2128–2139, Jul. 2012. L. Bilro, J. Oliveira, J. Pinto, and R. Nogueira, “A reliable low-cost wireless and wearable gait monitoring system based on a plastic optical fibre sensor,” Meas. Sci. Technol., vol. 22, no. 4, 2011, Art. no. 045801. A. G. Leal-Junior, A. Frizera, and M. J. Pontes, “Sensitive zone parameters and curvature radius evaluation for polymer optical fiber curvature sensors,” Opt. Laser Technol., vol. 100, pp. 272–281, 2018. J. Favre, B. Jolles, R. Aissaoui, and K. Aminian, “Ambulatory measurement of 3d knee joint angle,” J. Biomechanics, vol. 41, no. 5, pp. 1029– 1035, 2008. E. L. Bishop, J. C. Küpper, I. R. Fjeld, G. Kuntze, and J. L. Ronsky, “Error reduction in the finite helical axis for knee kinematics,” Comput. Methods Biomechanics Biomed. Eng., vol. 21, no. 2, pp. 186–193, 2018. C. N. Teague et al., “Novel methods for sensing acoustical emissions from the knee for wearable joint health assessment,” IEEE Trans. Biomed. Eng., vol. 63, no. 8, pp. 1581–1590, Aug. 2016. D. Graurock, T. Schauer, and T. Seel, “User-adaptive inertial sensor network for feedback-controlled gait support systems,” in Proc. Int. Functional Elect. Stimulation Soc., 2016. [Online]. Available: https://ifess2016. inria.fr/files/2016/02/IFESS_2016_paper_39.pdf J. K. Lee and E. J. Park, “Quasi real-time gait event detection using shankattached gyroscopes,” Med. Biol. Eng. Comput., vol. 49, no. 6, pp. 707– 712, 2011 A. G. Leal-Junior, A. Frizera, C. Marques, and M. J. Pontes, “Viscoelastic features based compensation technique for polymer optical fiber curvature sensors,” Opt. Laser Technol., vol. 105, pp. 35–40, 2018. E. J. Lefferts, F. L. Markley, and M. D. Shuster, “Kalman filtering for spacecraft attitude estimation,” J. Guid. Control Dyn., vol. 5, no. 5, pp. 417–429, 1982. ] Vicon. Plug-in gait reference guide, 2016. [Online]. Available: https:// docs.vicon.com/display/Nexus25/Plug-in+Gait+Reference+Guide M. Iosa, A. Cereatti, A.Merlo, I. Campanini, S. Paolucci, and A. Cappozzo, “Assessment of waveform similarity in clinical gait data: The linear fit method,” BioMed Res. Int., vol. 2014, 2014, doi: 10.1155/2014/214156. I. Lawrence and K. Lin, “A concordance correlation coefficient to evaluate reproducibility,” Biometrics, vol. 45, no. 1, pp. 255–268, 1989. B. F. Mentiplay, M. Banky, R. A. Clark, M. B. Kahn, and G. Williams, “Lower limb angular velocity during walking at various speeds,” Gait Posture, vol. 65, pp. 190–196, 2018.
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spelling	Vargas Valencia, Laura Susana3bb776f27af644e1140019f840deef20Schneider, Felipe B. A.09f7cfae70c2ac6277c9fcc4288d4371Leal Junior, Arnaldo G.3898db8c2c964dfa2aba87fdc94fd400Caicedo Rodríguez, Pablofb0a219e1eef4d761f8c809ee5bf5244Sierra Arévalo, Wilson A.498a316f63c69920fd002e377e22515bRodríguez Cheu, Luis E.c3558132d09a4b7f2e70e9059d5bd334Bastos Filho, Teodiano1be20e09f2aa6c45d23fdb2c64e1e6c2Frizera Neto, Anselmob4f92b73ce43fbc600e56a7251934589GiBiome2024-09-10T17:49:29Z2024-09-10T17:49:29Z2021-02https://repositorio.escuelaing.edu.co/handle/001/32552168-2208Universidad Escuela Colombiana de IngenieríaRepositorio Digitalhttps://repositorio.escuelaing.edu.co/—The knee flexion-extension angle is an important variable to be monitored in various clinical scenarios, for example, during physical rehabilitation assessment. The purpose of this work is to develop and validate a sensor fusion system based on a knee sleeve for monitoring of physical therapy. The system consists of merging data from two inertial measurement units (IMUs) and an intensityvariation based Polymer Optical Fiber (POF) curvature sensor using a quaternion-based Multiplicative Extended Kalman Filter (MEKF). The proposed data fusion method is magnetometer-free and deals with sensors’ uncertainties through reliability intervals defined during gait. Walking trials were performed by twelve healthy participants using our knee sleeve system and results were validated against a gold standard motion capture system. Additionally, a comparison with other three knee angle estimation methods, which are exclusively based on IMUs, was carried out. The proposed system presented better performance (mean RMSE < 3.3◦, LFM coefficients, a1 = 0.99 ± 0.04, a0 = 0.70 ± 2.29, R2 = 0.98 ± 0.01 and ρC > 0.99) when compared to the other evaluated methods. Experimental results demonstrate the usability and feasibility of our system to estimate knee motion with high accuracy, repeatability, and reproducibility. This wearable system may be suitable for motion assessment in rehabilitation labs in future studies.El ángulo de flexión-extensión de la rodilla es una variable importante que debe ser monitoreada en diversos escenarios clínicos, por ejemplo, durante la evaluación de rehabilitación física. El propósito de este trabajo es desarrollar y validar un sensor sistema de fusión basado en una rodillera para el seguimiento de fisioterapia. El sistema consiste en fusionar datos de dos unidades de medición inercial (IMU) y una curvatura de fibra óptica de polímero (POF) basada en variación de intensidad sensor que utiliza un multiplicativo extendido basado en cuaterniones Filtro Kalman (MEKF). El método de fusión de datos propuesto. No tiene magnetómetro y aborda las incertidumbres de los sensores mediante intervalos de confiabilidad definidos durante la marcha. Doce participantes sanos realizaron pruebas de caminata utilizando nuestro sistema de rodillera y los resultados fueron validados frente a un sistema de captura de movimiento estándar de oro. Además, se realiza una comparación con otros tres cálculos del ángulo de la rodilla. métodos, que se basan exclusivamente en IMU, se llevó a cabo afuera. El sistema propuesto presentó mejor desempeño (media RMSE < 3,3◦, coeficientes LFM, a1 = 0,99 ± 0,04, a0 = 0,70 ± 2,29, R2 = 0,98 ± 0,01 y ρC > 0,99) en comparación con los demás métodos evaluados. Resultados experimentales demostrar la usabilidad y viabilidad de nuestro sistema para estimar el movimiento de la rodilla con alta precisión, repetibilidad y reproducibilidad. Este sistema portátil puede ser adecuado para Evaluación del movimiento en laboratorios de rehabilitación en futuros estudios.10 páginasapplication/pdfengIEEECanadáhttps://ieeexplore.ieee.org/document/9072540Sleeve for Knee Angle Monitoring: An IMU-POF Sensor Fusion SystemArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85Vol. 25 No. 2 February 2021474246525IEEE Journal of Biomedical and Health InformaticsA. Muro-De-La-Herran, B. Garcia-Zapirain, and A. Mendez-Zorrilla, “Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications,” Sensors, vol. 14, no. 2, pp. 3362– 3394, 2014.R. Abbasi-Kesbi, A. Nikfarjam, and H. Memarzadeh-Tehran, “A patientcentric sensory system for in-home rehabilitation,” IEEE Sensors J., vol. 17, no. 2, pp. 524–533, Jan. 2016.R. A. Bloomfield, M. C. Fennema, K. A. McIsaac, and M. G. Teeter, “Proposal and validation of a knee measurement system for patients with osteoarthritis,” IEEE Trans. Biomed. Eng., vol. 66, no. 2, pp. 319–326, Feb. 2019.P. B. Shull,W. Jirattigalachote,M. A. Hunt,M. R. Cutkosky, and S. L. Delp, “Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention,” Gait Posture, vol. 40, no. 1, pp. 11–19, 2014.T. Seel, J. Raisch, and T. Schauer, “IMU-based joint angle measurement for gait analysis,” Sensors, vol. 14, no. 4, pp. 6891–6909, 2014.T. McGrath, R. Fineman, and L. Stirling, “An auto-calibrating knee flexion-extension axis estimator using principal component analysis with inertial sensors,” Sensors, vol. 18, no. 6, p. 1882, 2018.E. Allseits et al., “A novel method for estimating knee angle using two leg-mounted gyroscopes for continuous monitoring with mobile health devices,” Sensors, vol. 18, no. 9, p. 2759, 2018.A. Tognetti, F. Lorussi, N. Carbonaro, and D. De Rossi, “Wearable goniometer and accelerometer sensory fusion for knee joint angle measurement in daily life,” Sensors, vol. 15, no. 11, pp. 28 435–28 455, 2015A. G. Leal-Junior et al., “POF-IMU sensor system: A fusion between inertial measurement units and POF sensors for low-cost and highly reliable systems,” Opt. Fiber Technol., vol. 43, pp. 82–89, 2018.I. Pasciuto et al., “How angular velocity features and different gyroscope noise types interact and determine orientation estimation accuracy,” Sensors, vol. 15, no. 9, pp. 23 983–24 001, 2015.E. Bergamini, G. Ligorio, A. Summa, G. Vannozzi, A. Cappozzo, and A. Sabatini, “Estimating orientation using magnetic and inertial sensors and different sensor fusion approaches: Accuracy assessment in manual and locomotion tasks,” Sensors, vol. 14, no. 10, pp. 18 625–18 649, 2014.C. L. Kendell and E. D. Lemaire, “Effect of mobility devices on orientation sensors that contain magnetometers,” Journal of Rehabilitation Research & Development, vol. 46, no. 7, pp. 957–963, 2009.D. Laidig, T. Schauer, and T. Seel, “Exploiting kinematic constraints to compensate magnetic disturbances when calculating joint angles of approximate hinge joints from orientation estimates of inertial sensors,” in Proc. Int. Conf. Rehabil. Robot., 2017, pp. 971–976.J. K. Lee and T. H. Jeon, “IMU-based but magnetometer-free joint angle estimation of constrained links,” in Proc. IEEE SENSORS, 2018, pp. 1–4A. G. Leal-Junior et al., “Polymer optical fiber sensors in wearable devices: Toward novel instrumentation approaches for gait assistance devices,” IEEE Sensors J., vol. 18, no. 17, pp. 7085–7092, Sep. 2018.M. Krehel, M. Wolf, L. F. Boesel, R. M. Rossi, G.-L. Bona, and L. J. Scherer, “Development of a luminous textile for reflective pulse oximetry measurements,” Biomed. Opt. Express, vol. 5, no. 8, pp. 2537–2547, 2014D. Lunni, G. Giordano, E. Sinibaldi, M. Cianchetti, and B. Mazzolai, “Shape estimation based on kalman filtering: Towards fully soft proprioception,” in Proc. IEEE Int. Conf. Soft Robot., 2018, pp. 541–546.L. Bilro, N. Alberto, J. L. Pinto, and R. Nogueira, “Optical sensors based on plastic fibers,” Sensors, vol. 12, no. 9, pp. 12 184–12 207, 2012.C. Massaroni, P. Saccomandi, and E. Schena, “Medical smart textiles based on fiber optic technology: an overview,” J. Functional Biomaterials, vol. 6, no. 2, pp. 204–221, 2015A. M. Sabatini, “Estimating three-dimensional orientation of human body parts by inertial/magnetic sensing,” Sensors, vol. 11, no. 2, pp. 1489–1525, 2011M. Ghobadi, P. Singla, and E. T. Esfahani, “Robust attitude estimation from uncertain observations of inertial sensors using covariance inflated multiplicative extended kalman filter,” IEEE Trans. Instrum. Meas., vol. 67, no. 1, pp. 209–217, Jan. 2017.H. G. Kortier, J. Antonsson, H. M. Schepers, F. Gustafsson, and P. H. Veltink, “Hand pose estimation by fusion of inertial and magnetic sensing aided by a permanent magnet,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 23, no. 5, pp. 796–806, Sep. 2015J. N. Gross, Y. Gu, M. B. Rhudy, S. Gururajan, and M. R. Napolitano, “Flight-test evaluation of sensor fusion algorithms for attitude estimation,” IEEE Trans. Aerosp. Electron. Syst., vol. 48, no. 3, pp. 2128–2139, Jul. 2012.L. Bilro, J. Oliveira, J. Pinto, and R. Nogueira, “A reliable low-cost wireless and wearable gait monitoring system based on a plastic optical fibre sensor,” Meas. Sci. Technol., vol. 22, no. 4, 2011, Art. no. 045801.A. G. Leal-Junior, A. Frizera, and M. J. Pontes, “Sensitive zone parameters and curvature radius evaluation for polymer optical fiber curvature sensors,” Opt. Laser Technol., vol. 100, pp. 272–281, 2018.J. Favre, B. Jolles, R. Aissaoui, and K. Aminian, “Ambulatory measurement of 3d knee joint angle,” J. 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Williams, “Lower limb angular velocity during walking at various speeds,” Gait Posture, vol. 65, pp. 190–196, 2018.info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbRehabilitación médicaMedical rehabilitationAparatos fisiológicosPhysiological apparatusFusión de datos multisensorMultisensor data fusionSensores inercialesÁngulos articularesMovimiento estimaciónsensores POFFusión de sensoresSistemas portátilesInertial sensorsJoint anglesMotion estimationPOF sensorsSensor fusionWearable systemsTEXTSleeve_for_Knee_Angle_Monitoring_An_IMU-POF_Sensor_Fusion_System.pdf.txtSleeve_for_Knee_Angle_Monitoring_An_IMU-POF_Sensor_Fusion_System.pdf.txtExtracted texttext/plain58574https://repositorio.escuelaing.edu.co/bitstream/001/3255/4/Sleeve_for_Knee_Angle_Monitoring_An_IMU-POF_Sensor_Fusion_System.pdf.txtccbc1214f0147432f3db4a46c987cb95MD54metadata only accessTHUMBNAILPortada Sleeve_for_Knee_Angle_Monitoring_An_IMU-POF_Sensor_Fusion_System.PNGPortada Sleeve_for_Knee_Angle_Monitoring_An_IMU-POF_Sensor_Fusion_System.PNGimage/png288408https://repositorio.escuelaing.edu.co/bitstream/001/3255/3/Portada%20Sleeve_for_Knee_Angle_Monitoring_An_IMU-POF_Sensor_Fusion_System.PNG95607ebe7fb9f4c5c20d3d6f8bf0a0efMD53open accessSleeve_for_Knee_Angle_Monitoring_An_IMU-POF_Sensor_Fusion_System.pdf.jpgSleeve_for_Knee_Angle_Monitoring_An_IMU-POF_Sensor_Fusion_System.pdf.jpgGenerated Thumbnailimage/jpeg20011https://repositorio.escuelaing.edu.co/bitstream/001/3255/5/Sleeve_for_Knee_Angle_Monitoring_An_IMU-POF_Sensor_Fusion_System.pdf.jpg8b1995f7c7a98ebd6ec09d97141b4cd9MD55metadata only accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81881https://repositorio.escuelaing.edu.co/bitstream/001/3255/2/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD52open accessORIGINALSleeve_for_Knee_Angle_Monitoring_An_IMU-POF_Sensor_Fusion_System.pdfSleeve_for_Knee_Angle_Monitoring_An_IMU-POF_Sensor_Fusion_System.pdfapplication/pdf1980285https://repositorio.escuelaing.edu.co/bitstream/001/3255/1/Sleeve_for_Knee_Angle_Monitoring_An_IMU-POF_Sensor_Fusion_System.pdf0af585c41e9c0ac7f59b8372fe5fd1b0MD51metadata only access001/3255oai:repositorio.escuelaing.edu.co:001/32552024-09-11 03:01:36.647metadata only accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.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

Sleeve for Knee Angle Monitoring: An IMU-POF Sensor Fusion System

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