The objective of this work is to evaluate the effectiveness of a wearable physiological stress monitoring system in distinguishing between stressed and non-stressed state in older adults using machine learning techniques. This system utilizes EDA and BVP signal to detect occurrence of stress as indicated by salivary cortisol measurement which is a reliable objective measure of physiological stress. Data of 19 healthy older adults (11 female and 8 male) with mean age 73.15 ± 5.79 were used for this study. EDA and BVP signals were recorded using a finger tip sensor during the Trier Social Stress Test, which is a well known experimental protocol to reliably induce stress in humans in a social setting. 39 statistical measures of the peak characteristic of EDA and BVP signal were extracted. A supervised feature selection algorithm is used to select important features as an input to the machine learning model. Four machine learning algorithms were evaluated based on their performance in classifying between stressed and non-stressed states. Results indicate that the logistic regression performed the best among Random Forest, κ-NN, and Support Vector Machine achieving an macro-average and micro-average f1-score of 0.87 and 0.95 respectively and an AUC score of 0.81. We also evaluated the effectiveness of a novel deep learning Long Short-Term Memory (LSTM) based classifier in distinguishing between stressed and non-stressed state. Results on test data shows that LSTM based classifier achieved an improvement of 6.7% and 2% in terms of macro-average f1-score and micro-average f1-score respectively. Also the AUC score for LSTM classifier is found to be 0.9 which is about 11% higher than the best performing logistic regression model. This work can be used to design a convenient unobtrusive wearable device to monitor stress levels in older adults in their home environment, thereby facilitating aging in place and improving the quality of life.

这项工作的目的是评估使用机器学习技术的可穿戴生理压力监测系统在区分老年人的压力状态和非压力状态方面的有效性。该系统利用EDA和BVP信号来检测压力的发生，如唾液皮质醇测量所表明的那样，这是对生理压力的可靠客观测量。本研究使用19位健康的老年人（11位女性和8位男性）的数据，平均年龄为73.15±5.79。在Trier Social Stress Test（特里尔社交压力测试）期间，使用指尖传感器记录了EDA和BVP信号，该实验是众所周知的实验协议，可以在社交环境中可靠地诱发人的压力。提取了39种EDA和BVP信号峰值特征的统计量。监督的特征选择算法用于选择重要特征作为机器学习模型的输入。根据四种机器学习算法在压力状态和非压力状态之间进行分类的性能，对它们进行了评估。结果表明，逻辑回归在随机森林中表现最好，κ- NN和支持向量机分别实现f1得分的宏观平均和微观平均得分分别为0.87和0.95，AUC得分为0.81。我们还评估了基于新颖的深度学习长期短期记忆（LSTM）的分类器在区分压力状态和非压力状态时的有效性。测试数据的结果表明，基于LSTM的分类器的宏观平均f1-得分和微观平均f1-得分分别提高了6.7％和2％。此外，发现LSTM分类器的AUC得分为0.9，这比性能最好的逻辑回归模型高约11％。这项工作可用于设计方便的，不引人注意的可穿戴设备，以监控老年人在家庭环境中的压力水平，从而促进就地老化并改善生活质量。