Related Experiment Video
Updated: May 14, 2025

Home-Based Monitor for Gait and Activity Analysis
Published on: August 8, 2019
Wearable sensors: a valid tool for quantifying cervical spondylotic myelopathy (CSM)
Justin Mathew1, Steven D Glassman1, Jeffrey L Gum1
1Norton Leatherman Spine Center, Louisville, KY 40202, USA.
Background Context:
Though nearly ubiquitous in testing for cervical spondylotic myelopathy (CSM), the conventional Romberg test is constrained by its binary nature. Differentiating which patients have mild cases from those who require surgery more urgently is challenging without objective metrics. The recent advance of performing a Romberg test on a force plate enables a more granular measure of imbalance in patients with CSM. Nonetheless, the use of force plates limits the amount of patient data that can be collected and the setting in which they can be collected. The advent of wearable sensors offers the opportunity to measure imbalance in patients when they are away from the clinical setting.
Purpose:
To determine if wearable sensors provide quantitative Romberg test data comparable to that of using a force plate.
Study Design:
Prospective longitudinal cohort.
Patient Sample:
Subjects with CSM scheduled for surgery.
Outcome Measure:
Quantitative Romberg test.
Methods:
Patients scheduled for surgical treatment of CSM underwent Romberg testing on a force plate with wearable sensors placed at the C7 level. Data on force plate displacement (measured in mm of displacement) was compared to motion data from the wearable sensor (measured in degrees of angular displacement).
Results:
Data was collected on 48 patients, mean age of 57.77 years, mean BMI of 31.69 kg/m2, with 23 (48%) females. There were strong statistically significant correlations between data from the force plate and from the wearable sensor with eyes closed for total lateral motion (r=0.766, p<.001), total path travelled (r=0.658, p<.001) and maximum lateral sway (r=0.800, p<.001).
Conclusion:
Wearable sensors present a growing subset of remote digital health technology to gather biomechanical gait and stance data. The results of this study suggest the feasibility of using sensors to quantify CSM severity. These data can elucidate the disease course and manifestations of conditions like CSM and may drive diagnostic and therapeutic decisions in the future.

