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Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents
Open accessAbstract
Wearable acceleration sensors are increasingly used for the assessment of free-living physical activity. Acceleration sensor calibration is a potential source of error. This study aims to describe and evaluate an autocalibration method to minimize calibration error using segments within the free-living records (no extra experiments needed). The autocalibration method entailed the extraction of nonmovement periods in the data, for which the measured vector magnitude should ideally be the gravitational acceleration (1 g); this property was used to derive calibration correction factors using an iterative closest-point fitting process. The reduction in calibration error was evaluated in data from four cohorts: UK ( n = 921), Kuwait ( n = 120), Cameroon ( n = 311), and Brazil ( n = 200). Our method significantly reduced calibration error in all cohorts ( P < 0.01), ranging from 16.6 to 3.0 m g in the Kuwaiti cohort to 76.7 to 8.0 m g error in the Brazil cohort. Utilizing temperature sensor data resulted in a small nonsignificant additional improvement ( P > 0.05). Temperature correction coefficients were highest for the z-axis, e.g., 19.6-m g offset per 5°C. Further, application of the autocalibration method had a significant impact on typical metrics used for describing human physical activity, e.g., in Brazil average wrist acceleration was 0.2 to 51% lower than uncalibrated values depending on metric selection ( P < 0.01). The autocalibration method as presented helps reduce the calibration error in wearable acceleration sensor data and improves comparability of physical activity measures across study locations. Temperature ultization seems essential when temperature deviates substantially from the average temperature in the record but not for multiday summary measures.
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Cites
Validation of the GENEA Accelerometer
ESLIGER, D. W., ROWLANDS, A. V., HURST, T. L., CATT, M., MURRAY, P., & ESTON, R. G. (2011) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e31820513be · Cited by 625
Cited by
Doherty, A., Jackson, D., Hammerla, N., Plötz, T., Olivier, P., Granat, M. H., White, T., van Hees, V. T., Trenell, M. I., Owen, C. G., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0169649 · Cited by 1,250
Self-citationMigueles, J. H., Rowlands, A. V., Huber, F., Sabia, S., & van Hees, V. T. (2019) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2018-0063 · Cited by 955
A novel, open access method to assess sleep duration using a wrist-worn accelerometer
van Hees, V. T., Sabia, S., Anderson, K. N., Denton, S. J., Oliver, J., Catt, M., Abell, J. G., Kivimäki, M., Trenell, M. I., & Singh-Manoux, A. (2015) · PLOS ONE · doi:10.1371/journal.pone.0142533 · Cited by 744
Self-citationJones, S. E., Lane, J. M., Wood, A. R., van Hees, V. T., Tyrrell, J., Beaumont, R. N., Jeffries, A. R., Dashti, H. S., Hillsdon, M., Ruth, K. S., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08259-7 · Cited by 691
Evaluation of raw acceleration sedentary thresholds in children and adults
Hildebrand, M., Hansen, B. H., van Hees, V. T., & Ekelund, U. (2017) · Scandinavian Journal of Medicine & Science in Sports · doi:10.1111/sms.12795 · Cited by 425
Wearable-device-measured physical activity and future health risk
Strain, T., Wijndaele, K., Dempsey, P. C., Sharp, S. J., Pearce, M., Jeon, J., Lindsay, T., Wareham, N., & Brage, S. (2020) · Nature Medicine · doi:10.1038/s41591-020-1012-3 · Cited by 355
Self-citationGenetic studies of accelerometer-based sleep measures yield new insights into human sleep behaviour
Jones, S. E., van Hees, V. T., Mazzotti, D. R., Marques-Vidal, P., Sabia, S., van der Spek, A., Dashti, H. S., Engmann, J., Kocevska, D., Tyrrell, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09576-1 · Cited by 300
Willetts, M., Hollowell, S., Aslett, L., Holmes, C., & Doherty, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-26174-1 · Cited by 250
Jensen, S. B. K., Blond, M. B., Sandsdal, R. M., Olsen, L. M., Juhl, C. R., Lundgren, J. R., Janus, C., Stallknecht, B. M., Holst, J. J., Madsbad, S., et al. (2024) · eClinicalMedicine · doi:10.1016/j.eclinm.2024.102475 · Cited by 207
Accelerometer-assessed Physical Activity in Epidemiology: Are Monitors Equivalent?
ROWLANDS, A. V., MIRKES, E. M., YATES, T., CLEMES, S., DAVIES, M., KHUNTI, K., & EDWARDSON, C. L. (2018) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001435 · Cited by 187
White, T., Westgate, K., Wareham, N. J., & Brage, S. (2016) · PLOS ONE · doi:10.1371/journal.pone.0167472 · Cited by 169
Self-citationGenerating ActiGraph counts from raw acceleration recorded by an alternative monitor.
BRØND, J. C., ANDERSEN, L. B., & ARVIDSSON, D. (2017) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001344 · Cited by 166
Comparability and feasibility of wrist- and hip-worn accelerometers in free-living adolescents.
Scott, J. J., Rowlands, A. V., Cliff, D. P., Morgan, P. J., Plotnikoff, R. C., & Lubans, D. R. (2017) · Journal of Science and Medicine in Sport · doi:10.1016/j.jsams.2017.04.017 · Cited by 120
Rowlands, A. V. (2018) · Pediatric Exercise Science · doi:10.1123/pes.2018-0201 · Cited by 87
O'Brien, J. T., Gallagher, P., Stow, D., Hammerla, N., Ploetz, T., Firbank, M., Ladha, C., Ladha, K., Jackson, D., McNaney, R., et al. (2017) · Psychological Medicine · doi:10.1017/s0033291716002166 · Cited by 76
Kim, Y., White, T., Wijndaele, K., Sharp, S. J., Wareham, N. J., & Brage, S. (2017) · International Journal of Obesity · doi:10.1038/ijo.2017.122 · Cited by 66
Self-citationWearable accelerometer-derived physical activity and incident disease
Khurshid, S., Weng, L.-C., Nauffal, V., Pirruccello, J. P., Venn, R. A., Al-Alusi, M. A., Benjamin, E. J., Ellinor, P. T., & Lubitz, S. A. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00676-9 · Cited by 63
Chan, S., Hang, Y., Tong, C., Acquah, A., Schonfeldt, A., Gershuny, J., & Doherty, A. (2024) · Scientific Data · doi:10.1038/s41597-024-03960-3 · Cited by 61
IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition
Leng, Z., Bhattacharjee, A., Rajasekhar, H., Zhang, L., Bruda, E., Kwon, H., & Plötz, T. (2024) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3678545 · Cited by 61
Early postpartum physical activity and pelvic floor support and symptoms 1 year postpartum
Nygaard, I. E., Wolpern, A., Bardsley, T., Egger, M. J., & Shaw, J. M. (2021) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2020.08.033 · Cited by 48
Taylor, S. L., Noonan, R. J., Knowles, Z. R., Owen, M. B., McGrane, B., Curry, W. B., & Fairclough, S. J. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15051011 · Cited by 47
Jensen, S. B. K., Janus, C., Lundgren, J. R., Juhl, C. R., Sandsdal, R. M., Olsen, L. M., Andresen, A., Borg, S. A., Jacobsen, I. C., Finlayson, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32307-y · Cited by 42
Jin, X., Chen, Y., Feng, H., Zhou, M., Chan, J. W. Y., Liu, Y., Kong, A. P. S., Tan, X., Wing, Y.-K., Liang, Y. Y., et al. (2024) · Journal of Sport and Health Science · doi:10.1016/j.jshs.2023.03.001 · Cited by 42
Segmenting accelerometer data from daily life with unsupervised machine learning
van Kuppevelt, D., Heywood, J., Hamer, M., Sabia, S., Fitzsimons, E., & van Hees, V. (2019) · PLOS ONE · doi:10.1371/journal.pone.0208692 · Cited by 41
Self-citationMeredith-Jones, K. A., Haszard, J. J., Graham-DeMello, A., Campbell, A., Stewart, T., Galland, B. C., Cox, A., Kennedy, G., Duncan, S., & Taylor, R. W. (2024) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-024-01590-x · Cited by 32
COOMBES, J. S., KEATING, S. E., MIELKE, G. I., FASSETT, R. G., COOMBES, B. K., O’LEARY, K. P., COX, E. R., & BURTON, N. W. (2022) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002768 · Cited by 32
Shelley, J., Fairclough, S. J., Knowles, Z. R., Southern, K. W., McCormack, P., Dawson, E. A., Graves, L. E. F., & Hanlon, C. (2018) · BMC Pediatrics · doi:10.1186/s12887-018-1301-x · Cited by 31
Maylor, B. D., Edwardson, C. L., Dempsey, P. C., Patterson, M. R., Plekhanova, T., Yates, T., & Rowlands, A. V. (2022) · Sensors · doi:10.3390/s22249984 · Cited by 30
Surveillance of Youth Physical Activity and Sedentary Behavior with Wrist Accelerometry
Kim, Y., Hibbing, P., Saint-Maurice, P. F., Ellingson, L. D., Hennessy, E., Wolff-Hughes, D. L., Perna, F. M., & Welk, G. J. (2017) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2017.01.012 · Cited by 29
Bannell, D. J., France-Ratcliffe, M., Buckley, B. J. R., Crozier, A., Davies, A. P., Hesketh, K. L., Jones, H., Cocks, M., Sprung, V. S., & on behalf of the MOTIVATE Team (2023) · DIGITAL HEALTH · doi:10.1177/20552076231183552 · Cited by 25
Mielke, G. I., de Almeida Mendes, M., Ekelund, U., Rowlands, A. V., Reichert, F. F., & Crochemore‐Silva, I. (2023) · Scandinavian Journal of Medicine & Science in Sports · doi:10.1111/sms.14416 · Cited by 26
“…estimates produced in different continents may be subject to calibration error as a potential source of bias, despite its small magnitude, 10 calibration studies to compare acceleration outputs between device-brands and derived intensity thresholds are particularly important in locations…”
BackgroundSelf-citationMigueles, J. H., Molina-Garcia, P., Torres-Lopez, L. V., Cadenas-Sanchez, C., Rowlands, A. V., Ebner-Priemer, U. W., Koch, E. D., Reif, A., & Ortega, F. B. (2022) · Scientific Reports · doi:10.1038/s41598-022-09469-2 · Cited by 22
Physical activity, inactivity and sleep during the Diabetes Remission Clinical Trial (DiRECT)
Cassidy, S., Trenell, M., Stefanetti, R. J., Charman, S. J., Barnes, A. C., Brosnahan, N., McCombie, L., Thom, G., Peters, C., Zhyzhneuskaya, S., et al. (2023) · Diabetic Medicine · doi:10.1111/dme.15010 · Cited by 19
Self-citationDashti, H. S., Jones, S. E., Wood, A. R., Lane, J. M., van Hees, V. T., Wang, H., Rhodes, J. A., Song, Y., Patel, K., Anderson, S. G., et al. (2018) · doi:10.1101/274977 · Cited by 614
Self-citationJones, S. E., Hees, V. T. V., Mazzotti, D. R., Marques-Vidal, P., Sabia, S., der Spek, A. V., Dashti, H. S., Engmann, J., Kocevska, D., Tyrrell, J., et al. (2018) · doi:10.1101/303925 · Cited by 14
Self-citationIf only we had more data!: Sensor-Based Human Activity Recognition in Challenging Scenarios
Plötz, T. (2023) · 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) · doi:10.1109/percomworkshops56833.2023.10150267 · Cited by 16
Frequency-Aware Masked Autoencoders for Human Activity Recognition using Accelerometers
Lorenzen, N. R., Jennum, P. J., Mignot, E., & Brink-Kjaer, A. (2025) · 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11252605 · Cited by 2
Metadata
- Authors
- van Hees, V. T., Fang, Z., Langford, J., Assah, F., Mohammad, A., da Silva, I. C. M., Trenell, M. I., White, T., Wareham, N. J., & Brage, S.
- Venue
- Journal of Applied Physiology
- Publisher
- American Physiological Society
- Published
- 2014-10-01
- Volume
- 117
- Issue
- 7
- Pages
- 738-744
- ISSN
- 0161-7567 (ISSN-L)
- 8750-7587
- 1522-1601
- OpenAlex
- W2109784184
- Cited by
- 676
- References
- 1
- FWCI
- 13.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
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