{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T06:33:43Z","timestamp":1792996423934,"version":"4.5.0"},"reference-count":92,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Osteoarthritis (OA) is a highly prevalent musculoskeletal disorder and a major cause of disability, posing growing challenges for healthcare systems worldwide. Conventional supervised clinical assessments provide valuable insights but are largely limited to cross-sectional snapshots and often fail to reflect the variability of real-world functioning, physical activity patterns, and symptom fluctuations experienced by individuals with OA, especially those with knee OA. This perspective introduces a multisensor digital phenotyping framework for smart knee OA assessment, integrating supervised laboratory evaluations with unsupervised continuous monitoring in daily living environments using wearable sensors, smart insoles, activity trackers, and mobile devices. Feasibility was tested in 40 participants (20 knee OA patients, 20 controls). Raw data from questionnaires, electronic goniometry, dynamometry, force plate, connected insoles, and seven-day home monitoring were harmonized via a standardized pipeline aligned with the ICF framework. The pipeline employed anomaly detection, missing data imputation, z-score normalization, and cloud-based storage. This framework is envisioned to facilitate advanced data integration and machine-learning-ready analytics, enabling longitudinal monitoring, pattern recognition, and individualized health profiling. By conceptually bridging cross-sectional and continuous sensing modalities, this approach has the potential to enhance ecological validity, support earlier identification of functional decline, and inform data-driven clinical decision-making. Key methodological, technological, and ethical challenges\u2014including data quality, interpretability, privacy, digital literacy, and clinical adoption\u2014are also highlighted. Overall, this paper underscores the promise of AI-enabled multisensor digital phenotyping to advance smart, personalized, and precision healthcare for individuals with knee OA.<\/jats:p>","DOI":"10.3390\/s26113563","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:08:07Z","timestamp":1780510087000},"page":"3563","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Wearable Sensors and Artificial Intelligence for Ecological Knee Osteoarthritis Assessment: Development and Feasibility of a Hybrid Digital Phenotyping Framework"],"prefix":"10.3390","volume":"26","author":[{"given":"Jean","family":"Mapinduzi","sequence":"first","affiliation":[{"name":"REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium"},{"name":"Technology-Supported and Data-Driven Rehabilitation, Data Sciences Institute, Hasselt University, 3590 Diepenbeek, Belgium"},{"name":"TechnoRehab Lab<sup>2<\/sup>, Fili\u00e8re de Kin\u00e9sith\u00e9rapie et de R\u00e9adaptation, D\u00e9partement des Sciences Cliniques, Institut National de Sant\u00e9 Publique (INSP), Bujumbura 6807, Burundi"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4222-4518","authenticated-orcid":false,"given":"Kim","family":"Daniels","sequence":"additional","affiliation":[{"name":"REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium"},{"name":"Department of PXL\u2014Healthcare, PXL University of Applied Sciences and Arts, 3500 Hasselt, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oy\u00e9n\u00e9","family":"Kossi","sequence":"additional","affiliation":[{"name":"REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium"},{"name":"ENATSE, National School of Public Health and Epidemiology, Universit\u00e9 de Parakou, Parakou P.O. Box 123, Benin"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9990-7723","authenticated-orcid":false,"given":"Jonas","family":"Verbrugghe","sequence":"additional","affiliation":[{"name":"REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7729-4700","authenticated-orcid":false,"given":"Bruno","family":"Bonnech\u00e8re","sequence":"additional","affiliation":[{"name":"REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium"},{"name":"Technology-Supported and Data-Driven Rehabilitation, Data Sciences Institute, Hasselt University, 3590 Diepenbeek, Belgium"},{"name":"Department of PXL\u2014Healthcare, PXL University of Applied Sciences and Arts, 3500 Hasselt, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1016\/S0140-6736(19)30417-9","article-title":"Osteoarthritis","volume":"393","author":"Hunter","year":"2019","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1136\/annrheumdis-2013-204763","article-title":"The global burden of hip and knee osteoarthritis: Estimates from the global burden of disease 2010 study","volume":"73","author":"Cross","year":"2014","journal-title":"Ann. 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