Considerations For Using Fitness Trackers In Psychophysiology Research

Keywords

Fitness tracker; Microsoft band; Optical heart rate; Photoplethysmography; Psychophysiology; Sensors; Wearables

Abstract

Wrist worn fitness trackers have become ubiquitous in recent years. Economies of scale have drastically reduced the cost of these devices while concurrent advances in technology have expanded their physiological recording capabilities. These devices now contain numerous sensors capable of monitoring and collecting various physiological attributes. Additionally, some of these devices provide access to the application programming interface (API), allowing researchers direct access to the data. The use of these devices offers a wide-ranging benefit to the scientific research community. However, there are several factors to consider when selecting a fitness tracker for use in research. Data rights, data protection, and data quality are all important considerations that must be addressed. In addition, other factors, such as sensor types, capabilities, and sampling rates, can directly affect the utility of a wearable device for use in research. In this paper, the Microsoft Band 2 fitness tracker was selected to evaluate participant mental workload during task performance in a simulated nuclear nower plant (NPP) Main Control Room (MCR) as well as training effectiveness in UH-60A/L simulated environments. The Microsoft Band 2 fitness tracker was selected specifically for its optical hear rate sensor, API access to RR intervals (interval between two continuous heartbeats), and direct access to real-time streaming data from the device. To validate the utility of using the Microsoft Band 2 fitness trackers in scientific research, the RR interval and heart rate sensor readings need to be directly compared to FDA medical approved sensor readings. This paper discusses considerations when using a fitness tracker for psychophysiology research and compares data collected from the Microsoft Band 2 to two different FDA approved medical grade ECG devices.

Publication Date

1-1-2017

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

10273 LNCS

Number of Pages

598-606

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-319-58521-5_47

Socpus ID

85025122145 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85025122145

This document is currently not available here.

Share

COinS