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      In-Ear Pulse Rate Measurement: A Valid Alternative to Heart Rate Derived from Electrocardiography?

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          Abstract

          Heart rate measurement has become one of the most widely used methods of monitoring the intensity of physical activity. The purpose of this study was to assess whether in-ear photoplethysmographic (PPG) pulse rate (PR) measurement devices represent a valid alternative to heart rate derived from electrocardiography (ECG), which is considered a gold standard. Twenty subjects (6 women, 14 men) completed one trial of graded cycling under laboratory conditions. In the trial, PR was recorded by two commercially available in-ear devices, the Dash Pro and the Cosinuss°One. They were compared to HR measured by a Bodyguard2 ECG. Validity of the in-ear PR measurement devices was tested by ANOVA, mean absolute percentage errors (MAPE), intra-class correlation coefficient (ICC), and Bland–Altman plots. Both devices achieved a MAPE ≤5%. Despite excellent to good levels of agreement, Bland–Altman plots showed that both in-ear devices tend to slightly underestimate the ECG’s HR values. It may be concluded that in-ear PPG PR measurement is a promising technique that shows accurate but imprecise results under controlled conditions. However, PPG PR measurement in the ear is sensitive to motion artefacts. Thus, accuracy and precision of the measured PR depend highly on measurement site, stress situation, and exercise.

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          The Optics of Human Skin

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            Accuracy in Wrist-Worn, Sensor-Based Measurements of Heart Rate and Energy Expenditure in a Diverse Cohort

            The ability to measure physical activity through wrist-worn devices provides an opportunity for cardiovascular medicine. However, the accuracy of commercial devices is largely unknown. The aim of this work is to assess the accuracy of seven commercially available wrist-worn devices in estimating heart rate (HR) and energy expenditure (EE) and to propose a wearable sensor evaluation framework. We evaluated the Apple Watch, Basis Peak, Fitbit Surge, Microsoft Band, Mio Alpha 2, PulseOn, and Samsung Gear S2. Participants wore devices while being simultaneously assessed with continuous telemetry and indirect calorimetry while sitting, walking, running, and cycling. Sixty volunteers (29 male, 31 female, age 38 ± 11 years) of diverse age, height, weight, skin tone, and fitness level were selected. Error in HR and EE was computed for each subject/device/activity combination. Devices reported the lowest error for cycling and the highest for walking. Device error was higher for males, greater body mass index, darker skin tone, and walking. Six of the devices achieved a median error for HR below 5% during cycling. No device achieved an error in EE below 20 percent. The Apple Watch achieved the lowest overall error in both HR and EE, while the Samsung Gear S2 reported the highest. In conclusion, most wrist-worn devices adequately measure HR in laboratory-based activities, but poorly estimate EE, suggesting caution in the use of EE measurements as part of health improvement programs. We propose reference standards for the validation of consumer health devices (http://precision.stanford.edu/).
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              Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables

              Background New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects. Objective The objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects, compare common brands in terms of developer access to collected health data, and features to consider when deciding which brand to use in future research. Methods We searched for devices and brand names in six wearable device databases. For each brand, we identified additional devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects. Finally, we investigated developer accessibility to the health data collected by identified brands. Results We identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device. Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as often as other brands. Conclusions The wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently used in research projects, and even less are thoroughly validated.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                21 August 2019
                September 2019
                : 19
                : 17
                : 3641
                Affiliations
                Technical University of Munich, Department of Mechanical Engineering, Professorship of Sport Equipment and Materials, Boltzmannstraße 15, D-85747 Garching, Germany
                Author notes
                [* ]Correspondence: stefanie.passler@ 123456tum.de ; Tel.: +49-89-289-15380; Fax: +49-89-289-15389
                Author information
                https://orcid.org/0000-0003-3685-6700
                Article
                sensors-19-03641
                10.3390/s19173641
                6749408
                31438600
                ddb8e2f0-e0c1-4735-8f56-1f399ad65751
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 10 July 2019
                : 19 August 2019
                Categories
                Article

                Biomedical engineering
                photoplethysmography,heart rate,consumer-wearable devices,in-ear,validation,optical pulse rate monitoring,pulse rate

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