IEEE Trans Biomed Eng. 2019 Jan;66(1):101-110. doi: 10.1109/TBME.2018.2830366. Epub 2018 Apr 26.
Recognition of human activities and mental states using wearable sensors and smartphones has attracted considerable attention recently. In particular, prediction of the stress level of a subject using an electrocardiogram sensor has been studied extensively. In this paper, we attempt to predict the degree of concentration by using heart-rate features. However, due to strong diversity in individuals and high sampling costs, building an accurate prediction model is still highly challenging.
To overcome these difficulties, we propose to use a multitask learning (MTL) technique for effectively sharing information among similar individuals.
Through experiments with 18 healthy subjects performing daily office works, such as writing reports, we demonstrate that the proposed method significantly improves the accuracy of concentration prediction in small sample situations.
The performance of the MTL method is shown to be stable across different subjects, which is an important advantage over conventional models.
This improvement has significant impact in real-world concentration recognition because the data collection burden of each user can be drastically mitigated.
使用可穿戴传感器和智能手机识别人类活动和心理状态引起了广泛关注。特别是，使用心电图传感器预测受试者的压力水平已得到广泛研究。在本文中，我们尝试使用心率特征来预测专注度。然而，由于个体差异较大且采样成本较高，因此构建准确的预测模型仍然极具挑战性。
为了克服这些困难，我们提出使用多任务学习（MTL）技术在相似个体之间有效地共享信息。
通过对 18 名健康受试者进行日常办公工作（如写报告）的实验，我们证明了该方法在小样本情况下显著提高了专注度预测的准确性。
MTL 方法的性能在不同受试者中表现稳定，这是其优于传统模型的一个重要优势。
由于每个用户的数据收集负担可以大大减轻，因此这种改进在现实世界的专注识别中具有重大影响。
