20.500.12556/DiRROS-29736 Wearable online freezing of gait detection and cueing system 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. Parkinson’s disease freezing of gait machine learning real-time systems wearable devices on-demand stimulation Parkinsonova bolezen strojno učenje prenosne naprave true false true Angleški jezik Slovenski jezik Neznano 2026-06-04 10:24:11 2026-06-04 10:24:12 2026-06-05 04:05:56 0000-00-00 00:00:00 2024 0 0 Nasl. z nasl. zaslona; Opis vira z dne 29. 10. 2024; 23 str. no. 10, [article no.] 1048 Vol. 11 2024 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 004.5 2306-5354 10.3390/bioengineering11101048 213244419 RAZ_Slemensek_Jan_2024.pdf RAZ_Slemensek_Jan_2024.pdf 1 ACDD3C548D8624FE32D369971DDAB3C5 188252c61464a7c152deacf64aca7552c86076a8259e664646ed9ad58ac9e6c3 e7bee206-5fee-11f1-9215-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=44686 https://dk.um.si/IzpisGradiva.php?id=91742 1 c524d12e-5fee-11f1-9215-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=44685 Univerzitetni klinični center Ljubljana 0 0 0