Gait partitioning methods in Parkinson's disease patients with motor fluctuations: A comparative analysis

I. Mileti, M. Germanotta, S. Alcaro, A. Pacilli, I. Imbimbo, M. Petracca, C. Erra, E. Di Sipio, I. Aprile, S. Rossi, A. R. Bentivoglio, L. Padua, E. Palermo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Measuring gait quality of patients through wearable sensors has become an useful tool to assist in drug administration. Several methods, based on inertial sensors placed on lower limbs, have been proposed over the last decade to estimate clinical meaningful parameters such as gait phase distribution. Application of those algorithms for monitoring gait abnormalities would have a tremendous potential in patients with Parkinson's disease. However, their applicability to patients with severe gait impairment has not been fully tested and compared. In the present study, we conducted a comparative analysis of gait phase detection methods applied to patients with Parkinson's disease both in OFF and ON levodopa conditions. We compared gait partitioning performance of three already proposed algorithms based on a threshold method and a novel Hidden Markov Model approach. Fourteen subjects with idiopathic PD have been enrolled in this study, and evaluated twice during the same day: both in OFF and in ON conditions. All patients have performed three walking tasks along a 20 m walkway in with four Inertial Measured Units placed on shanks and feet. The sagittal angular velocity of all segments and the linear acceleration of feet have been gathered to evaluate stance and swing phases. Force resistive sensors used as foot-switches have been placed under the feet to estimate the reference gait phase sequence. The goodness (G) of methods was evaluated through the Receiver Operating Characteristic. The results provided an optimum goodness for all examined methods (0 < G < 0.25). The best performance has been achieved with the Hidden Model Markov both in OFF (G=0.01) and ON (G=0.01) levodopa conditions. Our results encourage the applicability of HMM in developments of wearable systems for daily monitoring in patients with motor fluctuations.

Original languageEnglish
Title of host publication2017 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages402-407
Number of pages6
ISBN (Electronic)9781509029839
DOIs
Publication statusPublished - Jul 19 2017
Event12th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2017 - Rochester, United States
Duration: May 7 2017May 10 2017

Conference

Conference12th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2017
CountryUnited States
CityRochester
Period5/7/175/10/17

Keywords

  • gait detection
  • gait phases
  • motor fluctuations
  • Parkinson's Disease

ASJC Scopus subject areas

  • Instrumentation
  • Computer Science Applications
  • Medicine (miscellaneous)

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    Mileti, I., Germanotta, M., Alcaro, S., Pacilli, A., Imbimbo, I., Petracca, M., Erra, C., Di Sipio, E., Aprile, I., Rossi, S., Bentivoglio, A. R., Padua, L., & Palermo, E. (2017). Gait partitioning methods in Parkinson's disease patients with motor fluctuations: A comparative analysis. In 2017 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2017 - Proceedings (pp. 402-407). [7985910] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MeMeA.2017.7985910