Wearable Sensors Enable Mobility Monitoring And Assessment
A systematic review through March 9, 2025 evaluated 30 studies on wearable sensors for clinical and real-world mobility monitoring, disease risk assessment, and rehabilitation. It found 67% (20/30) used IMU-based sensors, wrist-worn devices were most common (13/20), and models like random forest (6/30) and deep learning (5/30) were frequent, with fall-risk AUCs up to 0.97. Authors recommend standardized protocols and large longitudinal validation to enable clinical translation.
Key Points
- 1Reports analyzed 30 studies; 67% used IMU sensors and 65% of IMU studies used wrist devices.
- 2Demonstrate high predictive performance: fall-risk models reported AUCs up to 0.97 in some studies.
- 3Highlight need for standardized sensor placement, harmonized ML pipelines, and large longitudinal validation.
Scoring Rationale
Comprehensive synthesis across clinical domains, but limited novelty and moderate study count constrain immediate translational impact.
Sources
Public references used for this report.
Practice interview problems based on real data
1,625 SQL & Python problems across 15 industry datasets — the exact type of data you work with.
Try 250 free problems