Methods
We conducted a PRISMA-compliant review of PubMed, IEEE Xplore, and rehabilitation technology databases (2015-2024), including studies that: (1) implemented wearable sensors in clinical stroke rehabilitation settings, (2) reported on implementation challenges or success factors, and (3) provided quantitative clinical outcomes. Two reviewers independently extracted data on sensor types, clinical workflows, staff training requirements, and patient outcomes, assessing implementation quality using the Consolidated Framework for Implementation Research (CFIR).
Results
Analysis of 38 studies (n=2,417 patients) revealed three critical translation gaps: (1) 68% of systems required technical support unavailable in clinics, (2) 54% generated data too complex for clinical interpretation, and (3) 42% disrupted existing workflows. Successful implementations shared common features: simplified interfaces reducing setup time to <5 minutes (?=78% adoption rate), automated reports aligning with clinical scales (89% therapist satisfaction), and modular designs allowing gradual integration. Clinically, sensor-guided therapy groups showed 4.3-point greater FMA-UE improvements versus controls (p=0.008), with the largest effects in clinics using therapist-friendly visualization tools (?=5.1 points, p=0.003).
Conclusion
Effective translation of wearable sensors into clinical practice requires solutions that prioritize therapist needs alongside technological innovation. Key requirements include intuitive interfaces, clinically meaningful output metrics, and flexible implementation pathways. Future development must focus on creating rehabilitation-specific sensor systems designed with clinician input from the outset, rather than adapting research prototypes for clinical use.
