Computers in Biology and Medicine ( IF 7.7 ) Pub Date : 2020-07-29 , DOI: 10.1016/j.compbiomed.2020.103935 Jaakko Tervonen 1 , Sampsa Puttonen 2 , Mikko J Sillanpää 3 , Leila Hopsu 2 , Zsolt Homorodi 1 , Janne Keränen 1 , Janne Pajukanta 4 , Antti Tolonen 5 , Arttu Lämsä 1 , Jani Mäntyjärvi 1
Stress has become a major health concern and there is a need to study and develop new digital means for real-time stress detection. Currently, the majority of stress detection research is using population based approaches that lack the capability to adapt to individual differences. They also use supervised learning methods, requiring extensive labeling of training data, and they are typically tested on data collected in a laboratory and thus do not generalize to field conditions. To address these issues, we present multiple personalized models based on an unsupervised algorithm, the Self-Organizing Map (SOM), and we propose an algorithmic pipeline to apply the method for both laboratory and field data. The performance is evaluated on a dataset of physiological measurements from a laboratory test and on a field dataset consisting of four weeks of physiological and smartphone usage data. In these tests, the performance on the field data was steady across the different personalization levels (accuracy around 60%) and a fully personalized model performed the best on the laboratory data, achieving accuracy of 92% which is comparable to state-of-the-art supervised classifiers. These results demonstrate the feasibility of SOM in personalized mental stress detection both in constrained and free-living environment.
中文翻译:
具有自组织图的个性化精神压力检测:从实验室到现场。
压力已成为主要的健康问题,需要研究和开发用于实时压力检测的新数字手段。当前,大多数压力检测研究正在使用基于人群的方法,这些方法缺乏适应个体差异的能力。他们还使用有监督的学习方法,需要对训练数据进行广泛标记,并且通常在实验室收集的数据上对它们进行测试,因此不能推广到野外条件。为了解决这些问题,我们提出了一种基于无监督算法的自组织映射(SOM)的多个个性化模型,并且我们提出了一种算法管道来将该方法应用于实验室和现场数据。在来自实验室测试的生理测量数据集和由四个星期的生理和智能手机使用数据组成的现场数据集上评估性能。在这些测试中,现场数据在不同的个性化级别(稳定度约为60%)上均保持稳定,并且完全个性化的模型在实验室数据上表现最佳,达到了92%的准确度,可与最新状态媲美技术监督的分类器。这些结果证明了SOM在受限和自由生活环境下个性化精神压力检测中的可行性。达到了92%的准确度,可与最新的监督分类器相提并论。这些结果证明了SOM在受限和自由生活环境下个性化精神压力检测中的可行性。达到92%的准确性,可与最新的监督分类器相提并论。这些结果证明了SOM在受限和自由生活环境下个性化精神压力检测中的可行性。