Long Short-Term Memory Network Based Unobtrusive Workload Monitoring With Consumer Grade Smartwatches

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 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 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 assess low and high workload. Our system has the capability of 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 with feature extraction outperforms traditional classifiers and Convolutional Neural Networks on discrimination of low and high perceived workload with smartwatches in the wild.