Improved Gait Speed Calculation via Modulation Spectral Analysis of Noisy Accelerometer Data

Chronic diseases among older adults carry a heavy burden on a country's healthcare system and economy. As such, there is a critical need for the development of cost-effective, technology-based tools that can be scaled to meet the needs of older adults. Gait speed, for example, is an important p...

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Fecha de publicación:: 2021

Institución:: Universidad de Medellín

Repositorio:: Repositorio UDEM

Idioma:: eng

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network_acronym_str	REPOUDEM2
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repository_id_str
dc.title.none.fl_str_mv	Improved Gait Speed Calculation via Modulation Spectral Analysis of Noisy Accelerometer Data
title	Improved Gait Speed Calculation via Modulation Spectral Analysis of Noisy Accelerometer Data
spellingShingle	Improved Gait Speed Calculation via Modulation Spectral Analysis of Noisy Accelerometer Data Accelerometer gait speed modulation spectrum telehealth wearables Accelerometers Clinical research Cost effectiveness Costs Data handling Kinematics Modulation Spectrum analysis Wearable technology Accelerometer data Clinical practices Error variability Health-care system Home application Modulation domains Modulation spectral analysis Technology-based Speed
title_short	Improved Gait Speed Calculation via Modulation Spectral Analysis of Noisy Accelerometer Data
title_full	Improved Gait Speed Calculation via Modulation Spectral Analysis of Noisy Accelerometer Data
title_fullStr	Improved Gait Speed Calculation via Modulation Spectral Analysis of Noisy Accelerometer Data
title_full_unstemmed	Improved Gait Speed Calculation via Modulation Spectral Analysis of Noisy Accelerometer Data
title_sort	Improved Gait Speed Calculation via Modulation Spectral Analysis of Noisy Accelerometer Data
dc.subject.spa.fl_str_mv	Accelerometer gait speed modulation spectrum telehealth wearables
topic	Accelerometer gait speed modulation spectrum telehealth wearables Accelerometers Clinical research Cost effectiveness Costs Data handling Kinematics Modulation Spectrum analysis Wearable technology Accelerometer data Clinical practices Error variability Health-care system Home application Modulation domains Modulation spectral analysis Technology-based Speed
dc.subject.keyword.eng.fl_str_mv	Accelerometers Clinical research Cost effectiveness Costs Data handling Kinematics Modulation Spectrum analysis Wearable technology Accelerometer data Clinical practices Error variability Health-care system Home application Modulation domains Modulation spectral analysis Technology-based Speed
description	Chronic diseases among older adults carry a heavy burden on a country's healthcare system and economy. As such, there is a critical need for the development of cost-effective, technology-based tools that can be scaled to meet the needs of older adults. Gait speed, for example, is an important predictor of change in functional status and health outcomes in older adults. There is no universally accepted method for measuring gait speed in clinical practice and research, and differences in methods may influence the observed associations between gait speed and health. Moreover, existing methods are sensitive to artifacts, which are present in burgeoning low-cost wearable devices. To overcome this limitation, this paper proposes an artifact-robust gait speed calculation method using spectrooral signal processing of accelerometer data. To this end, a new so-called modulation domain gait speed (MD-GS) metric is proposed and tested on data collected from forty older adults performing a 400-meter walk test with a sensor placed on a waist-worn belt. Average gait speed calculation is performed for each participant. Experimental results showed the proposed method achieved very high correlation ( ρ =0.98 ) with ground truth gait speeds, as well as low errors and error variability (0.05±0.14) m/s, thus substantially outperforming gait speed calculation using a well-known kinematic model. The increased robustness against artifacts, make it a promising solution for aging-in-home applications based on low-cost wearable devices. © 2001-2012 IEEE.
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-02-05T14:57:33Z
dc.date.available.none.fl_str_mv	2021-02-05T14:57:33Z
dc.date.none.fl_str_mv	2021
dc.type.eng.fl_str_mv	Article
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dc.identifier.issn.none.fl_str_mv	1530437X
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/11407/5887
dc.identifier.doi.none.fl_str_mv	10.1109/JSEN.2020.3013996
identifier_str_mv	1530437X 10.1109/JSEN.2020.3013996
url	http://hdl.handle.net/11407/5887
dc.language.iso.none.fl_str_mv	eng
language	eng
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dc.relation.citationvolume.none.fl_str_mv	21
dc.relation.citationissue.none.fl_str_mv	1
dc.relation.citationstartpage.none.fl_str_mv	520
dc.relation.citationendpage.none.fl_str_mv	528
dc.relation.references.none.fl_str_mv	Colby, S.L., (2015) Projections of the Size and Composition of the Us Population: 2014 to 2060, , Current Population, United States Census Bureau, Suitland-Silver Hill, MD, USA, Tech. Rep. P25-1143 Bloom, D.E., Mitgang, E., Osher, B., (2016) Demography of Global Aging, , https://ssrn.com/abstract=2834214, Accessed: Jan. 5, 2020 Hung, W.W., Ross, J.S., Boockvar, K.S., Siu, A.L., Recent trends in chronic disease, impairment and disability among older adults in the United States (2011) Bmc Geriatrics, 11 (1), p. 47. , Dec Centers for Disease Control and Prevention and others The State of Aging and Health in America 2013, Centers for Disease Control Prevention, Atlanta, GA, USA, 2013 Reeder, B., Whitehouse, K., Sensor-based detection of gait speed in older adults: An integrative review (2015) Res. Gerontological Nursing, 8 (1), pp. 12-27. , Jan Lord, S.E., Weatherall, M., Rochester, L., Community ambulation in older adults: Which internal characteristics are important? (2010) Arch. Phys. Med. Rehabil., 91 (3), pp. 378-383 Prohaska, T.R., Anderson, L.A., Hooker, S.P., Hughes, S.L., Belza, B., Mobility and aging: Transference to transportation (2011) J. Aging Res., 2011. , Aug Webber, S.C., Porter, M.M., Menec, V.H., Mobility in older adults: A comprehensive framework (2010) Gerontologist, 50 (4), pp. 443-450. , Aug Peel, N.M., Kuys, S.S., Klein, K., Gait speed as a measure in geriatric assessment in clinical settings: A systematic review (2013) J. Gerontol., Ser. A, 68 (1), pp. 39-46. , Jan Studenski, S., Bradypedia: Is gait speed ready for clinical use? (2009) J. Nutrition, Health Aging, 13 (10), pp. 878-880. , Dec Fritz, S., Lusardi, M., Walking speed: The sixth vital sign (2009) J. Geriatric Phys. Therapy, 32 (2), pp. 2-5 Kan Van, G.Abellan, Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an international academy on nutrition and aging (IANA) task force (2009) J. Nutrition, Health Aging, 13 (10), pp. 881-889. , Dec Cesari, M., Prognostic value of usual gait speed in wellfunctioning older people, Results from the health, aging and body composition study (2005) J. Amer. Geriatrics Soc., 53 (10), pp. 1675-1680 Studenski, S., Gait speed and survival in older adults (2011) J. Amer. Med. Assoc., 305 (1), pp. 50-58 Tao, W., Liu, T., Zheng, R., Feng, H., Gait analysis using wearable sensors (2012) Sensors, 12 (2), pp. 2255-2283. , Feb Rueterbories, J., Spaich, E.G., Larsen, B., Andersen, O.K., Methods for gait event detection and analysis in ambulatory systems (2010) Med. Eng. Phys., 32 (6), pp. 545-552. , Jul Rampp, A., Barth, J., Schuelein, S., Gassmann, K.-G., Klucken, J., Eskofier, B.M., Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients (2015) IEEE Trans. Biomed. Eng., 62 (4), pp. 1089-1097. , Apr Sabatini, A.M., Martelloni, C., Scapellato, S., Cavallo, F., Assessment of walking features from foot inertial sensing (2005) IEEE Trans. Biomed. Eng., 52 (3), pp. 486-494. , Mar Zijlstra, W., Hof, A.L., Assessment of spatio-temporal gait parameters from trunk accelerations during human walking (2003) Gait Posture, 18 (2), pp. 1-10. , Oct Vathsangam, H., Emken, A., Spruijt-Metz, D., Sukhatme, G.S., Toward free-living walking speed estimation using Gaussian processbased regression with on-body accelerometers and gyroscopes (2010) Proc. 4th Int. Icst Conf. Pervas. Comput. Technol. Healthcare, pp. 1-8 Hannink, J., Kautz, T., Pasluosta, C.F., Gasmann, K.-G., Klucken, J., Eskofier, B.M., Sensor-based gait parameter extraction with deep convolutional neural networks (2017) IEEE J. Biomed. Health Informat., 21 (1), pp. 85-93. , Jan Alotaibi, M., Mahmood, A., Improved gait recognition based on specialized deep convolutional neural network (2017) Comput. Vis. Image Understand., 164, pp. 103-110. , Nov Gadaleta, M., Rossi, M., IDNet: Smartphone-based gait recognition with convolutional neural networks (2018) Pattern Recognit., 74, pp. 25-37. , Feb Khandelwal, S., Wickström, N., Identification of gait events using expert knowledge and continuous wavelet transform analysis (2014) Proc. 7th Int. Conf. Bio-Inspired Syst. Signal Process., Angers, France, pp. 197-204. , Mar Williamson, J., Data sensing and analysis: Challenges for wearables (2015) Proc. 20th Asia South Pacific Design Autom. Conf., pp. 136-141. , Jan Patel, S., Park, H., Bonato, P., Chan, L., Rodgers, M., A review of wearable sensors and systems with application in rehabilitation (2012) J. NeuroEng. Rehabil., 9 (1), p. 21 Rolland, Y.M., Cesari, M., Miller, M.E., Penninx, B.W., Atkinson, H.H., Pahor, M., Reliability of the 400-m usual-pace walk test as an assessment of mobility limitation in older adults (2004) J. Amer. Geriatrics Soc., 52 (6), pp. 972-976. , Jun Cassani, R., Falk, T.H., Spectrotemporal modeling of biomedical signals: Theoretical foundation and applications (2019) Encyclopedia of Biomedical Engineering, pp. 144-163. , R. Narayan, Ed. Oxford, U.K.: Elsevier Falk, T.H., Sejdic, E., Chau, T., Chan, W.-Y., (2010) Spectro-Temporal Analysis Auscultatory Sounds, , London, U.K.: INTECH Open Access Falk, T.H., Stadler, S., Kleijn, W.B., Chan, W.-Y., Noise suppression based on extending a speech-dominated modulation band (2007) Proc. Interspeech, pp. 970-973 Tobon, D.P.V., Falk, T.H., Maier, M., MS-QI: A modulation spectrum-based ECG quality index for telehealth applications (2016) IEEE Trans. Biomed. Eng., 63 (8), pp. 1613-1622. , Aug Tobon, D.P., Falk, T.H., Adaptive spectro-temporal filtering for electrocardiogram signal enhancement (2018) IEEE J. Biomed. Health Informat., 22 (2), pp. 421-428. , Mar Vestergaard, S., Patel, K.V., Bandinelli, S., Ferrucci, L., Guralnik, J.M., Characteristics of 400-meter walk test performance and subsequent mortality in older adults (2009) Rejuvenation Res., 12 (3), pp. 177-184. , Jun Atlas, L., Clark, P., Schimmel, S., (2010) Modulation Toolbox Version 2.1 for Matlab, , Seattle, WA, USA: Univ. Washington Del Din, S., Godfrey, A., Rochester, L., Validation of an accelerometer to quantify a comprehensive battery of gait characteristics in healthy older adults and Parkinson's disease: Toward clinical and at home use (2016) IEEE J. Biomed. Health Informat., 20 (3), pp. 838-847. , May Moe-Nilssen, R., A new method for evaluating motor control in gait under real-life environmental conditions. Part 1: The instrument (1998) Clin. Biomechan., 13 (4-5), pp. 320-327. , Jun McCamley, J., Donati, M., Grimpampi, E., Mazzá, C., An enhanced estimate of initial contact and final contact instants of time using lower trunk inertial sensor data (2012) Gait Posture, 36 (2), pp. 316-318. , Jun Winter, D.A., (2009) Biomechanics Motor Control Human Movement, , Hoboken, NJ, USA: Wiley Khandelwal, S., Wickström, N., Evaluation of the performance of accelerometer-based gait event detection algorithms in different realworld scenarios using the MAREA gait database (2017) Gait Posture, 51, pp. 84-90. , Jan
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_16ec
rights_invalid_str_mv	http://purl.org/coar/access_right/c_16ec
dc.publisher.none.fl_str_mv	Institute of Electrical and Electronics Engineers Inc.
dc.publisher.program.spa.fl_str_mv	Ingeniería de Telecomunicaciones
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingenierías
publisher.none.fl_str_mv	Institute of Electrical and Electronics Engineers Inc.
dc.source.none.fl_str_mv	IEEE Sensors Journal
institution	Universidad de Medellín
repository.name.fl_str_mv	Repositorio Institucional Universidad de Medellin
repository.mail.fl_str_mv	repositorio@udem.edu.co
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spelling	20212021-02-05T14:57:33Z2021-02-05T14:57:33Z1530437Xhttp://hdl.handle.net/11407/588710.1109/JSEN.2020.3013996Chronic diseases among older adults carry a heavy burden on a country's healthcare system and economy. As such, there is a critical need for the development of cost-effective, technology-based tools that can be scaled to meet the needs of older adults. Gait speed, for example, is an important predictor of change in functional status and health outcomes in older adults. There is no universally accepted method for measuring gait speed in clinical practice and research, and differences in methods may influence the observed associations between gait speed and health. Moreover, existing methods are sensitive to artifacts, which are present in burgeoning low-cost wearable devices. To overcome this limitation, this paper proposes an artifact-robust gait speed calculation method using spectrooral signal processing of accelerometer data. To this end, a new so-called modulation domain gait speed (MD-GS) metric is proposed and tested on data collected from forty older adults performing a 400-meter walk test with a sensor placed on a waist-worn belt. Average gait speed calculation is performed for each participant. Experimental results showed the proposed method achieved very high correlation ( ρ =0.98 ) with ground truth gait speeds, as well as low errors and error variability (0.05±0.14) m/s, thus substantially outperforming gait speed calculation using a well-known kinematic model. The increased robustness against artifacts, make it a promising solution for aging-in-home applications based on low-cost wearable devices. © 2001-2012 IEEE.engInstitute of Electrical and Electronics Engineers Inc.Ingeniería de TelecomunicacionesFacultad de Ingenieríashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097781983&doi=10.1109%2fJSEN.2020.3013996&partnerID=40&md5=49e78fed974c013cdf44bcac5de09ee0211520528Colby, S.L., (2015) Projections of the Size and Composition of the Us Population: 2014 to 2060, , Current Population, United States Census Bureau, Suitland-Silver Hill, MD, USA, Tech. Rep. P25-1143Bloom, D.E., Mitgang, E., Osher, B., (2016) Demography of Global Aging, , https://ssrn.com/abstract=2834214, Accessed: Jan. 5, 2020Hung, W.W., Ross, J.S., Boockvar, K.S., Siu, A.L., Recent trends in chronic disease, impairment and disability among older adults in the United States (2011) Bmc Geriatrics, 11 (1), p. 47. , DecCenters for Disease Control and Prevention and others The State of Aging and Health in America 2013, Centers for Disease Control Prevention, Atlanta, GA, USA, 2013Reeder, B., Whitehouse, K., Sensor-based detection of gait speed in older adults: An integrative review (2015) Res. Gerontological Nursing, 8 (1), pp. 12-27. , JanLord, S.E., Weatherall, M., Rochester, L., Community ambulation in older adults: Which internal characteristics are important? (2010) Arch. Phys. Med. Rehabil., 91 (3), pp. 378-383Prohaska, T.R., Anderson, L.A., Hooker, S.P., Hughes, S.L., Belza, B., Mobility and aging: Transference to transportation (2011) J. Aging Res., 2011. , AugWebber, S.C., Porter, M.M., Menec, V.H., Mobility in older adults: A comprehensive framework (2010) Gerontologist, 50 (4), pp. 443-450. , AugPeel, N.M., Kuys, S.S., Klein, K., Gait speed as a measure in geriatric assessment in clinical settings: A systematic review (2013) J. Gerontol., Ser. A, 68 (1), pp. 39-46. , JanStudenski, S., Bradypedia: Is gait speed ready for clinical use? (2009) J. Nutrition, Health Aging, 13 (10), pp. 878-880. , DecFritz, S., Lusardi, M., Walking speed: The sixth vital sign (2009) J. Geriatric Phys. Therapy, 32 (2), pp. 2-5Kan Van, G.Abellan, Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an international academy on nutrition and aging (IANA) task force (2009) J. Nutrition, Health Aging, 13 (10), pp. 881-889. , DecCesari, M., Prognostic value of usual gait speed in wellfunctioning older people, Results from the health, aging and body composition study (2005) J. Amer. Geriatrics Soc., 53 (10), pp. 1675-1680Studenski, S., Gait speed and survival in older adults (2011) J. Amer. Med. Assoc., 305 (1), pp. 50-58Tao, W., Liu, T., Zheng, R., Feng, H., Gait analysis using wearable sensors (2012) Sensors, 12 (2), pp. 2255-2283. , FebRueterbories, J., Spaich, E.G., Larsen, B., Andersen, O.K., Methods for gait event detection and analysis in ambulatory systems (2010) Med. Eng. Phys., 32 (6), pp. 545-552. , JulRampp, A., Barth, J., Schuelein, S., Gassmann, K.-G., Klucken, J., Eskofier, B.M., Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients (2015) IEEE Trans. Biomed. Eng., 62 (4), pp. 1089-1097. , AprSabatini, A.M., Martelloni, C., Scapellato, S., Cavallo, F., Assessment of walking features from foot inertial sensing (2005) IEEE Trans. Biomed. Eng., 52 (3), pp. 486-494. , MarZijlstra, W., Hof, A.L., Assessment of spatio-temporal gait parameters from trunk accelerations during human walking (2003) Gait Posture, 18 (2), pp. 1-10. , OctVathsangam, H., Emken, A., Spruijt-Metz, D., Sukhatme, G.S., Toward free-living walking speed estimation using Gaussian processbased regression with on-body accelerometers and gyroscopes (2010) Proc. 4th Int. Icst Conf. Pervas. Comput. Technol. Healthcare, pp. 1-8Hannink, J., Kautz, T., Pasluosta, C.F., Gasmann, K.-G., Klucken, J., Eskofier, B.M., Sensor-based gait parameter extraction with deep convolutional neural networks (2017) IEEE J. Biomed. Health Informat., 21 (1), pp. 85-93. , JanAlotaibi, M., Mahmood, A., Improved gait recognition based on specialized deep convolutional neural network (2017) Comput. Vis. Image Understand., 164, pp. 103-110. , NovGadaleta, M., Rossi, M., IDNet: Smartphone-based gait recognition with convolutional neural networks (2018) Pattern Recognit., 74, pp. 25-37. , FebKhandelwal, S., Wickström, N., Identification of gait events using expert knowledge and continuous wavelet transform analysis (2014) Proc. 7th Int. Conf. Bio-Inspired Syst. Signal Process., Angers, France, pp. 197-204. , MarWilliamson, J., Data sensing and analysis: Challenges for wearables (2015) Proc. 20th Asia South Pacific Design Autom. Conf., pp. 136-141. , JanPatel, S., Park, H., Bonato, P., Chan, L., Rodgers, M., A review of wearable sensors and systems with application in rehabilitation (2012) J. NeuroEng. Rehabil., 9 (1), p. 21Rolland, Y.M., Cesari, M., Miller, M.E., Penninx, B.W., Atkinson, H.H., Pahor, M., Reliability of the 400-m usual-pace walk test as an assessment of mobility limitation in older adults (2004) J. Amer. Geriatrics Soc., 52 (6), pp. 972-976. , JunCassani, R., Falk, T.H., Spectrotemporal modeling of biomedical signals: Theoretical foundation and applications (2019) Encyclopedia of Biomedical Engineering, pp. 144-163. , R. Narayan, Ed. Oxford, U.K.: ElsevierFalk, T.H., Sejdic, E., Chau, T., Chan, W.-Y., (2010) Spectro-Temporal Analysis Auscultatory Sounds, , London, U.K.: INTECH Open AccessFalk, T.H., Stadler, S., Kleijn, W.B., Chan, W.-Y., Noise suppression based on extending a speech-dominated modulation band (2007) Proc. Interspeech, pp. 970-973Tobon, D.P.V., Falk, T.H., Maier, M., MS-QI: A modulation spectrum-based ECG quality index for telehealth applications (2016) IEEE Trans. Biomed. Eng., 63 (8), pp. 1613-1622. , AugTobon, D.P., Falk, T.H., Adaptive spectro-temporal filtering for electrocardiogram signal enhancement (2018) IEEE J. Biomed. Health Informat., 22 (2), pp. 421-428. , MarVestergaard, S., Patel, K.V., Bandinelli, S., Ferrucci, L., Guralnik, J.M., Characteristics of 400-meter walk test performance and subsequent mortality in older adults (2009) Rejuvenation Res., 12 (3), pp. 177-184. , JunAtlas, L., Clark, P., Schimmel, S., (2010) Modulation Toolbox Version 2.1 for Matlab, , Seattle, WA, USA: Univ. WashingtonDel Din, S., Godfrey, A., Rochester, L., Validation of an accelerometer to quantify a comprehensive battery of gait characteristics in healthy older adults and Parkinson's disease: Toward clinical and at home use (2016) IEEE J. Biomed. Health Informat., 20 (3), pp. 838-847. , MayMoe-Nilssen, R., A new method for evaluating motor control in gait under real-life environmental conditions. Part 1: The instrument (1998) Clin. Biomechan., 13 (4-5), pp. 320-327. , JunMcCamley, J., Donati, M., Grimpampi, E., Mazzá, C., An enhanced estimate of initial contact and final contact instants of time using lower trunk inertial sensor data (2012) Gait Posture, 36 (2), pp. 316-318. , JunWinter, D.A., (2009) Biomechanics Motor Control Human Movement, , Hoboken, NJ, USA: WileyKhandelwal, S., Wickström, N., Evaluation of the performance of accelerometer-based gait event detection algorithms in different realworld scenarios using the MAREA gait database (2017) Gait Posture, 51, pp. 84-90. , JanIEEE Sensors JournalAccelerometergait speedmodulation spectrumtelehealthwearablesAccelerometersClinical researchCost effectivenessCostsData handlingKinematicsModulationSpectrum analysisWearable technologyAccelerometer dataClinical practicesError variabilityHealth-care systemHome applicationModulation domainsModulation spectral analysisTechnology-basedSpeedImproved Gait Speed Calculation via Modulation Spectral Analysis of Noisy Accelerometer DataArticleinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Tobon V., D.P., Telecommunications and Electronic Engineering Department, Universidad de Medellín, Medellín, 050026, ColombiaGarudadri, H., Qualcomm Institute, University of California San Diego (UCSD), San Diego, CA 92093, United StatesGodino, J.G., Family Medicine and Public Health, University of California San Diego (UCSD), San Diego, CA 92093, United StatesGodbole, S., Family Medicine and Public Health, University of California San Diego (UCSD), San Diego, CA 92093, United StatesPatrick, K., Family Medicine and Public Health, University of California San Diego (UCSD), San Diego, CA 92093, United StatesFalk, T.H., INRS-EMT, University of Quebec, Montreál, QC H2L 2C4, Canadahttp://purl.org/coar/access_right/c_16ecTobon V. D.P.Garudadri H.Godino J.G.Godbole S.Patrick K.Falk T.H.11407/5887oai:repository.udem.edu.co:11407/58872021-02-05 09:57:33.62Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

Improved Gait Speed Calculation via Modulation Spectral Analysis of Noisy Accelerometer Data

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