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Exploring physiological data in smartwatches for workload management.

John Muñoz
01st August 2016 - 11:00

The active monitoring of workload levels has been found to significantly reduce work-related stress. Heart rate and heart rate variability (HRV) measurements have shown a strong potential to accurately describe daily workload levels. We develop a prototype that employs HR data and presents feedback in glanceable form, by highlighting workload levels and physical activity over the past hour.  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. 

The results suggest that HRV from wearables can effectively be used to monitor workload levels during work hours. 

Short Bio:

John Muñoz is a PhD student and researcher in the NeuorehabLab of M-iti.

His research has been focused on the development of software tools for processing physiological signals related with Electrocardiography (ECG), Electrodermal Activity (EDA) and Electromyography (EMG) in order to provide relevant features of interest about the human physiological, physical and emotional state. Currently, he is an assistant researcher in the AHA project which aims to promote non-sedentary behaviors through the use of novel serious games for health approaches.  

The work he is going to present is a result for an Independent Study carried out with the Prof. Evangelos Karapanos and it will be presented in the IEEE HealthCom'16.

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