{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T16:42:22Z","timestamp":1764002542790,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T00:00:00Z","timestamp":1653350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AstraZeneca"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Physical activity (PA) is globally recognized as a pillar of general health. Step count, as one measure of PA, is a well known predictor of long-term morbidity and mortality. Despite its popularity in consumer devices, a lack of methodological standards and clinical validation remains a major impediment to step count being accepted as a valid clinical endpoint. Previous works have mainly focused on device-specific step-count algorithms and often employ sensor modalities that may not be widely available. This may limit step-count suitability in clinical scenarios. In this paper, we trained neural network models on publicly available data and tested on an independent cohort using two approaches: generalization and personalization. Specifically, we trained neural networks on accelerometer signals from one device and either directly applied them or adapted them individually to accelerometer data obtained from a separate subject cohort wearing multiple distinct devices. The best models exhibited highly accurate step-count estimates for both the generalization (96\u201399%) and personalization (98\u201399%) approaches. The results demonstrate that it is possible to develop device-agnostic, accelerometer-only algorithms that provide highly accurate step counts, positioning step count as a reliable mobility endpoint and a strong candidate for clinical validation.<\/jats:p>","DOI":"10.3390\/s22113989","type":"journal-article","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:14:14Z","timestamp":1653437654000},"page":"3989","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Accurate Step Count with Generalized and Personalized Deep Learning on Accelerometer Data"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1761-3105","authenticated-orcid":false,"given":"Long","family":"Luu","sequence":"first","affiliation":[{"name":"Digital Health, Oncology R&D, AstraZeneca, Gaithersburg, MD 20878, USA"}]},{"given":"Arvind","family":"Pillai","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA"}]},{"given":"Halsey","family":"Lea","sequence":"additional","affiliation":[{"name":"Digital Health, Oncology R&D, AstraZeneca, Gaithersburg, MD 20878, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8126-9922","authenticated-orcid":false,"given":"Ruben","family":"Buendia","sequence":"additional","affiliation":[{"name":"Biometrics, Late-Stage Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, 43183 Gothenburg, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5116-7471","authenticated-orcid":false,"given":"Faisal M.","family":"Khan","sequence":"additional","affiliation":[{"name":"AI & Analytics, Data Science & Artificial Intelligence R&D, AstraZeneca, Gaithersburg, MD 20878, USA"}]},{"given":"Glynn","family":"Dennis","sequence":"additional","affiliation":[{"name":"AI & Analytics, Data Science & Artificial Intelligence R&D, AstraZeneca, Gaithersburg, MD 20878, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s12170-010-0109-5","article-title":"Steps to Better Cardiovascular Health: How Many Steps Does It Take to Achieve Good Health and How Confident Are We in This Number?","volume":"4","year":"2010","journal-title":"Curr. 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