Print Email Facebook Twitter Comparison of three different physiological wristband sensor systems and their applicability for resilience-and work load monitoring Title Comparison of three different physiological wristband sensor systems and their applicability for resilience-and work load monitoring Author Binsch, O. Wabeke, T.R. Valk, P.J.L. Publication year 2016 Abstract Leveraging miniaturized sensor and monitoring technology integrated in easy-to-wear wristband wearables represents a great opportunity for advancing Resilience and Mental Health of e.g. employees that experience high workload. Therefore, it is important to gain insights into the reliability of such technology before far reaching conclusions can be drawn and interventions can be developed. To that aim, we tested three wearable wristband sensor systems (Apple Watch, Microsoft Band and Fitbit Surge) and compared the assessed sensor output with a reliable ground truth. The results showed that heart rate, steps and distance varies considerably around the ground truth during tasks that required body movement. However, during the rest condition (sitting on chair) the heart rate was considered more reliable. It is concluded that caution is warranted while using and interpreting physiological data assessed by the new technology, but, in rest (e.g. pauses, sleep) the wearable' sensors could be used to detect undesirable physiological patterns, indicative of threats to resilience or (mental) health. © 2016 IEEE. Subject Human & Operational ModellingTPI - Training & Performance InnovationsELSS - Earth, Life and Social SciencesPerceptionBody sensor networksHealth risksHeartPhysiologyWearable technologyBody movementsGain insightGround truthMental healthMonitoring technologiesPhysiological dataSensor outputSensor systemsWearable sensors To reference this document use: http://resolver.tudelft.nl/uuid:616ed944-999c-4d6c-a6d6-39146bed0ac4 DOI https://doi.org/10.1109/bsn.2016.7516272 TNO identifier 572384 Publisher Institute of Electrical and Electronics Engineers Inc. ISBN 9781509030873 Source 13th Annual Body Sensor Networks Conference, BSN 2016, 14 June 2016 through 17 June 2016, 272-276 Article number 7516272 Document type conference paper Files To receive the publication files, please send an e-mail request to TNO Library.