Introduction. We investigate the generalization ability of models built on datasets containing a small number of subjects, recorded in single study protocols. Next, we propose and evaluate methods combining these datasets into a single, large dataset. Finally, we propose and evaluate the use of ensemble techniques by combining gradient boosting with an artificial neural network to measure predictive power on new, unseen data. Methods. Sensor biomarker data from six public datasets were utilized in this study. To test model generalization, we developed a gradient boosting model trained on one dataset (SWELL), and tested its predictive power on two datasets previously used in other studies (WESAD, NEURO). Next, we merged four small datasets, i.e. (SWELL, NEURO, WESAD, UBFC-Phys), to provide a combined total of 99 subjects,. In addition, we utilized random sampling combined with another dataset (EXAM) to build a larger training dataset consisting of 200 synthesized subjects,. Finally, we developed an ensemble model that combines our gradient boosting model with an artificial neural network, and tested it on two additional, unseen publicly available stress datasets (WESAD and Toadstool). Results. Our method delivers a robust stress measurement system capable of achieving 85% predictive accuracy on new, unseen validation data, achieving a 25% performance improvement over single models trained on small datasets. Conclusion. Models trained on small, single study protocol datasets do not generalize well for use on new, unseen data and lack statistical power. Ma-chine learning models trained on a dataset containing a larger number of varied study subjects capture physiological variance better, resulting in more robust stress detection.
翻译:我们调查了在包含少量主题的数据集上建起的模型的概括能力,该模型记录在单一的研究协议中。接下来,我们提出并评价将这些数据集合并成一个单一的大型数据集的方法。最后,我们提出并评价使用组合式技术的方法,将梯度推动与人工神经网络相结合,以测量新的、隐蔽的数据的预测力。方法。本研究使用了6个公共数据集的传感器生物标记数据。为了测试模型概括性,我们开发了一个梯度推动模型,在一个数据集(SWELL)上培训了一个梯度推动模型,并在其他研究中使用了两个数据集(WESAD, NEURO)上测试了两个数据集的预测力。最后,我们合并了四个小型数据集,即(SWELL、NEURO、WESAD、UBFC-Phys),以提供总共99个科目。此外,我们利用随机抽样结合另一个数据集(EXAM),以建立一个由200个经过综合研究的小型精度改进型模型。最后,我们开发了一个小数级模型模型,用来模拟模型,以整合我们经过训练的、没有经过训练的轨道数据测量的数据的模型,用来推进的模型, 将我们的数据结果与经过更新的精确的模型整合了。