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Perspective

Wearable Sensors and Artificial Intelligence for Ecological Knee Osteoarthritis Assessment: Development and Feasibility of a Hybrid Digital Phenotyping Framework

1
REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium
2
Technology-Supported and Data-Driven Rehabilitation, Data Sciences Institute, Hasselt University, 3590 Diepenbeek, Belgium
3
TechnoRehab Lab, Filière de Kinésithérapie et de Réadaptation, Département des Sciences Cliniques, Institut National de Santé Publique (INSP), Bujumbura 6807, Burundi
4
Department of PXL—Healthcare, PXL University of Applied Sciences and Arts, 3500 Hasselt, Belgium
5
ENATSE, National School of Public Health and Epidemiology, Université de Parakou, Parakou P.O. Box 123, Benin
*
Author to whom correspondence should be addressed.
Sensors 2026, 26(11), 3563; https://doi.org/10.3390/s26113563
Submission received: 10 April 2026 / Revised: 21 May 2026 / Accepted: 2 June 2026 / Published: 3 June 2026
(This article belongs to the Special Issue State of the Art in Wearable Sensors for Health Monitoring)

Abstract

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—including data quality, interpretability, privacy, digital literacy, and clinical adoption—are 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.
Keywords: osteoarthritis; smart healthcare; AI-enabled sensing; digital phenotyping; wearable sensors; remote patient monitoring; precision diagnosis osteoarthritis; smart healthcare; AI-enabled sensing; digital phenotyping; wearable sensors; remote patient monitoring; precision diagnosis

Share and Cite

MDPI and ACS Style

Mapinduzi, J.; Daniels, K.; Kossi, O.; Verbrugghe, J.; Bonnechère, B. Wearable Sensors and Artificial Intelligence for Ecological Knee Osteoarthritis Assessment: Development and Feasibility of a Hybrid Digital Phenotyping Framework. Sensors 2026, 26, 3563. https://doi.org/10.3390/s26113563

AMA Style

Mapinduzi J, Daniels K, Kossi O, Verbrugghe J, Bonnechère B. Wearable Sensors and Artificial Intelligence for Ecological Knee Osteoarthritis Assessment: Development and Feasibility of a Hybrid Digital Phenotyping Framework. Sensors. 2026; 26(11):3563. https://doi.org/10.3390/s26113563

Chicago/Turabian Style

Mapinduzi, Jean, Kim Daniels, Oyéné Kossi, Jonas Verbrugghe, and Bruno Bonnechère. 2026. "Wearable Sensors and Artificial Intelligence for Ecological Knee Osteoarthritis Assessment: Development and Feasibility of a Hybrid Digital Phenotyping Framework" Sensors 26, no. 11: 3563. https://doi.org/10.3390/s26113563

APA Style

Mapinduzi, J., Daniels, K., Kossi, O., Verbrugghe, J., & Bonnechère, B. (2026). Wearable Sensors and Artificial Intelligence for Ecological Knee Osteoarthritis Assessment: Development and Feasibility of a Hybrid Digital Phenotyping Framework. Sensors, 26(11), 3563. https://doi.org/10.3390/s26113563

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