Accelerometry Data in Health Research: Challenges and Opportunities

Abstract Wearable accelerometers provide detailed, objective, and continuous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popularity of wearable technology in health research. An ever-increasing number of...
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Autor*in:	Karas, Marta [verfasserIn] Bai, Jiawei Strączkiewicz, Marcin Harezlak, Jaroslaw Glynn, Nancy W. Harris, Tamara Zipunnikov, Vadim Crainiceanu, Ciprian Urbanek, Jacek K.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Wearable computing Accelerometry Wearable accelerometers Physical activity Accelerometers

Anmerkung:	© International Chinese Statistical Association 2019

Übergeordnetes Werk:	Enthalten in: Statistics in biosciences - New York, NY : Springer, 2009, 11(2019), 2 vom: 12. Jan., Seite 210-237
Übergeordnetes Werk:	volume:11 ; year:2019 ; number:2 ; day:12 ; month:01 ; pages:210-237

Links:	Volltext

DOI / URN:	10.1007/s12561-018-9227-2

Katalog-ID:	SPR026593637

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520			\|a Abstract Wearable accelerometers provide detailed, objective, and continuous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popularity of wearable technology in health research. An ever-increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper, we discuss problems related to the collection and analysis of raw accelerometry data and refer to published solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability, and the effects of sensor location on the body. We also discuss challenges related to sampling frequency, device calibration, data labeling, and multiple PA monitors synchronization. We illustrate these points using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment.
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allfields	10.1007/s12561-018-9227-2 doi (DE-627)SPR026593637 (SPR)s12561-018-9227-2-e DE-627 ger DE-627 rakwb eng Karas, Marta verfasserin (orcid)0000-0001-5889-3970 aut Accelerometry Data in Health Research: Challenges and Opportunities 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Chinese Statistical Association 2019 Abstract Wearable accelerometers provide detailed, objective, and continuous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popularity of wearable technology in health research. An ever-increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper, we discuss problems related to the collection and analysis of raw accelerometry data and refer to published solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability, and the effects of sensor location on the body. We also discuss challenges related to sampling frequency, device calibration, data labeling, and multiple PA monitors synchronization. We illustrate these points using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment. Wearable computing (dpeaa)DE-He213 Accelerometry (dpeaa)DE-He213 Wearable accelerometers (dpeaa)DE-He213 Physical activity (dpeaa)DE-He213 Accelerometers (dpeaa)DE-He213 Bai, Jiawei aut Strączkiewicz, Marcin aut Harezlak, Jaroslaw aut Glynn, Nancy W. aut Harris, Tamara aut Zipunnikov, Vadim aut Crainiceanu, Ciprian aut Urbanek, Jacek K. aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 11(2019), 2 vom: 12. Jan., Seite 210-237 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:11 year:2019 number:2 day:12 month:01 pages:210-237 https://dx.doi.org/10.1007/s12561-018-9227-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2019 2 12 01 210-237
spelling	10.1007/s12561-018-9227-2 doi (DE-627)SPR026593637 (SPR)s12561-018-9227-2-e DE-627 ger DE-627 rakwb eng Karas, Marta verfasserin (orcid)0000-0001-5889-3970 aut Accelerometry Data in Health Research: Challenges and Opportunities 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Chinese Statistical Association 2019 Abstract Wearable accelerometers provide detailed, objective, and continuous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popularity of wearable technology in health research. An ever-increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper, we discuss problems related to the collection and analysis of raw accelerometry data and refer to published solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability, and the effects of sensor location on the body. We also discuss challenges related to sampling frequency, device calibration, data labeling, and multiple PA monitors synchronization. We illustrate these points using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment. Wearable computing (dpeaa)DE-He213 Accelerometry (dpeaa)DE-He213 Wearable accelerometers (dpeaa)DE-He213 Physical activity (dpeaa)DE-He213 Accelerometers (dpeaa)DE-He213 Bai, Jiawei aut Strączkiewicz, Marcin aut Harezlak, Jaroslaw aut Glynn, Nancy W. aut Harris, Tamara aut Zipunnikov, Vadim aut Crainiceanu, Ciprian aut Urbanek, Jacek K. aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 11(2019), 2 vom: 12. Jan., Seite 210-237 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:11 year:2019 number:2 day:12 month:01 pages:210-237 https://dx.doi.org/10.1007/s12561-018-9227-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2019 2 12 01 210-237
allfields_unstemmed	10.1007/s12561-018-9227-2 doi (DE-627)SPR026593637 (SPR)s12561-018-9227-2-e DE-627 ger DE-627 rakwb eng Karas, Marta verfasserin (orcid)0000-0001-5889-3970 aut Accelerometry Data in Health Research: Challenges and Opportunities 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Chinese Statistical Association 2019 Abstract Wearable accelerometers provide detailed, objective, and continuous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popularity of wearable technology in health research. An ever-increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper, we discuss problems related to the collection and analysis of raw accelerometry data and refer to published solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability, and the effects of sensor location on the body. We also discuss challenges related to sampling frequency, device calibration, data labeling, and multiple PA monitors synchronization. We illustrate these points using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment. Wearable computing (dpeaa)DE-He213 Accelerometry (dpeaa)DE-He213 Wearable accelerometers (dpeaa)DE-He213 Physical activity (dpeaa)DE-He213 Accelerometers (dpeaa)DE-He213 Bai, Jiawei aut Strączkiewicz, Marcin aut Harezlak, Jaroslaw aut Glynn, Nancy W. aut Harris, Tamara aut Zipunnikov, Vadim aut Crainiceanu, Ciprian aut Urbanek, Jacek K. aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 11(2019), 2 vom: 12. Jan., Seite 210-237 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:11 year:2019 number:2 day:12 month:01 pages:210-237 https://dx.doi.org/10.1007/s12561-018-9227-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2019 2 12 01 210-237
allfieldsGer	10.1007/s12561-018-9227-2 doi (DE-627)SPR026593637 (SPR)s12561-018-9227-2-e DE-627 ger DE-627 rakwb eng Karas, Marta verfasserin (orcid)0000-0001-5889-3970 aut Accelerometry Data in Health Research: Challenges and Opportunities 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Chinese Statistical Association 2019 Abstract Wearable accelerometers provide detailed, objective, and continuous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popularity of wearable technology in health research. An ever-increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper, we discuss problems related to the collection and analysis of raw accelerometry data and refer to published solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability, and the effects of sensor location on the body. We also discuss challenges related to sampling frequency, device calibration, data labeling, and multiple PA monitors synchronization. We illustrate these points using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment. Wearable computing (dpeaa)DE-He213 Accelerometry (dpeaa)DE-He213 Wearable accelerometers (dpeaa)DE-He213 Physical activity (dpeaa)DE-He213 Accelerometers (dpeaa)DE-He213 Bai, Jiawei aut Strączkiewicz, Marcin aut Harezlak, Jaroslaw aut Glynn, Nancy W. aut Harris, Tamara aut Zipunnikov, Vadim aut Crainiceanu, Ciprian aut Urbanek, Jacek K. aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 11(2019), 2 vom: 12. Jan., Seite 210-237 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:11 year:2019 number:2 day:12 month:01 pages:210-237 https://dx.doi.org/10.1007/s12561-018-9227-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2019 2 12 01 210-237
allfieldsSound	10.1007/s12561-018-9227-2 doi (DE-627)SPR026593637 (SPR)s12561-018-9227-2-e DE-627 ger DE-627 rakwb eng Karas, Marta verfasserin (orcid)0000-0001-5889-3970 aut Accelerometry Data in Health Research: Challenges and Opportunities 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Chinese Statistical Association 2019 Abstract Wearable accelerometers provide detailed, objective, and continuous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popularity of wearable technology in health research. An ever-increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper, we discuss problems related to the collection and analysis of raw accelerometry data and refer to published solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability, and the effects of sensor location on the body. We also discuss challenges related to sampling frequency, device calibration, data labeling, and multiple PA monitors synchronization. We illustrate these points using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment. Wearable computing (dpeaa)DE-He213 Accelerometry (dpeaa)DE-He213 Wearable accelerometers (dpeaa)DE-He213 Physical activity (dpeaa)DE-He213 Accelerometers (dpeaa)DE-He213 Bai, Jiawei aut Strączkiewicz, Marcin aut Harezlak, Jaroslaw aut Glynn, Nancy W. aut Harris, Tamara aut Zipunnikov, Vadim aut Crainiceanu, Ciprian aut Urbanek, Jacek K. aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 11(2019), 2 vom: 12. Jan., Seite 210-237 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:11 year:2019 number:2 day:12 month:01 pages:210-237 https://dx.doi.org/10.1007/s12561-018-9227-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2019 2 12 01 210-237
language	English
source	Enthalten in Statistics in biosciences 11(2019), 2 vom: 12. Jan., Seite 210-237 volume:11 year:2019 number:2 day:12 month:01 pages:210-237
sourceStr	Enthalten in Statistics in biosciences 11(2019), 2 vom: 12. Jan., Seite 210-237 volume:11 year:2019 number:2 day:12 month:01 pages:210-237
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Wearable computing Accelerometry Wearable accelerometers Physical activity Accelerometers
isfreeaccess_bool	false
container_title	Statistics in biosciences
authorswithroles_txt_mv	Karas, Marta @@aut@@ Bai, Jiawei @@aut@@ Strączkiewicz, Marcin @@aut@@ Harezlak, Jaroslaw @@aut@@ Glynn, Nancy W. @@aut@@ Harris, Tamara @@aut@@ Zipunnikov, Vadim @@aut@@ Crainiceanu, Ciprian @@aut@@ Urbanek, Jacek K. @@aut@@
publishDateDaySort_date	2019-01-12T00:00:00Z
hierarchy_top_id	601009983
id	SPR026593637
language_de	englisch
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author	Karas, Marta
spellingShingle	Karas, Marta misc Wearable computing misc Accelerometry misc Wearable accelerometers misc Physical activity misc Accelerometers Accelerometry Data in Health Research: Challenges and Opportunities
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topic_title	Accelerometry Data in Health Research: Challenges and Opportunities Wearable computing (dpeaa)DE-He213 Accelerometry (dpeaa)DE-He213 Wearable accelerometers (dpeaa)DE-He213 Physical activity (dpeaa)DE-He213 Accelerometers (dpeaa)DE-He213
topic	misc Wearable computing misc Accelerometry misc Wearable accelerometers misc Physical activity misc Accelerometers
topic_unstemmed	misc Wearable computing misc Accelerometry misc Wearable accelerometers misc Physical activity misc Accelerometers
topic_browse	misc Wearable computing misc Accelerometry misc Wearable accelerometers misc Physical activity misc Accelerometers
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title	Accelerometry Data in Health Research: Challenges and Opportunities
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title_full	Accelerometry Data in Health Research: Challenges and Opportunities
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author_browse	Karas, Marta Bai, Jiawei Strączkiewicz, Marcin Harezlak, Jaroslaw Glynn, Nancy W. Harris, Tamara Zipunnikov, Vadim Crainiceanu, Ciprian Urbanek, Jacek K.
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title_sort	accelerometry data in health research: challenges and opportunities
title_auth	Accelerometry Data in Health Research: Challenges and Opportunities
abstract	Abstract Wearable accelerometers provide detailed, objective, and continuous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popularity of wearable technology in health research. An ever-increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper, we discuss problems related to the collection and analysis of raw accelerometry data and refer to published solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability, and the effects of sensor location on the body. We also discuss challenges related to sampling frequency, device calibration, data labeling, and multiple PA monitors synchronization. We illustrate these points using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment. © International Chinese Statistical Association 2019
abstractGer	Abstract Wearable accelerometers provide detailed, objective, and continuous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popularity of wearable technology in health research. An ever-increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper, we discuss problems related to the collection and analysis of raw accelerometry data and refer to published solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability, and the effects of sensor location on the body. We also discuss challenges related to sampling frequency, device calibration, data labeling, and multiple PA monitors synchronization. We illustrate these points using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment. © International Chinese Statistical Association 2019
abstract_unstemmed	Abstract Wearable accelerometers provide detailed, objective, and continuous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popularity of wearable technology in health research. An ever-increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper, we discuss problems related to the collection and analysis of raw accelerometry data and refer to published solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability, and the effects of sensor location on the body. We also discuss challenges related to sampling frequency, device calibration, data labeling, and multiple PA monitors synchronization. We illustrate these points using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment. © International Chinese Statistical Association 2019
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title_short	Accelerometry Data in Health Research: Challenges and Opportunities
url	https://dx.doi.org/10.1007/s12561-018-9227-2
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author2	Bai, Jiawei Strączkiewicz, Marcin Harezlak, Jaroslaw Glynn, Nancy W. Harris, Tamara Zipunnikov, Vadim Crainiceanu, Ciprian Urbanek, Jacek K.
author2Str	Bai, Jiawei Strączkiewicz, Marcin Harezlak, Jaroslaw Glynn, Nancy W. Harris, Tamara Zipunnikov, Vadim Crainiceanu, Ciprian Urbanek, Jacek K.
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doi_str	10.1007/s12561-018-9227-2
up_date	2024-07-03T21:44:25.427Z
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