| Title: | Wearable online freezing of gait detection and cueing system |
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| Authors: | ID Slemenšek, Jan (Author)
ID Geršak, Jelka (Author)
ID Bratina, Božidar (Author)
ID Van Midden, Vesna M. (Author)
ID Pirtošek, Zvezdan (Author)
ID Šafarič, Riko (Author) |
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| Files: |  bioengineering-11-01048-v2.pdf (6,29 MB)
MD5: 2B3A687224A734C9D539F4BCF17AB9CC
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
FS - Faculty of Mechanical Engineering
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| Abstract: | This paper presents a real-time wearable system designed to assist Parkinson’s disease patients experiencing freezing of gait episodes. The system utilizes advanced machine learning models, including convolutional and recurrent neural networks, enhanced with past sample data preprocessing to achieve high accuracy, efficiency, and robustness. By continuously monitoring gait patterns, the system provides timely interventions, improving mobility and reducing the impact of freezing episodes. This paper explores the implementation of a CNN+RNN+PS machine learning model on a microcontroller-based device. The device operates at a real-time processing rate of 40 Hz and is deployed in practical settings to provide ‘on demand’ vibratory stimulation to patients. This paper examines the system’s ability to operate with minimal latency, achieving an average detection delay of just 261 milliseconds and a freezing of gait detection accuracy of 95.1%. While patients received on-demand stimulation, the system’s effectiveness was assessed by decreasing the average duration of freezing of gait episodes by 45%. These preliminarily results underscore the potential of personalized, real-time feedback systems in enhancing the quality of life and rehabilitation outcomes for patients with movement disorders. |
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| Keywords: | Parkinson’s disease, freezing of gait, machine learning, real-time systems, wearable devices, on-demand stimulation |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 17.10.2024 |
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| Article acceptance date: | 18.10.2024 |
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| Publication date: | 20.10.2024 |
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| Publisher: | MDPI |
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| Year of publishing: | 2024 |
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| Number of pages: | 23 str. |
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| Numbering: | Vol. 11, no. 10, [article no.] 1048 |
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| PID: | 20.500.12556/DKUM-91742  |
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| UDC: | 004.5 |
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| ISSN on article: | 2306-5354 |
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| COBISS.SI-ID: | 213244419 |
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| DOI: | 10.3390/bioengineering11101048 |
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| Copyright: | © 2024 by the authors |
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| Publication date in DKUM: | 31.01.2025 |
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| Views: | 0 |
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| Downloads: | 4 |
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| Metadata: |    |
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| Categories: | Misc.
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