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Continuous high perceived workload has a negative impact on the individual'swell-being. Prior works focused on detecting the workload with medical-gradewearable systems in the restricted settings, and the effect of applying deeplearning techniques for perceived workload detection in the wild settings isnot investigated. We present an unobtrusive, comfortable, pervasive andaffordable Long Short-Term Memory Network based continuous workload monitoringsystem based on a smartwatch application that monitors the perceived workloadof individuals in the wild. We make use of modern consumer-grade smartwatches.We have recorded physiological data from daily life with perceived workloadquestionnaires from subjects in their real-life environments over a month. Themodel was trained and evaluated with the daily-life physiological data comingfrom different days which makes it robust to daily changes in the heart ratevariability, that we use with accelerometer features to asses low and highworkload. Our system has the capability of removing motion-related artifactsand detecting perceived workload by using traditional and deep classifiers. Wediscussed the problems related to in the wild applications with theconsumer-grade smartwatches. We showed that Long Short-Term Memory Networkoutperforms traditional classifiers on discrimination of low and high workloadwith smartwatches in the wild.

Long Short-Term Network Based Unobtrusive Perceived Workload Monitoring with Consumer Grade Smartwatches in the Wild