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|Title:||Workload management through glanceable feedback: The role of heart rate variability||Authors:||Muñoz, John Edison
|Keywords:||Glanceable;Heart rate variability (HRV);Photoplethismography (PPG);SDNN Index;Smartwatch;Workload||Category:||Computer and Information Sciences||Field:||Natural Sciences||Issue Date:||Sep-2016||Publisher:||Institute of Electrical and Electronics Engineers Inc.||Source:||18th International Conference on e-Health Networking, Applications and Services, 2016, 14-17 September, Munich, Germany||Conference:||18th International Conference on e-Health Networking, Applications and Services||Abstract:||The active monitoring of workload levels has been found to significantly reduce work-related stress. Heart rate and heart rate variability (HRV) measurements via photoplethysmography (PPG) sensors have shown a strong potential to accurately describe daily workload levels. However, due its complexity, HRV is commonly misunderstood and the associated measurements are rarely incorporated for workload monitoring in novel technological devices such as smartwatches and activity trackers. In this paper we explore the potential of consumer-grade smartwatches, equipped with PPG sensors, to assist in the active monitoring of workload during work hours. We develop a prototype that employs the SDNN index, a powerful HRV marker for cardiac resilience to differentiate between high and low workload levels along the work day, and presents feedback in glanceable form, by highlighting workload levels and physical activity over the past hour in 5-minutes blocks at the periphery of the smartwatch. A field study with 9 participants and 3 variations of our prototype attempts to quantify the impact of the HRV feedback over subjective and objective workload as well as users' engagement with the smartwatch. Results showed workload levels as inferred from the PPG sensor to positively correlate with self-reported workload and HRV feedback to result to lower levels of workload as compared to a conventional activity tracker. Moreover, users engaged more frequently with the smartwatch when HRV feedback was presented, than when only physical activity feedback was provided. The results suggest that HRV as inferred from PPG sensors in wearables can effectively be used to monitor workload levels during work hours.||URI:||http://ktisis.cut.ac.cy/handle/10488/13279||DOI:||10.1109/HealthCom.2016.7749477||Rights:||© 2016 IEEE.||Type:||Conference Papers|
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια/Conference papers|
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