A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction
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Abstract
Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson’s Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used to help patients recover from FOG and resume normal gait. RAS might be ineffective due to the latency between the start of a FOG event, its detection and initialization of RAS. We propose a system capable of both FOG prediction and detection using signals from tri-axial accelerometer sensors that will be useful in initializing RAS with minimal latency. We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). We also propose three Ensemble Network Architectures that combine all the time frequency representations and DL architectures. Experimental results show that our ensemble architectures significantly improve the performance compared with existing techniques. We also present the results of applying our method trained on a publicly available dataset to data collected from patients using wearable sensors in collaboration with A.T. Still University.
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- Authors
- Ashfaque Mostafa, T., Soltaninejad, S., McIsaac, T. L., & Cheng, I.
- Affiliations
- Multimedia Research Center, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
- University of Alberta (CA)
- Creighton University (US)
- Corresponding author
- Cheng, Irene
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-09-27
- Volume
- 21
- Issue
- 19
- Pages
- 6446
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC8512068
- OpenAlex
- W3204676295
- Cited by
- 32
- References
- 13
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 5 · 2023: 8 · 2022: 3
- FWCI
- 7.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Natural Sciences and Engineering Research Council of Canada (NSERC RGPIN-2018-04367)
- Topics
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- freezing of gait, Parkinson's disease, rhythmic auditory stimulation, time-frequency representation, fog detection, fog forecasting, triaxial accelerometer, wearable sensors, recurrence plots, short-time Fourier transform, discrete wavelet transform, pseudo Wigner-Ville distribution, neural network ensembles, LSTM, convolutional neural networks
- MeSH terms
- Gait, Humans, Parkinson Disease, Quality of Life, Gait Disorders, Neurologic, Wavelet Analysis
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