2019/11/30 by Deniz Ekiz, Ekiz, Deniz, Yekta Said Can +3
Computer Science · Engineering · Medicine · #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #Healthcare Technology and Patient Monitoring #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Non-Invasive Vital Sign Monitoring #cs.HC #cs.LG
paper · pdf · doi:10.48550/arxiv.1912.00019
arxiv created 2019/11/30 · openalex publication_date 2019/11/30 · arxiv updated 2019/12/03 · openalex created_date 2019/12/05 · openalex updated_date 2026/07/28
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.