Physiological data-driven models for motion sickness prediction
利用血容量脉搏、皮肤电活动和颈部肌电图等生理时间序列数据，通过分类算法预测晕动病，二分类准确率达81%，并发现生理数据可提前180秒预测症状。
With advances in autonomous vehicle technology and in-cabin occupant monitoring systems, prediction of motion sickness (MS) has emerged as a key challenge to improve passenger experience. In this paper, a framework for MS prediction is proposed leveraging classification algorithms and timeseries physiological data, including blood volume pulse, electrodermal activity, and neck surface electromyography. The dataset used for model training contains over 1500 min of in-vehicle data, three test conditions, and a range of subject demographics. Model predictions were able to achieve 81% accuracy for binary classification (sick or not sick) and 58% for ternary classification (low, moderate or high sickness). In addition, feature importance analysis identified electrodermal activity and surface electromyography as the most relevant data streams for MS prediction. Finally, the paper analyzed the temporal dependency of physiological data on MS response and found that physiological data can precede a subject's self-reporting of MS by up to 180 s.