Sensor-Based Outcome in Orthopaedics and Rheumatology

Dear Colleagues,

Today’s fast-paced developments and advancements in wearable sensors open up new opportunities for assessing much needed quantitative outcomes in many pathologies. Specifically in orthopaedic conditions and rheumatic diseases, gait patterns, gait function, and the amount, type, and intensity of daily physical activity play an important role in the initiation, progression, and course of the disease. For instance, the way a joint is loaded may determine the rate of progression of osteoarthritis. Currently, clinical trials on interventions—including physiotherapy, surgery, or pharmacotherapy—largely rely on patient reported outcomes. Objectively assessed parameters such as gait patterns, gait function, or amount, type or intensity of daily physical may represent much needed suitable outcomes that can be assessed in large cohorts using wearables and may even be able to capture disease flares before these are noted by the patient and/or communicated to the treating physician.

This Special Issue will push forward current frontiers in sensor-based outcome in orthopaedics and rheumatology by focusing on the development and application of validated novel sensor systems and analyses approaches with the goal of identifying relevant outcomes for orthopaedic conditions and rheumatic diseases and developing wearable sensors targeting these outcomes.

Topics of interest include, but are not limited to, the following:

• Wearable sensor systems for quantitative gait and movement analysis with a focus on orthopaedic injuries and conditions and rheumatic diseases;
• Wearable sensor systems for long-term quantitative assessment of real-world and free-living physical activity;
• Wearable sensor movement analysis combined with musculoskeletal modelling for assessment of ambulatory joint load;
• Machine-learning-based approaches for deriving gait and movement parameters;
• Exploration of deep learning capabilities to learn representations to model disease presence and progression;
• Digital biomarker development.

Prof. Dr. Annegret Mündermann
Dr. Anna Maria Liphardt
Guest Editors

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