JMIR Publications

ABSTRACT

Background:

Knee osteoarthritis (knee OA) is a multifactorial degenerative joint condition characterized by chronic pain, stiffness, and reduced mobility. Traditional clinical assessments are unable to capture the dynamic nature of symptom fluctuation in real-world settings, which limits timely and personalized intervention.

Objective:

This scoping review aims to synthesize current research on sensor technologies and their associated data modalities used for daily life monitoring of knee OA symptoms. The review focuses on determining what types of physiological, biomechanical, and subjective signals can be used for daily symptom tracking.

Methods:

A systematic literature search was conducted across PubMed, Web of Science, and IEEE Xplore following PRISMA-ScR guidelines. Eligibility criteria included non-invasive, sensor-based monitoring approaches that could be applied by patients in everyday environments. Extracted data encompassed sensor modalities, placement, measured variables, symptom types, and main study findings.

Results:

A total of 18 studies were included, employing a variety of wearable and ambient sensor modalities, such as inertial measurement units (IMUs), electromyography (EMG), temperature and heart rate sensors, and mobile apps. Biomechanical parameters—such as range of motion, joint torque, and gait dynamics—were the most frequently monitored. However, the review also highlighted that there was a lack of integration between the collected sensor data and actionable feedback or adaptive monitoring frameworks.

Conclusions:

The review suggests that while many sensor systems can technically measure relevant biomechanical and physiological markers, few of them are integrated into frameworks that enable patient feedback. The results underscore the importance of moving towards multidimensional monitoring approaches in daily life.