On the elliptical trainer without arm levers, only the Apple Watch was accurate ( r c = 0.94). While bicycling, only the Garmin, Apple Watch, and Scosche Rhythm+ had acceptable agreement ( r c > 0.80). On treadmill, all devices performed well ( r c = 0.88–0.93) except the Fitbit Blaze ( r c = 0.76). Scosche Rhythm+ and Fitbit Blaze were less accurate ( r c = 0.75 and r c = 0.67, respectively). ResultsĪcross all exercise conditions, the chest strap monitor (Polar H7) had the best agreement with ECG ( r c = 0.996) followed by the Apple Watch ( r c = 0.92), the TomTom Spark ( r c = 0.83), and the Garmin Forerunner ( r c = 0.81). Agreement between HR measurements was assessed using Lin's concordance correlation coefficient ( r c). For each exercise type, HR was recorded at rest, light, moderate, and vigorous intensity. Each participant underwent HR monitoring with an electrocardiogaphic chest strap monitor (Polar H7), forearm monitor (Scosche Rhythm+), and two randomly assigned wrist-worn HR monitors (Apple Watch, Fitbit Blaze, Garmin Forerunner 235, and TomTom Spark Cardio), one on each wrist. Methodsįifty healthy adult volunteers (mean ± SD age = 38 ± 12 yr, 54% female) completed exercise protocols on a treadmill, a stationary bicycle, and an elliptical trainer (±arm movement). We sought to assess the accuracy of five optically based HR monitors during various types of aerobic exercise. The accuracy of newer, optically based monitors is unconfirmed. The circuit demonstrates a topology that takes advantage of the ECG's characteristics to extract R-wave timings at the chest and the ear in the presence of baseline drift, muscle artifact, and signal clipping.Athletes and members of the public increasingly rely on wearable HR monitors to guide physical activity and training. With 58nW of power consumption, the ECG circuit replaces the traditional instrumentation amplifier, analog-to-digital converter, and signal processor with a single chip solution. While the clinical device uses commercial components, a custom integrated circuit for ECG heartbeat detection is designed with the goal of reducing power consumption and device size. The results demonstrate a linear relationship between the J-wave amplitude and stroke volume, and a linear relationship between the RJ interval and PEP. A clinical test involving hemodynamic maneuvers is performed on 13 subjects. The ear-worn device is wirelessly connected to a computer for real time data recording. Because both head BCG and ECG have low signal-to-noise ratios, cross-correlation is used to statistically extract the RJ interval. When the BCG and the ECG are used together, an electromechanical duration called the RJ interval can be obtained. The ECG is sensed locally near the ear using a single-lead configuration. Ensemble averaging is used to obtain consistent J-wave amplitudes, which are related to stroke volume. The head BCG's principal peaks (J-waves) are synchronized to heartbeats. The source of periodic head movements is identified as a type of BCG, which is measured using an accelerometer. Being a natural anchoring point, the ear is demonstrated as a viable location for the integrated sensing of physiological signals. This work presents a wearable heart monitor at the ear that uses the ballistocardiogram (BCG) and the electrocardiogram (ECG) to extract heart rate, stroke volume, and pre-ejection period (PEP) for the application of continuous heart monitoring.
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