Classification of freezing of gait using accelerometer data: A systematic performance evaluation approach

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Parkinson's disease is one of the most common neurodegenerative chronic diseases which can affect the patient's quality of life by creating several motor and non-motor impairments. The freezing of gait is one such motor impairment which can cause the inability to move forward despite the intention to walk. The identification of the freezing-of-gait events using sensor technology and machine-learning algorithms can result in an improvement in the quality of life and can decrease the risk of fall in Parkinson's patients. Our study focuses on a systematic performance evaluation of machine learning algorithms for developing a good fit and generalized model. In this work, we train time-domain and frequency-domain-transform-based features on fully connected artificial and deep neural network algorithm for classifying the events of freezing of gait in patients by using accelerometer data. We evaluate these algorithms for hyperparameters such as batch size, optimizer type, and window sizes in a step-wise process. We identify an optimal combination of parameters according to the accuracy and model fit optimality metrics, for artificial and deep neural network to classify freezing of gait events in Parkinson's patients. We were able to achieve classification accuracy of - with Adam optimizer, batch sizes (BS) of 256 and 8 and epochs of 60 and 40 for ANN and DNN respectively.

Original languageEnglish
Title of host publicationiWOAR 2023 - 8th International Workshop on Sensor-based Activity Recognition and Artificial Intelligence, Proceedings
EditorsDenys J.C. Matthies, Marcin Grzegorzek, Arjan Kuijper, Heike Leutheuser
PublisherACM
Number of pages11
ISBN (Electronic)979-8-4007-0816-9
DOIs
Publication statusPublished - 11 Oct 2023
Publication typeA4 Article in conference proceedings
EventInternational Workshop on Sensor-based Activity Recognition and Artificial Intelligence - Lubeck, Germany
Duration: 21 Sept 202322 Sept 2023

Conference

ConferenceInternational Workshop on Sensor-based Activity Recognition and Artificial Intelligence
Country/TerritoryGermany
CityLubeck
Period21/09/2322/09/23

Keywords

  • Accelerometer sensors
  • Artificial and deep learning algorithms
  • Freezing of gait
  • Hyperparameters
  • Learning curves
  • Machine learning
  • Parkinson's disease
  • Statistical features
  • Supervised algorithms
  • Window sizes

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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