Machine learning-based identification of a minimal wearable sensor for gait assessment in vestibular schwannoma

 Does your competent? doctor even have an OBJECTIVE gait monitoring device so EXACT REHAB PROTOCOLS  can be assigned to fix the gait disability?  One of my therapists was so bad they thought demonstrating; 'Walk this way' was appropriate!

NO? So, PURE INCOMPETENCE? 

Why haven't you fired the board of directors for not having correct performance objectives for staff?

Many possibilities out there and your incompetent? doctor has done nothing! 

wearable sensors(34 posts to January 2018)

Machine learning-based identification of a minimal wearable sensor for gait assessment in vestibular schwannoma

• Lou C. Kohler Voinov,
• Sergio Sanchez-Manso,
• Raabeae Aryan,
• Jennifer L. Millar,
• Michael C. Schubert &
• Kathleen E. Cullen 

We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply.

Abstract

Background

Vestibular schwannoma (VS) impairs balance and gait, often leading to substantial functional limitations that are incompletely captured by standard clinical assessments. While wearable inertial sensors and machine learning offer promise for objective gait analysis, clinical translation is limited by uncertainty regarding optimal sensor placement, task selection, and interpretability.

Methods

We recorded six-dimensional kinematics from 32 individuals with unilateral vestibular schwannoma and 32 age-matched healthy controls as they performed the ten tasks of the Functional Gait Assessment (FGA). Adapting a previously developed deep learning framework, we systematically evaluated all combinations of ten gait tasks and six sensor locations, yielding 60 task–sensor models. Models were trained and tested using subject-level leave-one-out cross-validation. To support clinical interpretation, we derived a continuous kinematic score reflecting the similarity of an individual’s gait patterns to those observed in vestibular pathology.

Results

Model performance depended strongly on task and sensor selection. Gait with eyes closed (FGA8) emerged as the most informative task, and wrist-mounted sensors consistently outperformed head and trunk sensors across tasks. A wrist-worn sensor during gait with eyes closed provided discrimination between VS and control participants with overall accuracies ranging from 60 to 83%, while substantially reducing instrumentation burden. The resulting kinematic score significantly differentiated groups, and model accuracies correlated with established clinical measures including the Dizziness Handicap Inventory.

Conclusions

These findings demonstrate that machine learning can be used not only to classify gait pathology, but to identify a minimal, clinically deployable wearable configuration for objective gait assessment in vestibular schwannoma. The proposed wrist-based kinematic score offers a continuous, interpretable metric that complements existing clinical assessments and supports longitudinal monitoring across in-clinic and remote rehabilitation settings. This work advances the development of quantitative, scalable digital biomarkers for precision rehabilitation in vestibular disorders.