Activity Recognition in Parkinson's Patients from Motion Data Using a CNN Model Trained by Healthy Subjects.
Activity Recognition in Parkinson's Patients from Motion Data Using a CNN Model Trained by Healthy Subjects.
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DOI：
10.1109/embc48229.2022.9871181
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发表时间：
2022-07-01
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通讯作者：
Ghoraani, Behnaz
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作者：
Davidashvilly, Shelly;Hssayeni, Murtadha;Ghoraani, Behnaz
帕金森病（PwPD）患者的身体活动识别是具有挑战性的，由于缺乏足够大的和高质量的运动数据的PwPD。解决这一障碍的一种常见方法是使用基于健康患者质量更好的数据训练的模型。由于运动并发症影响PwPD中的运动模式以及数据之间传感器轴方向的差异，模型可能难以在这些领域进行概括。在本文中，我们研究了在年轻健康人群中训练的深度卷积神经网络（CNN）模型对PD的可推广性，以及数据增强在缓解传感器位置变化方面的作用。我们使用了两个公开的健康数据集-PAMAP 2和MHEALTH。两个数据集分别有9名和10名受试者将传感器放置在胸部、手腕和脚踝上。还使用了私人PD数据集。提出的CNN模型在PAMAP 2上进行了k倍交叉验证，基于受试者的数量，有和没有数据增强，并直接在MHEALTH和PD数据上进行测试。在没有数据增强的情况下，训练模型在MHEALTH上的准确率为48.16%，在没有模型自适应技术的情况下直接应用时，PD数据的准确率为0%。随着数据的增加，准确度分别提高到87.43%和44.78%，表明该方法补偿了数据之间潜在的传感器放置变化。临床相关性-可穿戴传感器和机器学习可以提供有关PwPD活动水平的重要信息。这些信息可以被治疗医生用来进行适当的临床干预，如康复，以提高生活质量。
Physical activity recognition in patients with Parkinson's Disease (PwPD) is challenging due to the lack of large-enough and good quality motion data for PwPD. A common approach to this obstacle involves the use of models trained on better quality data from healthy patients. Models can struggle to generalize across these domains due to motor complications affecting the movement patterns in PwPD and differences in sensor axes orientations between data. In this paper, we investigated the generalizability of a deep convolutional neural network (CNN) model trained on a young, healthy population to PD, and the role of data augmentation on alleviating sensor position variability. We used two publicly available healthy datasets - PAMAP2 and MHEALTH. Both datasets had sensor placements on the chest, wrist, and ankle with 9 and 10 subjects, respectively. A private PD dataset was utilized as well. The proposed CNN model was trained on PAMAP2 in k-fold cross-validation based on the number of subjects, with and without data augmentation, and tested directly on MHEALTH and PD data. Without data augmentation, the trained model resulted in 48.16% accuracy on MHEALTH and 0% on the PD data when directly applied with no model adaptation techniques. With data augmentation, the accuracies improved to 87.43% and 44.78%, respectively, indicating that the method compensated for the potential sensor placement variations between data. Clinical Relevance- Wearable sensors and machine learning can provide important information about the activity level of PwPD. This information can be used by the treating physician to make appropriate clinical interventions such as rehabilitation to improve quality of life.