{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T20:05:04Z","timestamp":1777320304453,"version":"3.51.4"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000050","name":"U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["U01HL145386"],"award-info":[{"award-number":["U01HL145386"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["U01HL145386"],"award-info":[{"award-number":["U01HL145386"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["U01HL145386"],"award-info":[{"award-number":["U01HL145386"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"U.S. Department of Health & Human Services | NIH | National Institute of Mental Health","doi-asserted-by":"publisher","award":["R37MH119194"],"award-info":[{"award-number":["R37MH119194"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"U.S. Department of Health & Human Services | NIH | National Institute of Mental Health","doi-asserted-by":"publisher","award":["R37MH119194"],"award-info":[{"award-number":["R37MH119194"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The ubiquity of personal digital devices offers unprecedented opportunities to study human behavior. Current state-of-the-art methods quantify physical activity using \u201cactivity counts,\u201d a measure which overlooks specific types of physical activities. We propose a walking recognition method for sub-second tri-axial accelerometer data, in which activity classification is based on the inherent features of walking: intensity, periodicity, and duration. We validate our method against 20 publicly available, annotated datasets on walking activity data collected at various body locations (thigh, waist, chest, arm, wrist). We demonstrate that our method can estimate walking periods with high sensitivity and specificity: average sensitivity ranged between 0.92 and 0.97 across various body locations, and average specificity for common daily activities was typically above 0.95. We also assess the method\u2019s algorithmic fairness to demographic and anthropometric variables and measurement contexts (body location, environment). Finally, we release our method as open-source software in Python and MATLAB.<\/jats:p>","DOI":"10.1038\/s41746-022-00745-z","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T11:03:18Z","timestamp":1677150198000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["A \u201cone-size-fits-most\u201d walking recognition method for smartphones, smartwatches, and wearable accelerometers"],"prefix":"10.1038","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8703-4451","authenticated-orcid":false,"given":"Marcin","family":"Straczkiewicz","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1964-5231","authenticated-orcid":false,"given":"Emily J.","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Jukka-Pekka","family":"Onnela","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"745_CR1","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1038\/s41746-021-00514-4","volume":"4","author":"M Straczkiewicz","year":"2021","unstructured":"Straczkiewicz, M., James, P. & Onnela, J.-P. A systematic review of smartphone-based human activity recognition methods for health research. npj Digit. Med. 4, 148 (2021).","journal-title":"npj Digit. Med."},{"key":"745_CR2","doi-asserted-by":"crossref","unstructured":"Karas, M. et al. Accelerometry data in health research: challenges and opportunities: review and examples. Stat. Biosci. 11, 210\u2013237 (2019).","DOI":"10.1007\/s12561-018-9227-2"},{"key":"745_CR3","doi-asserted-by":"crossref","unstructured":"Migueles, J. H. et al. Calibration and cross-validation of accelerometer cut-points to classify sedentary time and physical activity from hip and non-dominant and dominant wrists in older adults. Sensors. 21, 3326 (2021).","DOI":"10.3390\/s21103326"},{"key":"745_CR4","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.1007\/s40279-017-0716-0","volume":"47","author":"JH Migueles","year":"2017","unstructured":"Migueles, J. H. et al. Accelerometer data collection and processing criteria to assess physical activity and other outcomes: a systematic review and practical considerations. Sports Med. 47, 1821\u20131845 (2017).","journal-title":"Sports Med."},{"key":"745_CR5","doi-asserted-by":"publisher","first-page":"2569","DOI":"10.1080\/02640414.2020.1794244","volume":"38","author":"AHK Montoye","year":"2020","unstructured":"Montoye, A. H. K. et al. Development of cut-points for determining activity intensity from a wrist-worn ActiGraph accelerometer in free-living adults. J. Sports Sci. 38, 2569\u20132578 (2020).","journal-title":"J. Sports Sci."},{"key":"745_CR6","doi-asserted-by":"publisher","first-page":"e181","DOI":"10.1111\/apa.12129","volume":"102","author":"OG Jenni","year":"2013","unstructured":"Jenni, O. G., Chaouch, A., Caflisch, J. & Rousson, V. Infant motor milestones: poor predictive value for outcome of healthy children. Acta Paediatr. 102, e181\u2013e184 (2013).","journal-title":"Acta Paediatr."},{"key":"745_CR7","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1161\/ATVBAHA.112.300878","volume":"33","author":"PT Williams","year":"2013","unstructured":"Williams, P. T. & Thompson, P. D. Walking versus running for hypertension, cholesterol, and diabetes mellitus risk reduction. Arterioscler. Thromb. Vasc. Biol. 33, 1085\u20131091 (2013).","journal-title":"Arterioscler. Thromb. Vasc. Biol."},{"key":"745_CR8","doi-asserted-by":"publisher","first-page":"710 LP","DOI":"10.1136\/bjsports-2014-094157","volume":"49","author":"S Hanson","year":"2015","unstructured":"Hanson, S. & Jones, A. Is there evidence that walking groups have health benefits? A systematic review and meta-analysis. Br. J. Sports Med. 49, 710 LP\u2013710715 (2015).","journal-title":"Br. J. Sports Med."},{"key":"745_CR9","doi-asserted-by":"publisher","first-page":"1703","DOI":"10.1001\/archinte.161.14.1703","volume":"161","author":"K Yaffe","year":"2001","unstructured":"Yaffe, K., Barnes, D., Nevitt, M., Lui, L. Y. & Covinsky, K. A prospective study of physical activity and cognitive decline in elderly women: women who walk. Arch. Intern. Med. 161, 1703\u20131708 (2001).","journal-title":"Arch. Intern. Med."},{"key":"745_CR10","doi-asserted-by":"publisher","first-page":"1695","DOI":"10.1001\/archinte.158.15.1695","volume":"158","author":"MA Pereira","year":"1998","unstructured":"Pereira, M. A. et al. A randomized walking trial in postmenopausal women: effects on physical activity and health 10 years later. Arch. Intern. Med. 158, 1695\u20131701 (1998).","journal-title":"Arch. Intern. Med."},{"key":"745_CR11","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1161\/STROKEAHA.113.002246","volume":"45","author":"BJ Jefferis","year":"2014","unstructured":"Jefferis, B. J., Whincup, P. H., Papacosta, O. & Wannamethee, S. G. Protective effect of time spent walking on risk of stroke in older men. Stroke 45, 194\u2013199 (2014).","journal-title":"Stroke"},{"key":"745_CR12","doi-asserted-by":"publisher","first-page":"1502","DOI":"10.1111\/biom.12892","volume":"74","author":"EL Ray","year":"2018","unstructured":"Ray, E. L., Sasaki, J. E., Freedson, P. S. & Staudenmayer, J. Physical activity classification with dynamic discriminative methods. Biometrics 74, 1502\u20131511 (2018).","journal-title":"Biometrics"},{"key":"745_CR13","first-page":"403","volume":"72","author":"AP Hills","year":"1991","unstructured":"Hills, A. P. & Parker, A. W. Gait characteristics of obese children. Arch. Phys. Med. Rehabil. 72, 403\u2013407 (1991).","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"745_CR14","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1016\/j.gaitpost.2008.10.061","volume":"29","author":"CK Balasubramanian","year":"2009","unstructured":"Balasubramanian, C. K., Neptune, R. R. & Kautz, S. A. Variability in spatiotemporal step characteristics and its relationship to walking performance post-stroke. Gait Posture 29, 408\u2013414 (2009).","journal-title":"Gait Posture"},{"key":"745_CR15","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1093\/gerona\/glx174","volume":"73","author":"JK Urbanek","year":"2018","unstructured":"Urbanek, J. K. et al. Validation of gait characteristics extracted from raw accelerometry during walking against measures of physical function, mobility, fatigability, and fitness. J. Gerontol. A. Biol. Sci. Med. Sci. 73, 676\u2013681 (2018).","journal-title":"J. Gerontol. A. Biol. Sci. Med. Sci."},{"key":"745_CR16","doi-asserted-by":"publisher","first-page":"2269","DOI":"10.1088\/0967-3334\/35\/11\/2269","volume":"35","author":"MB Del Rosario","year":"2014","unstructured":"Del Rosario, M. B. et al. A comparison of activity classification in younger and older cohorts using a smartphone. Physiol. Meas. 35, 2269\u20132286 (2014).","journal-title":"Physiol. Meas."},{"key":"745_CR17","doi-asserted-by":"publisher","first-page":"158","DOI":"10.3389\/fneur.2012.00158","volume":"3","author":"MV Albert","year":"2012","unstructured":"Albert, M. V., Toledo, S., Shapiro, M. & Kording, K. Using mobile phones for activity recognition in Parkinson\u2019s patients. Front. Neurol. 3, 158 (2012).","journal-title":"Front. Neurol."},{"key":"745_CR18","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1249\/MSS.0000000000000840","volume":"48","author":"K Ellis","year":"2016","unstructured":"Ellis, K., Kerr, J., Godbole, S., Staudenmayer, J. & Lanckriet, G. Hip and wrist accelerometer algorithms for free-living behavior classification. Med. Sci. Sports Exerc. 48, 933\u2013940 (2016).","journal-title":"Med. Sci. Sports Exerc."},{"key":"745_CR19","doi-asserted-by":"publisher","first-page":"N1","DOI":"10.1088\/1361-6579\/38\/1\/N1","volume":"38","author":"A Hickey","year":"2017","unstructured":"Hickey, A., Del Din, S., Rochester, L. & Godfrey, A. Detecting free-living steps and walking bouts: validating an algorithm for macro gait analysis. Physiol. Meas. 38, N1\u2013N15 (2017).","journal-title":"Physiol. Meas."},{"key":"745_CR20","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1136\/bjsports-2014-093546","volume":"48","author":"RP Troiano","year":"2014","unstructured":"Troiano, R. P., McClain, J. J., Brychta, R. J. & Chen, K. Y. Evolution of accelerometer methods for physical activity research. Br. J. Sports Med. 48, 1019\u20131023 (2014).","journal-title":"Br. J. Sports Med."},{"key":"745_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0169649","volume":"12","author":"A Doherty","year":"2017","unstructured":"Doherty, A. et al. Large scale population assessment of physical activity using wrist worn accelerometers: the UK biobank study. PLoS ONE 12, 1\u201314 (2017).","journal-title":"PLoS ONE"},{"key":"745_CR22","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1038\/s41386-020-0771-3","volume":"46","author":"J-P Onnela","year":"2021","unstructured":"Onnela, J.-P. Opportunities and challenges in the collection and analysis of digital phenotyping data. Neuropsychopharmacology 46, 45\u201354 (2021).","journal-title":"Neuropsychopharmacology"},{"key":"745_CR23","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.1109\/TMC.2013.47","volume":"13","author":"O Yurur","year":"2014","unstructured":"Yurur, O., Labrador, M. & Moreno, W. Adaptive and energy efficient context representation framework in mobile sensing. IEEE Trans. Mob. Comput. 13, 1681\u20131693 (2014).","journal-title":"IEEE Trans. Mob. Comput."},{"key":"745_CR24","doi-asserted-by":"publisher","first-page":"115001","DOI":"10.1088\/1361-6579\/ac41b8","volume":"42","author":"JJ Davis","year":"2021","unstructured":"Davis, J. J., Straczkiewicz, M., Harezlak, J. & Gruber, A. H. CARL: a running recognition algorithm for free-living accelerometer data. Physiol. Meas. 42, 115001 (2021).","journal-title":"Physiol. Meas."},{"key":"745_CR25","doi-asserted-by":"publisher","first-page":"42592","DOI":"10.1109\/ACCESS.2018.2858933","volume":"6","author":"H Gjoreski","year":"2018","unstructured":"Gjoreski, H. et al. The university of Sussex-Huawei locomotion and transportation dataset for multimodal analytics with mobile devices. IEEE Access 6, 42592\u201342604 (2018).","journal-title":"IEEE Access"},{"key":"745_CR26","doi-asserted-by":"publisher","first-page":"511","DOI":"10.3390\/s19030511","volume":"19","author":"A Esmaeili Kelishomi","year":"2019","unstructured":"Esmaeili Kelishomi, A., Garmabaki, A. H. S., Bahaghighat, M. & Dong, J. Mobile user indoor-outdoor detection through physical daily activities. Sensors 19, 511 (2019).","journal-title":"Sensors"},{"key":"745_CR27","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1016\/j.gaitpost.2013.02.011","volume":"38","author":"J M\u00fcller","year":"2013","unstructured":"M\u00fcller, J., M\u00fcller, S., Baur, H. & Mayer, F. Intra-individual gait speed variability in healthy children aged 1\u201315 years. Gait Posture 38, 631\u2013636 (2013).","journal-title":"Gait Posture"},{"key":"745_CR28","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1093\/gerona\/gls174","volume":"68","author":"NM Peel","year":"2013","unstructured":"Peel, N. M., Kuys, S. S. & Klein, K. Gait speed as a measure in geriatric assessment in clinical settings: a systematic review. J. Gerontol. Ser. A 68, 39\u201346 (2013).","journal-title":"J. Gerontol. Ser. A"},{"key":"745_CR29","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1088\/0967-3334\/37\/10\/1757","volume":"37","author":"M Straczkiewicz","year":"2016","unstructured":"Straczkiewicz, M., Urbanek, J. K., Fadel, W. F., Crainiceanu, C. M. & Harezlak, J. Automatic car driving detection using raw accelerometry data. Physiol. Meas. 37, 1757\u20131769 (2016).","journal-title":"Physiol. Meas."},{"key":"745_CR30","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.inffus.2020.04.004","volume":"62","author":"M Gjoreski","year":"2020","unstructured":"Gjoreski, M. et al. Classical and deep learning methods for recognizing human activities and modes of transportation with smartphone sensors. Inf. Fusion 62, 47\u201362 (2020).","journal-title":"Inf. Fusion"},{"key":"745_CR31","first-page":"290","volume":"46","author":"MP Murray","year":"1967","unstructured":"Murray, M. P. Gait as a total pattern of movement. Am. J. Phys. Med. 46, 290\u2013333 (1967).","journal-title":"Am. J. Phys. Med."},{"key":"745_CR32","doi-asserted-by":"crossref","unstructured":"Sztyler, T. & Stuckenschmidt, H. On-body localization of wearable devices: an investigation of position-aware activity recognition. in 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom) 1\u20139 (IEEE, 2016).","DOI":"10.1109\/PERCOM.2016.7456521"},{"key":"745_CR33","first-page":"36","volume":"84","author":"A Pachi","year":"2005","unstructured":"Pachi, A. & Ji, T. Frequency and velocity of people walking. Struct. Eng. 84, 36\u201340 (2005).","journal-title":"Struct. Eng."},{"key":"745_CR34","doi-asserted-by":"crossref","unstructured":"BenAbdelkader, C., Cutler, R. & Davis, L. Stride and cadence as a biometric in automatic person identification and verification. in Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition 372\u2013377 (IEEE, 2002).","DOI":"10.1109\/AFGR.2002.1004182"},{"key":"745_CR35","unstructured":"Scholz, R. The Technique of the Violin (Kessinger Publishing, LLC, 1900)."},{"key":"745_CR36","doi-asserted-by":"crossref","unstructured":"Hagedorn, P. & DasGupta, A. Appendix B: Harmonic waves and dispersion relation. in Vibrations and Waves in Continuous Mechanical Systems 367\u2013372 (John Wiley & Sons, Ltd, 2007).","DOI":"10.1002\/9780470518434.app2"},{"key":"745_CR37","doi-asserted-by":"publisher","first-page":"2661","DOI":"10.1109\/TSP.2002.804066","volume":"50","author":"SC Olhede","year":"2002","unstructured":"Olhede, S. C. & Walden, A. T. Generalized Morse wavelets. IEEE Trans. Signal Process. 50, 2661\u20132670 (2002).","journal-title":"IEEE Trans. Signal Process."},{"key":"745_CR38","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1109\/TSP.2008.2007607","volume":"57","author":"JM Lilly","year":"2009","unstructured":"Lilly, J. M. & Olhede, S. C. Higher-Order Properties of Analytic Wavelets. IEEE Trans. Signal Process. 57, 146\u2013160 (2009).","journal-title":"IEEE Trans. Signal Process."},{"key":"745_CR39","unstructured":"Lilly, J. M. jLab: A data analysis package for Matlab, v 1.6.6. http:\/\/www.jmlilly.net\/jmlsoft.html (2019)."},{"key":"745_CR40","doi-asserted-by":"crossref","unstructured":"Straczkiewicz, M., Glynn, N. W. & Harezlak, J. On placement, location and orientation of wrist-worn tri-axial accelerometers during free-living measurements. Sensors. 19, 2095 (2019).","DOI":"10.3390\/s19092095"},{"key":"745_CR41","doi-asserted-by":"crossref","unstructured":"Lockhart, J. W. et al. Design Considerations for the WISDM Smart Phone-Based Sensor Mining Architecture. in Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data 25\u201333 (Association for Computing Machinery, 2011).","DOI":"10.1145\/2003653.2003656"},{"key":"745_CR42","doi-asserted-by":"crossref","unstructured":"Shoaib, M., Bosch, S., Incel, O. D., Scholten, H. & Havinga, P. J. M. Complex human activity recognition using smartphone and wrist-worn motion sensors. Sensors 16, 426 (2016).","DOI":"10.3390\/s16040426"},{"key":"745_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0075196","volume":"8","author":"H Leutheuser","year":"2013","unstructured":"Leutheuser, H., Schuldhaus, D. & Eskofier, B. M. Hierarchical, multi-sensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset. PLoS ONE 8, 1\u201311 (2013).","journal-title":"PLoS ONE"},{"key":"745_CR44","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/MPRV.2017.3971131","volume":"16","author":"Y Vaizman","year":"2017","unstructured":"Vaizman, Y., Ellis, K. & Lanckriet, G. Recognizing detailed human context in the wild from smartphones and smartwatches. IEEE Pervasive Comput 16, 62\u201374 (2017).","journal-title":"IEEE Pervasive Comput"},{"key":"745_CR45","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X. & Reyes-Ortiz, J. L. A public domain dataset for human activity recognition using smartphones. in The European Symposium on Artificial Neural Networks (ESANN, 2013)."},{"key":"745_CR46","doi-asserted-by":"crossref","unstructured":"Ichino, H., Kaji, K., Sakurada, K., Hiroi, K. & Kawaguchi, N. HASC-PAC2016: Large Scale Human Pedestrian Activity Corpus and Its Baseline Recognition. in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct 705\u2013714 (Association for Computing Machinery, 2016).","DOI":"10.1145\/2968219.2968277"},{"key":"745_CR47","doi-asserted-by":"crossref","unstructured":"Bruno, B., Mastrogiovanni, F., Sgorbissa, A., Vernazza, T. & Zaccaria, R. Analysis of human behavior recognition algorithms based on acceleration data. in 2013 IEEE International Conference on Robotics and Automation. 1602\u20131607 (IEEE, 2013).","DOI":"10.1109\/ICRA.2013.6630784"},{"key":"745_CR48","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1093\/biostatistics\/kxz033","volume":"22","author":"M Karas","year":"2019","unstructured":"Karas, M. et al. Adaptive empirical pattern transformation (ADEPT) with application to walking stride segmentation. Biostatistics 22, 331\u2013347 (2019).","journal-title":"Biostatistics"},{"key":"745_CR49","doi-asserted-by":"crossref","unstructured":"Ba\u00f1os, O. et al. mHealthDroid: a Novel Framework for Agile Development of Mobile Health Applications. in IWAAL (eds Pecchia, L., et al.) (Springer, 2014).","DOI":"10.1007\/978-3-319-13105-4_14"},{"key":"745_CR50","doi-asserted-by":"crossref","unstructured":"Vavoulas., G., Chatzaki., C., Malliotakis., T., Pediaditis., M. & Tsiknakis., M. The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones. in Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE, (ICT4AGEINGWELL 2016) 143\u2013151 (SciTePress, 2016).","DOI":"10.5220\/0005792401430151"},{"key":"745_CR51","doi-asserted-by":"crossref","unstructured":"Malekzadeh, M., Clegg, R. G., Cavallaro, A. & Haddadi, H. Mobile Sensor Data Anonymization. in Proceedings of the International Conference on Internet of Things Design and Implementation 49\u201358 (ACM, 2019).","DOI":"10.1145\/3302505.3310068"},{"key":"745_CR52","doi-asserted-by":"publisher","first-page":"10146","DOI":"10.3390\/s140610146","volume":"14","author":"M Shoaib","year":"2014","unstructured":"Shoaib, M., Bosch, S., Durmaz Incel, O., Scholten, H. & Havinga, P. J. M. Fusion of smartphone motion sensors for physical activity recognition. Sensors 14, 10146\u201310176 (2014).","journal-title":"Sensors"},{"key":"745_CR53","doi-asserted-by":"crossref","unstructured":"Mattfeld, R., Jesch, E. & Hoover, A. A new dataset for evaluating pedometer performance. in 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 865\u2013869 (IEEE, 2017).","DOI":"10.1109\/BIBM.2017.8217769"},{"key":"745_CR54","unstructured":"Jain, M., Singh, A. P., Bali, S. & Kaul, S. Speed-breaker early warning system. in USENIX\/ACM Workshop on Networked Systems for Developing Regions (NSDR, 2012)."},{"key":"745_CR55","doi-asserted-by":"publisher","first-page":"10691","DOI":"10.3390\/s140610691","volume":"14","author":"AT \u00d6zdemir","year":"2014","unstructured":"\u00d6zdemir, A. T. & Barshan, B. Detecting falls with wearable sensors using machine learning techniques. Sensors 14, 10691\u201310708 (2014).","journal-title":"Sensors"},{"key":"745_CR56","doi-asserted-by":"crossref","unstructured":"Sucerquia, A., L\u00f3pez, J. D. & Vargas-Bonilla, J. F. SisFall: a fall and movement dataset. Sensors. 17, 198 (2017).","DOI":"10.3390\/s17010198"},{"key":"745_CR57","doi-asserted-by":"crossref","unstructured":"John, D., Tang, Q., Albinali, F. & Intille, S. An open-source monitor-independent movement summary for accelerometer data processing. J. Meas. Phys. Behav. 2, 268\u2013281 (2019).","DOI":"10.1123\/jmpb.2018-0068"},{"key":"745_CR58","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.3390\/app7101101","volume":"7","author":"D Micucci","year":"2017","unstructured":"Micucci, D., Mobilio, M. & Napoletano, P. UniMiB SHAR: a dataset for human activity recognition using acceleration data from smartphones. Appl. Sci. 7, 1101 (2017).","journal-title":"Appl. Sci."},{"key":"745_CR59","doi-asserted-by":"publisher","first-page":"133190","DOI":"10.1109\/ACCESS.2019.2940729","volume":"7","author":"GM Weiss","year":"2019","unstructured":"Weiss, G. M., Yoneda, K. & Hayajneh, T. Smartphone and smartwatch-based biometrics using activities of daily living. IEEE Access 7, 133190\u2013133202 (2019).","journal-title":"IEEE Access"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-022-00745-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-022-00745-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-022-00745-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T00:53:38Z","timestamp":1728953618000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-022-00745-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,23]]},"references-count":59,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["745"],"URL":"https:\/\/doi.org\/10.1038\/s41746-022-00745-z","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,23]]},"assertion":[{"value":"2 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing financial or non-financial interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"29"}}