Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy.
RCAST-Research Center for Advanced Science & Technology, The University of Tokyo, Tokyo 153-8904, Japan.
Sensors (Basel). 2023 Mar 17;23(6):3225. doi: 10.3390/s23063225.
In this paper, we face the problem of task classification starting from physiological signals acquired using wearable sensors with experiments in a controlled environment, designed to consider two different age populations: young adults and older adults. Two different scenarios are considered. In the first one, subjects are involved in different cognitive load tasks, while in the second one, space varying conditions are considered, and subjects interact with the environment, changing the walking conditions and avoiding collision with obstacles. Here, we demonstrate that it is possible not only to define classifiers that rely on physiological signals to predict tasks that imply different cognitive loads, but it is also possible to classify both the population group age and the performed task. The whole workflow of data collection and analysis, starting from the experimental protocol, data acquisition, signal denoising, normalization with respect to subject variability, feature extraction and classification is described here. The dataset collected with the experiments together with the codes to extract the features of the physiological signals are made available for the research community.
在本文中，我们从使用可穿戴传感器获取的生理信号出发，面对任务分类的问题，这些实验是在受控环境中设计的，旨在考虑两个不同的年龄群体：年轻人和老年人。考虑了两种不同的情况。在第一种情况下，受试者参与不同的认知负荷任务，而在第二种情况下，则考虑空间变化的条件，并且受试者与环境交互，改变行走条件并避免与障碍物碰撞。在这里，我们证明不仅可以定义依赖于生理信号来预测涉及不同认知负荷的任务的分类器，而且还可以对人群年龄和执行的任务进行分类。从实验方案、数据采集、信号去噪、相对于主体变异性的归一化、特征提取和分类开始，这里描述了数据收集和分析的整个工作流程。与实验一起收集的数据集以及提取生理信号特征的代码可供研究社区使用。
