Slemenšek Jan, Geršak Jelka, Bratina Božidar, van Midden Vesna Marija, Pirtošek Zvezdan, Šafarič Riko
Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia.
Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia.
Bioengineering (Basel). 2024 Oct 20;11(10):1048. doi: 10.3390/bioengineering11101048.
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.
本文介绍了一种实时可穿戴系统，旨在帮助患有步态冻结发作的帕金森病患者。该系统利用先进的机器学习模型，包括卷积神经网络和循环神经网络，并通过过去样本数据预处理进行增强，以实现高精度、高效率和鲁棒性。通过持续监测步态模式，该系统提供及时干预，改善行动能力并减少冻结发作的影响。本文探讨了在基于微控制器的设备上实现CNN+RNN+PS机器学习模型。该设备以40Hz的实时处理速率运行，并部署在实际环境中，为患者提供“按需”振动刺激。本文研究了该系统以最小延迟运行的能力，平均检测延迟仅为261毫秒，步态冻结检测准确率达到95.1%。在患者接受按需刺激时，通过将步态冻结发作的平均持续时间减少45%来评估该系统的有效性。这些初步结果强调了个性化实时反馈系统在提高运动障碍患者生活质量和康复效果方面的潜力。
