Long Short-Term Network Based Unobtrusive Perceived Workload Monitoring with Consumer Grade Smartwatches in the Wild
This addresses the problem of unobtrusive workload monitoring for individuals in daily life, but it is incremental as it applies deep learning to an existing domain with consumer devices.
The paper tackled continuous perceived workload monitoring in real-world settings using consumer-grade smartwatches, showing that a Long Short-Term Memory Network outperforms traditional classifiers in discriminating low and high workload.
Continuous high perceived workload has a negative impact on the individual's well-being. Prior works focused on detecting the workload with medical-grade wearable systems in the restricted settings, and the effect of applying deep learning techniques for perceived workload detection in the wild settings is not investigated. We present an unobtrusive, comfortable, pervasive and affordable Long Short-Term Memory Network based continuous workload monitoring system based on a smartwatch application that monitors the perceived workload of individuals in the wild. We make use of modern consumer-grade smartwatches. We have recorded physiological data from daily life with perceived workload questionnaires from subjects in their real-life environments over a month. The model was trained and evaluated with the daily-life physiological data coming from different days which makes it robust to daily changes in the heart rate variability, that we use with accelerometer features to asses low and high workload. Our system has the capability of removing motion-related artifacts and detecting perceived workload by using traditional and deep classifiers. We discussed the problems related to in the wild applications with the consumer-grade smartwatches. We showed that Long Short-Term Memory Network outperforms traditional classifiers on discrimination of low and high workload with smartwatches in the wild.