Key result
Lab-trained machine learning improves daily stress detection accuracy versus training on daily life data.
Why the study?
Automatic wearable daily life stress detection systems lack sufficient accuracy when used in unrestricted daily life compared to controlled laboratory conditions.
Training stress detection machine learning models in controlled laboratory settings improves their accuracy when deployed in unrestricted daily life environments.
May guide ML development for ambulatory monitoring; leaves open validation in clinical cardiology cohorts.
Chronic stress leads to poor well-being, and it has effects on life quality and health. Societymay have significant benefits from an automatic daily life stress detection system using unobtrusivewearable devices using physiological signals. However, the performance of these systems is notsufficiently accurate when they are used in unrestricted daily life compared to the systems testedin controlled real-life and laboratory conditions. To test our stress level detection system thatpreprocesses noisy physiological signals, extracts features, and applies machine learning classificationtechniques, we used a laboratory experiment and ecological momentary assessment based datacollection with smartwatches in daily life. We investigated the effect of different labeling techniquesand different training and test environments. In the laboratory environments, we had more controlledsituations, and we could validate the perceived stress from self-reports. When machine learningmodels were trained in the laboratory instead of training them with the data coming from daily life,the accuracy of the system when tested in daily life improved significantly. The subjectivity effectcoming from the self-reports in daily life could be eliminated. Our system obtained higher stresslevel detection accuracy results compared to most of the previous daily life studies.
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Can et al. (2020) studied Stress. Machine learning models trained in the laboratory vs. Models trained with data from daily life was evaluated on Stress level detection accuracy in daily life. Training machine learning models in a laboratory environment significantly improved stress level detection accuracy in daily life compared to training with daily life data.
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