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June 5, 2026Sensors0 citationsOpen Access

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

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JMJean MapinduziKDKim DanielsOKOyéné Kossi

Key Points

  • This research aims to develop a hybrid digital phenotyping framework to better assess knee osteoarthritis using wearable sensors and AI.
  • Integrated supervised laboratory evaluations with continuous monitoring using wearable sensors.
  • Feasibility tested in 40 participants (20 knee OA patients, 20 controls) over a seven-day home monitoring period.
  • Data harmonization via a standardized pipeline aligned with the ICF framework, utilizing anomaly detection and z-score normalization.
  • Enhanced ecological validity and support for earlier identification of functional decline was observed.
  • Technological and ethical challenges related to data quality and privacy were identified.
  • Potential for machine-learning-ready analytics to facilitate individualized health profiling was demonstrated.

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.

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Cite This Study

Mapinduzi et al. (2026) studied this question.

synapsesocial.com/papers/6a22698b763171746d5482cfhttps://doi.org/10.3390/s26113563
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Wearable System for Knee Osteoarthritis: Based on Multimodal Physiological Signal Assessment and Intelligent Rehabilitation2025
  2. 2Variables for Daily Monitoring of Knee Osteoarthritis Symptoms: A Scoping Review (Preprint)2025
  3. 3Machine learning prediction of self-reported real-world knee osteoarthritis pain from wearable accelerometer-derived gait features2026
  4. 4Biomechanical modeling and imaging for knee osteoarthritis – is there a role for AI?2024 · 6 citations
  5. 5ARTIFICIAL INTELLIGENCE IN THE EARLY DETECTION AND PROGRESSION PREDICTION OF OSTEOARTHRITIS: A NARRATIVE REVIEW OF CURRENT TECHNOLOGIES AND CLINICAL IMPLICATIONS2026