4.5 Article

Assessing inertial measurement unit locations for freezing of gait detection and patient preference

Journal

Publisher

BMC
DOI: 10.1186/s12984-022-00992-x

Keywords

Freezing of gait; Inertial measurement units; Machine learning

Funding

  1. Mobilize Center [P41EB027060]
  2. RESTORE Center NIH NINDS [P2CHD101913, UH3 NS107709-01A1]
  3. Stanford Bio-X Bowes Fellowship
  4. Inventec Stanford Graduate Fellowship
  5. NSF Graduate Research Fellowship [DGE-1656518]
  6. National Center for Advancing Translational Sciences, National Institutes of Health [UL1 TR001085]

Ask authors/readers for more resources

This study assessed different sets of IMUs for freezing of gait detection and patient preference. The best technical set consisted of three IMUs, which performed well and were highly wearable. The minimal IMU set only required one ankle IMU. The correlations between the models and human raters were good to excellent. These findings have important implications for the monitoring and personalized treatment of freezing of gait.
Background Freezing of gait, a common symptom of Parkinson's disease, presents as sporadic episodes in which an individual's feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference. Methods Sixteen people with Parkinson's disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson's disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events. Results The best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86) for the best technical set and minimal IMU set, respectively. Conclusions Several IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the development and adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available