Continuous and multimodal stress detection has been performed recently through wearable devices and machine learning algorithms. However, a well-known and important challenge of working on physiological signals recorded by conventional monitoring devices is missing data due to sensors insufficient contact and interference by other equipment. This challenge becomes more problematic when the user/patient is mentally or physically active or stressed because of more frequent conscious or subconscious movements. In this paper, we propose ReLearn, a robust machine learning framework for stress detection from biomarkers extracted from multimodal physiological signals. ReLearn effectively copes with missing data and outliers both at training and inference phases. ReLearn, composed of machine learning models for feature selection, outlier detection, data imputation, and classification, allows us to classify all samples, including those with missing values at inference. In particular, according to our experiments and stress database, while by discarding all missing data, as a simplistic yet common approach, no prediction can be made for 34% of the data at inference, our approach can achieve accurate predictions, as high as 78%, for missing samples. Also, our experiments show that the proposed framework obtains a cross-validation accuracy of 86.8% even if more than 50% of samples within the features are missing.
翻译:最近通过磨损装置和机器学习算法对连续和多式联运压力进行了持续检测;然而,由于传感器的接触不足和其他设备干扰,在常规监测装置所记录的生理信号方面开展工作的众所周知和重要的挑战是缺乏数据;当用户/病人由于更频繁的觉悟或潜意识运动而精神或身体活跃或压力时,这种挑战就更成问题;在本文件中,我们提议ReLearn,一个强有力的机器学习框架,用于从从从多式联运生理信号中提取的生物标志中检测压力; 重新有效地应对在培训和推断阶段所缺少的数据和异常值; Reearn, 由用于特征选择、外部检测、数据估算和分类的机器学习模型组成,使得我们能够对所有样本进行分类,包括缺少值的样本。特别是,根据我们的实验和压力数据库,通过抛弃所有缺失的数据,作为一个简单但常见的方法,无法对34%的数据作出预测,我们的方法可以实现准确的预测,达到78 %的准确值,甚至缺失样本。此外,我们的实验显示,如果提议的框架的50 %的精确度,那么在样本中,则有比86 %的准确性特性。