基于深度学习的老年人行走任务分类（使用fNIRS）
Deep Learning Based Walking Tasks Classification in Older Adults using fNIRS
摘要：
步态特征的下降在老年人中很常见，是残疾、发病率和死亡率风险增加的指标。在双任务步行（DTW）条件下，进一步退化的表现，步态和次要的认知任务被发现在老年人，这是显着相关的跌倒史。最近研究了老年人DTW期间步态的皮质控制，特别是通过功能性近红外光谱（fNIRS）测量的前额叶皮质（PFC）。然而，在单任务和双任务步态条件下的认知激活的差异的自动分类还没有得到广泛的研究。在本文中，我们将其制定为分类任务，并利用深度学习来执行STW，DTW和单一认知任务（STA）的自动分类。我们对数据样本进行分析，揭示了随后用作附加特征的HbO 2和Hb值之间差异的特征。我们执行特征工程以将fNIRS特征制定为3通道图像，并应用各种图像处理技术进行数据增强以增强深度学习模型的性能。实验结果表明，使用fNIRS数据集以及性别和认知状态信息进行微调的预训练深度学习模型可以达到约81%的分类准确率，比传统的机器学习算法高出约10%。我们进一步进行消融研究，以确定功能的排名，如fNIRS水平和/或体素位置对分类任务的贡献。
英文摘要：
Decline in gait features is common in older adults and an indicator of increased risk of disability, morbidity, and mortality. Under dual task walking (DTW) conditions, further degradation in the performance of both the gait and the secondary cognitive task were found in older adults which were significantly correlated to falls history. Cortical control of gait, specifically in the pre-frontal cortex (PFC) as measured by functional near infrared spectroscopy (fNIRS), during DTW in older adults has recently been studied. However, the automatic classification of differences in cognitive activations under single and dual task gait conditions has not been extensively studied yet. In this paper, we formulate this as a classification task and leverage deep learning to perform automatic classification of STW, DTW and single cognitive task (STA). We conduct analysis on the data samples which reveals the characteristics on the difference between HbO2 and Hb values that are subsequently used as additional features. We perform feature engineering to formulate the fNIRS features as a 3-channel image and apply various image processing techniques for data augmentation to enhance the performance of deep learning models. Experimental results show that pre-trained deep learning models that are fine-tuned using the collected fNIRS dataset together with gender and cognitive status information can achieve around 81\% classification accuracy which is about 10\% higher than the traditional machine learning algorithms. We further perform an ablation study to identify rankings of features such as the fNIRS levels and/or voxel locations on the contribution of the classification task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
