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Reachable Workspace as a Clinical Outcome for Upper Extremity Function: A Narrative Review
Jay J Han1, Lawrence J Hayward2, Christina Adams3
1Department of Physical Medicine & Rehabilitation, University of California Irvine School of Medicine, Orange, California, USA.
Muscle & Nerve
|April 10, 2026
Summary
Three-dimensional reachable workspace (RWS) using motion sensing technology offers a reliable measure of upper extremity (UE) function. This innovative tool, quantified by relative surface area (RSA), shows clinical utility across various neuromuscular and musculoskeletal disorders.
Area of Science:
- Biomedical Engineering
- Clinical Outcome Assessment
- Rehabilitation Technology
Background:
- Upper extremity (UE) impairment affects mobility and function in various diseases.
- Existing assessment methods may lack sensitivity or comprehensive data capture.
- Motion sensing offers a novel approach to quantify UE function.
Purpose of the Study:
- To introduce and validate three-dimensional reachable workspace (RWS) and relative surface area (RSA) as measures of UE function.
- To demonstrate the clinical utility and sensitivity of RWS/RSA in distinguishing healthy from impaired UE function.
- To explore the application of RWS/RSA in various neuromuscular and musculoskeletal conditions.
Main Methods:
- Utilizing motion sensing technology to capture detailed UE motion.
- Reconstructing an individual's 3D reachable workspace (RWS).
- Quantifying RWS as relative surface area (RSA) for functional assessment.
Main Results:
- RSA reliably distinguishes between healthy and impaired UE function.
- RSA demonstrates sensitivity in detecting longitudinal changes, even in slowly progressive conditions.
- RWS/RSA show strong associations with established clinical measures, PROs, and functional performance.
Conclusions:
- Sensor-based RWS is a valid, reliable, and sensitive UE clinical outcome assessment (COA) tool.
- RWS/RSA have demonstrated clinical utility in neuromuscular diseases (FSHD, DMD, ALS) and other conditions (stroke, orthopedic).
- Future integration of mobile sensing, AI, and machine learning promises enhanced clinical studies and patient care.

