Montesinos Victoriano, Dell'Agnola Fabio, Arza Adriana, Aminifar Amir, Atienza David
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:2196-2201. doi: 10.1109/EMBC.2019.8857130.
Monitoring stress and, in general, emotions has attracted a lot of attention over the past few decades. Stress monitoring has many applications, including high-risk missions and surgical procedures as well as mental/emotional health monitoring. In this paper, we evaluate the possibility of stress and emotion monitoring using off-the-shelf wearable sensors. To this aim, we propose a multi-modal machine-learning technique for acute stress episodes detection, by fusing the information careered in several biosignals and wearable sensors. Furthermore, we investigate the contribution of each wearable sensor in stress detection and demonstrate the possibility of acute stress recognition using wearable devices. In particular, we acquire the physiological signals using the Shimmer3 ECG Unit and the Empatica E4 wristband. Our experimental evaluation shows that it is possible to detect acute stress episodes with an accuracy of 84.13%, for an unseen test set, using multi-modal machinelearning and sensor-fusion techniques.
在过去几十年里，监测压力以及一般意义上的情绪受到了广泛关注。压力监测有许多应用，包括高风险任务、外科手术以及心理/情绪健康监测。在本文中，我们评估了使用现成的可穿戴传感器进行压力和情绪监测的可能性。为此，我们提出了一种多模态机器学习技术，用于通过融合多种生物信号和可穿戴传感器中携带的信息来检测急性应激事件。此外，我们研究了每个可穿戴传感器在压力检测中的贡献，并展示了使用可穿戴设备进行急性应激识别的可能性。具体而言，我们使用Shimmer3心电图单元和Empatica E4腕带获取生理信号。我们的实验评估表明，使用多模态机器学习和传感器融合技术，对于一个未见过的测试集，能够以84.13%的准确率检测急性应激事件。
