Online System for Gait Parameters Estimation Using a LRF Sensor for Assistive Devices

Recent implementations of sensory systems have addressed gait characterization in several assistive, rehabilitation and human-robot interaction scenarios. Sensors such as laser rangefinders, force platforms and motion tracking systems have been widely used to achieve legs’ position tracking, as well...

Full description

Autores:: Aguirre, Andrés
Sierra M., Sergio D.
Múnera, Marcela
Cifuentes, Carlos A.

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	Online System for Gait Parameters Estimation Using a LRF Sensor for Assistive Devices
title	Online System for Gait Parameters Estimation Using a LRF Sensor for Assistive Devices
spellingShingle	Online System for Gait Parameters Estimation Using a LRF Sensor for Assistive Devices Rehabilitación médica Medical rehabilitation Robótica médica Robotics in medicine Aparatos fisiológicos Physiological apparatus Marcha asistida por un andador Interacción humano-robot Telémetro láser Sensores ambulatorios Espacio-temporal Parámetros de la marcha Walker-assisted gait Human-robot interaction Laser rangefinder Ambulatory sensors Spatio-temporal gait parameters
title_short	Online System for Gait Parameters Estimation Using a LRF Sensor for Assistive Devices
title_full	Online System for Gait Parameters Estimation Using a LRF Sensor for Assistive Devices
title_fullStr	Online System for Gait Parameters Estimation Using a LRF Sensor for Assistive Devices
title_full_unstemmed	Online System for Gait Parameters Estimation Using a LRF Sensor for Assistive Devices
title_sort	Online System for Gait Parameters Estimation Using a LRF Sensor for Assistive Devices
dc.creator.fl_str_mv	Aguirre, Andrés Sierra M., Sergio D. Múnera, Marcela Cifuentes, Carlos A.
dc.contributor.author.none.fl_str_mv	Aguirre, Andrés Sierra M., Sergio D. Múnera, Marcela Cifuentes, Carlos A.
dc.contributor.researchgroup.spa.fl_str_mv	GiBiome
dc.subject.armarc.none.fl_str_mv	Rehabilitación médica Medical rehabilitation Robótica médica Robotics in medicine Aparatos fisiológicos Physiological apparatus
topic	Rehabilitación médica Medical rehabilitation Robótica médica Robotics in medicine Aparatos fisiológicos Physiological apparatus Marcha asistida por un andador Interacción humano-robot Telémetro láser Sensores ambulatorios Espacio-temporal Parámetros de la marcha Walker-assisted gait Human-robot interaction Laser rangefinder Ambulatory sensors Spatio-temporal gait parameters
dc.subject.proposal.spa.fl_str_mv	Marcha asistida por un andador Interacción humano-robot Telémetro láser Sensores ambulatorios Espacio-temporal Parámetros de la marcha
dc.subject.proposal.eng.fl_str_mv	Walker-assisted gait Human-robot interaction Laser rangefinder Ambulatory sensors Spatio-temporal gait parameters
description	Recent implementations of sensory systems have addressed gait characterization in several assistive, rehabilitation and human-robot interaction scenarios. Sensors such as laser rangefinders, force platforms and motion tracking systems have been widely used to achieve legs’ position tracking, as well as to estimate gait spatio-temporal parameters. However, the validation of those measurements with a gold standard system is still lacking. In this sense, this work is aimed at proposing an online system for the estimation of gait parameters for walker-assisted gait with smart or robotic devices. Moreover, a validation study with an optoelectronic system was carried out. A group of 30 healthy volunteers was recruited. The trials were performed on a treadmill, where the subjects were asked to walk at 4 different speeds. The proposed system is equipped with a laser rangefinder to calculate the users’ legs position. Additionally, two adaptive filters, as well as a linear mathematical model were used to adjust the estimations of the users’ gait parameters. Results show that our proposed system is able to estimate the stride cadence and the step length with an error lower than 5% compared with the gold standard system.
publishDate	2021
dc.date.issued.none.fl_str_mv	2021
dc.date.accessioned.none.fl_str_mv	2024-10-04T17:18:32Z
dc.date.available.none.fl_str_mv	2024-10-04T17:18:32Z
dc.type.spa.fl_str_mv	Artículo de revista
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dc.relation.citationedition.spa.fl_str_mv	Vol. 21 No. 13, 2021
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dc.relation.citationissue.spa.fl_str_mv	13
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dc.relation.references.spa.fl_str_mv	J. Marín, T. Blanco, J. J. Marín, A. Moreno, E. Martitegui, and J. C. Arages, “Integrating a gait analysis test in hospital rehabilitation: A service design approach,” PLoS ONE, vol. 14, no. 10, Oct. 2019, Art. no. e0224409 M. W. Whittle, “Clinical gait analysis: A review,” Human Movement Sci., vol. 15, pp. 369–387, 1996. H. Stolze et al., “Gait analysis during treadmill and overground locomotion in children and adults,” Electroencephalogr. Clin. Neurophysiol./Electromyography Motor Control, vol. 105, no. 6, pp. 490–497, Dec. 1997 C. A. Cifuentes and A. Frizera, Human-Robot Interact. Strategies for Walker-Assisted Locomotion, vol. 115. Cham, Switzerland: Springer, 2016, [Online]. Available: http://link.springer.com/10.1007/978-3-319- 34063- J. Casas, N. Cespedes, M. Múnera, and C. A. Cifuentes, “Human-robot interaction for rehabilitation scenarios,” in Control Systems Design of Bio-Robotics and Bio-mechatronics with Advanced Applications. Amsterdam, The Netherlands: Elsevier, 2020, pp. 1–31. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/B9780128174630000010 S. D. S. Sierra M., M. Garzón, M. Mánera, and C. A. Cifuentes, “Human–robot–environment interaction interface for smart walker assisted gait: AGoRA walker,” Sensors, vol. 19, no. 13, p. 2897, Jun. 2019. W. M. Scheidegger et al., “A novel multimodal cognitive interaction for walker-assisted rehabilitation therapies,” in Proc. IEEE 16th Int. Conf. Rehabil. Robot. (ICORR), Jun. 2019, pp. 905–910. [Online]. Available: https://ieeexplore.ieee.org/document/8779469/ J. Ballesteros, C. Urdiales, A. B. Martinez, and M. Tirado, “Online estimation of rollator user condition using spatiotemporal gait parameters,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), Oct. 2016, pp. 3180–3185. C. A. Cifuentes, C. Rodriguez, A. Frizera, and T. Bastos, “Sensor fusion to control a robotic walker based on upper-limbs reaction forces and gait kinematics,” in Proc. 5th IEEE RAS/EMBS Int. Conf. Biomed. Robot. Biomechatron., Aug. 2014, pp. 1098–1103. 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, Feb. 2014. A. Ferrari, P. Ginis, M. Hardegger, F. Casamassima, L. Rocchi, and L. Chiari, “A mobile Kalman-filter based solution for the real-time estimation of spatio-temporal_newline gait parameters,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 24, no. 7, pp. 764–773, Jul. 2016. J. Rueterbories, E. G. Spaich, B. Larsen, and O. K. Andersen, “Methods for gait event detection and analysis in ambulatory systems,” Med. Eng. Phys., vol. 32, no. 6, pp. 545–552, Jul. 2010, doi: 10.1016/j.medengphy.2010.03.007. J. Taborri, E. Palermo, S. Rossi, and P. Cappa, “Gait partitioning methods: A systematic review,” Sensors, vol. 16, pp. 1–5, Dec. 2016. Y. Qi, C. B. Soh, E. Gunawan, K.-S. Low, and R. Thomas, “Assessment of foot trajectory for human gait phase detection using wireless ultrasonic sensor network,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 24, no. 1, pp. 88–97, Jan. 2016. R. Caldas, M. Mundt, W. Potthast, F. Buarque de Lima Neto, and B. Markert, “A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms,” Gait Posture, vol. 57, pp. 204–210, Sep. 2017. M. D. S. Sánchez Manchola, M. J. P. Pinto Bernal, M. Munera, and C. A. Cifuentes, “Gait phase detection for lower-limb exoskeletons using foot motion data from a single inertial measurement unit in hemiparetic individuals,” Sensors, vol. 19, no. 13, p. 2988, Jul. 2019. N.-H. Ho, P. Truong, and G.-M. Jeong, “Step-detection and adaptive step-length estimation for pedestrian dead-reckoning at various walking speeds using a smartphone,” Sensors, vol. 16, no. 9, p. 1423, Sep. 2016 M. Brodie et al., “Wearable pendant device monitoring using new wavelet-based methods shows daily life and laboratory gaits are different,” Med. Biol. Eng, vol. 54, pp. 663–674, Dec. 2016 M. Iwai et al., “The validity of spatiotemporal gait analysis using dual laser range sensors: A cross-sectional study,” Arch. Physiotherapy, vol. 9, no. 1, Dec. 2019. S. Fudickar, C. Stolle, N. Volkening, and A. Hein, “Scanning laser rangefinders for the unobtrusive monitoring of gait parameters in unsupervised settings,” Sensors, vol. 18, no. 10, p. 3424, Oct. 2018. [Online]. Available: http://www.mdpi.com/1424-8220/18/10/3424 Hokuyo Automatic CO. Scanning Rangefinder Distance Data URG04LX-UG01 Product Details. Accessed: Feb. 3, 2020. [Online]. Available: https://www.hokuyo-aut.jp/search/single.php?serial=166 B. Bioengineering. SMART-DX\|Motion Capture System. Accessed: Feb. 3, 2020. [Online]. Available: http://www.btsbioengineering.com/ products/smart-dx/ R. B. Davis, S. Õunpuu, D. Tyburski, and J. R. Gage, “A gait analysis data collection and reduction technique,” Hum. Movement Sci., vol. 10, no. 5, pp. 575–587, Oct. 1991. N. Sekiya, H. Nagasaki, and H. F. Ito Taketo, “The invariant relationship between step length and step rate during free walking,” J. Hum. Movement Stud., vol. 30, no. 6, pp. 241–257, 1996 W. T. Latt, U.-X. Tan, K. C. Veluvolu, C. Y. Shee, and W. T. Ang, “Real-time estimation and prediction of periodic signals from attenuated and phase-shifted sensed signals,” in Proc. IEEE/ASME Int. Conf. Adv. Intell. Mechatronics, Jul. 2009, pp. 1643–1648. [Online]. Available: http://ieeexplore.ieee.org/document/5229825/ V. Bonnet, C. Mazzá, J. McCamley, and A. Cappozzo, “Use of weighted Fourier linear combiner filters to estimate lower trunk 3D orientation from gyroscope sensors data,” J. NeuroEng. Rehabil., vol. 10, no. 1, p. 29, 2013. [Online]. Available: http://jneuroengrehab.biomedcentral. com/articles/10.1186/1743-0003-10-2%9 V. Bonnet, C. Mazzà, K. McCamley, and J. A. Cappozzo, “Use of weighted Fourier linear combiner filters to gyroscope sensors data,” J. Neuroeng. Rehabil., vol. 10, no. 1, p. 29, 2013. J. Gallego, E. Rocon, J. O. Roa, J. Moreno, and J. L. Pons, “Realtime estimation of pathological tremor parameters from gyroscope data,” Sensors, vol. 10, no. 3, pp. 2129–2149, Mar. 2010 A. F. Neto, J. A. Gallego, E. Rocon, J. L. Pons, and R. Ceres, “Extraction of user ’s navigation commands from upper body force interaction in walker assisted gait,” Biomed. Eng. OnLine, vol. 8, pp. 1–16, Oct. 2010. J. F. Item-Glatthorn, N. C. Casartelli, and N. A. Maffiuletti, “Reproducibility of gait parameters at different surface inclinations and speeds using an instrumented treadmill system,” Gait Posture, vol. 44, pp. 259–264, Feb. 2016. T. Pallej, M. Teixidó, M. Tresanchez, and J. Palacín, “Measuring gait using a ground laser range sensor,” Sensors, vol. 9, no. 11, pp. 9133–9146, Nov. 2009. http://www.pubmedcentral.nih.gov/ articlerender.fcgi?artid=3260635&to%ol=pmcentrez&rendertype= abstract M. Teixidó, T. Pallejâ, M. Tresanchez, M. Nogués, and J. Palacín, “Measuring oscillating walking paths with a LIDAR,” Sensors, vol. 11, no. 5, pp. 5071–5086, May 2011. F. Alton, L. Baldey, S. Caplan, and M. C. Morrissey, “A kinematic comparison of overground and treadmill walking,” Clin. Biomechanics, vol. 13, no. 6, pp. 434–440, Sep. 1998.
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spelling	Aguirre, Andrésb7c23972f4b7842df2d2f60ffa18a65eSierra M., Sergio D.737e72bd7de516ba4165724b71283ae0Múnera, Marcela8047a30ff2499f8ae5a4e903871b8f95Cifuentes, Carlos A.0b885a45437175ae12e5d0a6f598afc4GiBiome2024-10-04T17:18:32Z2024-10-04T17:18:32Z20211558-1748https://repositorio.escuelaing.edu.co/handle/001/32961558-1748Universidad Escuela Colombiana de Ingeniería Julio GaravitoRepositorio Digitalhttps://repositorio.escuelaing.edu.co/Recent implementations of sensory systems have addressed gait characterization in several assistive, rehabilitation and human-robot interaction scenarios. Sensors such as laser rangefinders, force platforms and motion tracking systems have been widely used to achieve legs’ position tracking, as well as to estimate gait spatio-temporal parameters. However, the validation of those measurements with a gold standard system is still lacking. In this sense, this work is aimed at proposing an online system for the estimation of gait parameters for walker-assisted gait with smart or robotic devices. Moreover, a validation study with an optoelectronic system was carried out. A group of 30 healthy volunteers was recruited. The trials were performed on a treadmill, where the subjects were asked to walk at 4 different speeds. The proposed system is equipped with a laser rangefinder to calculate the users’ legs position. Additionally, two adaptive filters, as well as a linear mathematical model were used to adjust the estimations of the users’ gait parameters. Results show that our proposed system is able to estimate the stride cadence and the step length with an error lower than 5% compared with the gold standard system.Implementaciones recientes de sistemas sensoriales. han abordado la caracterización de la marcha en varios servicios de asistencia, Escenarios de rehabilitación e interacción hombre-robot. Sensores como telémetros láser, plataformas de fuerza y movimiento. Los sistemas de seguimiento se han utilizado ampliamente para lograr las piernas. seguimiento de la posición, así como para estimar la marcha espacio-temporal parámetros. Sin embargo, la validación de esas mediciones con un sistema de patrón oro todavía falta. En este sentido, Este trabajo tiene como objetivo proponer un sistema en línea para la estimación de los parámetros de la marcha para la marcha asistida por andador con Dispositivos inteligentes o robóticos. Además, un estudio de validación con un Se realizó un sistema optoelectrónico. Un grupo de 30 sanos Se reclutaron voluntarios. Los ensayos se realizaron en un cinta rodante, donde se pidió a los sujetos que caminaran a 4 velocidades diferentes. El sistema propuesto está equipado con un láser. Telémetro para calcular la posición de las piernas de los usuarios. Además, dos filtros adaptativos, así como un modelo matemático lineal. se utilizaron para ajustar las estimaciones de los parámetros de la marcha de los usuarios. Los resultados muestran que nuestro sistema propuesto es capaz de estimar la cadencia de zancada y la longitud del paso con un error inferior al 5% en comparación con el sistema estándar de oro.9 páginasapplication/pdfengIEEE Sensors Councils.lhttps://www.ieee.org/publications/rights/index.htmlOnline System for Gait Parameters Estimation Using a LRF Sensor for Assistive DevicesArtículo 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. 21 No. 13, 202114280131427221IEEE SENSORS JOURNALJ. Marín, T. Blanco, J. J. Marín, A. Moreno, E. Martitegui, and J. C. Arages, “Integrating a gait analysis test in hospital rehabilitation: A service design approach,” PLoS ONE, vol. 14, no. 10, Oct. 2019, Art. no. e0224409M. W. Whittle, “Clinical gait analysis: A review,” Human Movement Sci., vol. 15, pp. 369–387, 1996.H. Stolze et al., “Gait analysis during treadmill and overground locomotion in children and adults,” Electroencephalogr. Clin. Neurophysiol./Electromyography Motor Control, vol. 105, no. 6, pp. 490–497, Dec. 1997C. A. Cifuentes and A. Frizera, Human-Robot Interact. Strategies for Walker-Assisted Locomotion, vol. 115. Cham, Switzerland: Springer, 2016, [Online]. Available: http://link.springer.com/10.1007/978-3-319- 34063-J. Casas, N. Cespedes, M. Múnera, and C. A. Cifuentes, “Human-robot interaction for rehabilitation scenarios,” in Control Systems Design of Bio-Robotics and Bio-mechatronics with Advanced Applications. Amsterdam, The Netherlands: Elsevier, 2020, pp. 1–31. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/B9780128174630000010S. D. S. Sierra M., M. Garzón, M. Mánera, and C. A. Cifuentes, “Human–robot–environment interaction interface for smart walker assisted gait: AGoRA walker,” Sensors, vol. 19, no. 13, p. 2897, Jun. 2019.W. M. Scheidegger et al., “A novel multimodal cognitive interaction for walker-assisted rehabilitation therapies,” in Proc. IEEE 16th Int. Conf. Rehabil. Robot. (ICORR), Jun. 2019, pp. 905–910. [Online]. Available: https://ieeexplore.ieee.org/document/8779469/J. Ballesteros, C. Urdiales, A. B. Martinez, and M. Tirado, “Online estimation of rollator user condition using spatiotemporal gait parameters,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), Oct. 2016, pp. 3180–3185.C. A. Cifuentes, C. Rodriguez, A. Frizera, and T. Bastos, “Sensor fusion to control a robotic walker based on upper-limbs reaction forces and gait kinematics,” in Proc. 5th IEEE RAS/EMBS Int. Conf. Biomed. Robot. Biomechatron., Aug. 2014, pp. 1098–1103.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, Feb. 2014.A. Ferrari, P. Ginis, M. Hardegger, F. Casamassima, L. Rocchi, and L. Chiari, “A mobile Kalman-filter based solution for the real-time estimation of spatio-temporal_newline gait parameters,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 24, no. 7, pp. 764–773, Jul. 2016.J. Rueterbories, E. G. Spaich, B. Larsen, and O. K. Andersen, “Methods for gait event detection and analysis in ambulatory systems,” Med. Eng. Phys., vol. 32, no. 6, pp. 545–552, Jul. 2010, doi: 10.1016/j.medengphy.2010.03.007.J. Taborri, E. Palermo, S. Rossi, and P. Cappa, “Gait partitioning methods: A systematic review,” Sensors, vol. 16, pp. 1–5, Dec. 2016.Y. Qi, C. B. Soh, E. Gunawan, K.-S. Low, and R. Thomas, “Assessment of foot trajectory for human gait phase detection using wireless ultrasonic sensor network,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 24, no. 1, pp. 88–97, Jan. 2016.R. Caldas, M. Mundt, W. Potthast, F. Buarque de Lima Neto, and B. Markert, “A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms,” Gait Posture, vol. 57, pp. 204–210, Sep. 2017.M. D. S. Sánchez Manchola, M. J. P. Pinto Bernal, M. Munera, and C. A. Cifuentes, “Gait phase detection for lower-limb exoskeletons using foot motion data from a single inertial measurement unit in hemiparetic individuals,” Sensors, vol. 19, no. 13, p. 2988, Jul. 2019.N.-H. Ho, P. Truong, and G.-M. Jeong, “Step-detection and adaptive step-length estimation for pedestrian dead-reckoning at various walking speeds using a smartphone,” Sensors, vol. 16, no. 9, p. 1423, Sep. 2016M. Brodie et al., “Wearable pendant device monitoring using new wavelet-based methods shows daily life and laboratory gaits are different,” Med. Biol. Eng, vol. 54, pp. 663–674, Dec. 2016M. Iwai et al., “The validity of spatiotemporal gait analysis using dual laser range sensors: A cross-sectional study,” Arch. Physiotherapy, vol. 9, no. 1, Dec. 2019.S. Fudickar, C. Stolle, N. Volkening, and A. Hein, “Scanning laser rangefinders for the unobtrusive monitoring of gait parameters in unsupervised settings,” Sensors, vol. 18, no. 10, p. 3424, Oct. 2018. [Online]. Available: http://www.mdpi.com/1424-8220/18/10/3424Hokuyo Automatic CO. Scanning Rangefinder Distance Data URG04LX-UG01 Product Details. Accessed: Feb. 3, 2020. [Online]. Available: https://www.hokuyo-aut.jp/search/single.php?serial=166B. Bioengineering. SMART-DX\|Motion Capture System. Accessed: Feb. 3, 2020. [Online]. Available: http://www.btsbioengineering.com/ products/smart-dx/R. B. Davis, S. Õunpuu, D. Tyburski, and J. R. Gage, “A gait analysis data collection and reduction technique,” Hum. Movement Sci., vol. 10, no. 5, pp. 575–587, Oct. 1991.N. Sekiya, H. Nagasaki, and H. F. Ito Taketo, “The invariant relationship between step length and step rate during free walking,” J. Hum. Movement Stud., vol. 30, no. 6, pp. 241–257, 1996W. T. Latt, U.-X. Tan, K. C. Veluvolu, C. Y. Shee, and W. T. Ang, “Real-time estimation and prediction of periodic signals from attenuated and phase-shifted sensed signals,” in Proc. IEEE/ASME Int. Conf. Adv. Intell. Mechatronics, Jul. 2009, pp. 1643–1648. [Online]. Available: http://ieeexplore.ieee.org/document/5229825/V. Bonnet, C. Mazzá, J. McCamley, and A. Cappozzo, “Use of weighted Fourier linear combiner filters to estimate lower trunk 3D orientation from gyroscope sensors data,” J. NeuroEng. Rehabil., vol. 10, no. 1, p. 29, 2013. [Online]. Available: http://jneuroengrehab.biomedcentral. com/articles/10.1186/1743-0003-10-2%9V. Bonnet, C. Mazzà, K. McCamley, and J. A. 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Biomechanics, vol. 13, no. 6, pp. 434–440, Sep. 1998.info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbRehabilitación médicaMedical rehabilitationRobótica médicaRobotics in medicineAparatos fisiológicosPhysiological apparatusMarcha asistida por un andadorInteracción humano-robotTelémetro láserSensores ambulatoriosEspacio-temporal Parámetros de la marchaWalker-assisted gaitHuman-robot interactionLaser rangefinderAmbulatory sensorsSpatio-temporal gait parametersTEXTOnline_System_for_Gait_Parameters_Estimation_Using_a_LRF_Sensor_for_Assistive_Devices (1).pdf.txtOnline_System_for_Gait_Parameters_Estimation_Using_a_LRF_Sensor_for_Assistive_Devices (1).pdf.txtExtracted texttext/plain48491https://repositorio.escuelaing.edu.co/bitstream/001/3296/4/Online_System_for_Gait_Parameters_Estimation_Using_a_LRF_Sensor_for_Assistive_Devices%20%281%29.pdf.txt53e85aa0c0f28f6c13e04b7856d0c6b5MD54metadata only accessTHUMBNAILPortada Online_System_for_Gait_Parameters_Estimation_Using_a_LRF_Sensor_for_Assistive_Devices (1).PNGPortada Online_System_for_Gait_Parameters_Estimation_Using_a_LRF_Sensor_for_Assistive_Devices (1).PNGimage/png195367https://repositorio.escuelaing.edu.co/bitstream/001/3296/3/Portada%20Online_System_for_Gait_Parameters_Estimation_Using_a_LRF_Sensor_for_Assistive_Devices%20%281%29.PNG95a2483e2be64f15b38892a816098baaMD53open accessOnline_System_for_Gait_Parameters_Estimation_Using_a_LRF_Sensor_for_Assistive_Devices (1).pdf.jpgOnline_System_for_Gait_Parameters_Estimation_Using_a_LRF_Sensor_for_Assistive_Devices (1).pdf.jpgGenerated Thumbnailimage/jpeg20210https://repositorio.escuelaing.edu.co/bitstream/001/3296/5/Online_System_for_Gait_Parameters_Estimation_Using_a_LRF_Sensor_for_Assistive_Devices%20%281%29.pdf.jpga9d8a08b73ea633f3dcc5ca1646de0dfMD55metadata only accessLICENSElicense.txtlicense.txttext/plain; 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Online System for Gait Parameters Estimation Using a LRF Sensor for Assistive Devices

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