INTRODUCTION
Current wearables that collect heart rate and acceleration were not designed for children and/or do not allow access to raw signals, making them fundamentally unverifiable. This study describes the creation and calibration of an open-source multichannel platform (PATCH) designed to measure heart rate and acceleration in children ages 3-8 years.


METHODS
Children (N = 63; Mean age = 6.3) participated in a 45-minute protocol ranging in intensities from sedentary to vigorous activity. Actiheart-5 was used as a comparison measure. We calculated mean bias, mean absolute error (MAE) mean absolute percent error (MA%E), Pearson correlations and Lin's concordance correlation coefficient (CCC).


RESULTS
Mean bias between PATCH and Actiheart heart rate was 2.26 BPM, MAE was 6.67 BPM and M%E was 5.99%. The correlation between PATCH and Actiheart HR was .89 and CCC was .88. For acceleration, mean bias was 1.16 mg and MAE was 12.24 mg. The correlation between PATCH and Actiheart was .96 and CCC was .95.


CONCLUSIONS
The PATCH demonstrated clinically acceptable accuracies to measure heart rate and acceleration compared to a research grade device.

Development and Calibration of a PATCH Device for Monitoring Children's Heart Rate and Acceleration.

Mobile devices (e.g., tablets and smartphones) have been rapidly integrated into the lives of children and have impacted how children engage with digital media. The portability of these devices allows for sporadic, on-demand interaction, reducing the accuracy of self-report estimates of mobile device use. Passive sensing applications objectively monitor time spent on a given device but are unable to identify who is using the device, a significant limitation in child screen time research. Behavioral biometric authentication, using embedded mobile device sensors to continuously authenticate users, could be applied to address this limitation. This study examined the preliminary accuracy of machine learning models trained on iPad sensor data to identify the unique user of the device in a sample of children ages 6 to 11. Data was collected opportunistically from nine participants (8.2 ± 1.75 years, 5 female) in the sedentary portion of two semistructured physical activity protocols. SensorLog was downloaded onto study iPads and collected data from the accelerometer, gyroscope, and magnetometer sensors while the participant interacted with the iPad. Five machine learning models, logistic regression (LR), support vector machine, neural net (NN), k-nearest neighbors (k-NN), and random forest (RF), were trained using 57 features generated from the sensor output to perform multiclass classification. A train-test split of 80%–20% was used for model fitting. Model performance was evaluated using <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>F</mml:mi><mml:mn>1</mml:mn></mml:math> score, accuracy, precision, and recall. Model performance was high, with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>F</mml:mi><mml:mn>1</mml:mn></mml:math> scores ranging from 0.75 to 0.94. RF and k-NN had the highest performance across metrics, with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>F</mml:mi><mml:mn>1</mml:mn></mml:math> scores of 0.94 for both models. This study highlights the potential of using existing mobile device sensors to continuously identify the user of a device in the context of screen time measurement. Future research should explore the performance of this technology in larger samples of children and in free-living environments. 

Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept Study

Abstract Study Objectives Evaluate wrist-placed accelerometry predicted heartrate compared to electrocardiogram (ECG) heartrate in children during sleep. Methods Children (n=82, 61% male, 43.9% Black) wore a wrist-placed Apple Watch Series 7 (AWS7) and ActiGraph GT9X during a polysomnogram. 3-Axis accelerometry data was extracted from AWS7 and the GT9X. Accelerometry heartrate estimates were derived from jerk (the rate of acceleration change), computed using the peak magnitude frequency in short time Fourier Transforms of Hilbert transformed jerk computed from acceleration magnitude. Heartrates from ECG traces were estimated from R-R intervals using R-pulse detection. Lin’s Concordance Correlation Coefficient (CCC), mean absolute error (MAE) and mean absolute percent error (MAPE) assessed agreement with ECG estimated heartrate. Secondary analyses explored agreement by polysomnography sleep stage and a signal quality metric. Results The developed scripts are available on Github. For the GT9X, CCC was poor at -0.11 and MAE and MAPE were high at 16.8 (SD=14.2) beats/minute and 20.4% (SD=18.5%). For AWS7, CCC was moderate at 0.61 while MAE and MAPE were lower at 6.4 (SD=9.9) beats/minute and 7.3% (SD=10.3%). Accelerometry estimated heartrate for AWS7 was more closely related to ECG heartrate during N2, N3 and REM sleep than lights on, wake, and N1 and when signal quality was high. These patterns were not evident for the GT9X. Conclusions Raw accelerometry data extracted from AWS7, but not the GT9X, can be used to estimate heartrate in children while they sleep. Future work is needed to explore the sources (i.e., hardware, software, etc.) of the GT9X’s poor performance. 

Jerks are Useful: Extracting pulse rate from wrist-placed accelerometry jerk during sleep in children.

Photoplethysmography (PPG) signal quality as a proxy for accuracy in heart rate (HR) measurement is useful in various public health contexts, ranging from short-term clinical diagnostics to free-living health behavior surveillance studies that inform public health policy. Each context has a different tolerance for acceptable signal quality, and it is reductive to expect a single threshold to meet the needs across all contexts. In this study, we propose two different metrics as sliding scales of PPG signal quality and assess their association with accuracy of HR measures compared to a ground truth electrocardiogram (ECG) measurement. Methods: We used two publicly available PPG datasets (BUT PPG and Troika) to test if our signal quality metrics could identify poor signal quality compared to gold standard visual inspection. To aid interpretation of the sliding scale metrics, we used ROC curves and Kappa values to calculate guideline cut points and evaluate agreement, respectively. We then used the Troika dataset and an original dataset of PPG data collected from the chest to examine the association between continuous metrics of signal quality and HR accuracy. PPG-based HR estimates were compared with reference HR estimates using the mean absolute error (MAE) and the root-mean-square error (RMSE). Point biserial correlations were used to examine the association between binary signal quality and HR error metrics (MAE and RMSE). Results: ROC analysis from the BUT PPG data revealed that the AUC was 0.758 (95% CI 0.624 to 0.892) for signal quality metrics of STD-width and 0.741 (95% CI 0.589 to 0.883) for self-consistency. There was a significant correlation between criterion poor signal quality and signal quality metrics in both Troika and originally collected data. Signal quality was highly correlated with HR accuracy (MAE and RMSE, respectively) between PPG and ground truth ECG. Conclusion: This proof-of-concept work demonstrates an effective approach for assessing signal quality and demonstrates the effect of poor signal quality on HR measurement. Our continuous signal quality metrics allow estimations of uncertainties in other emergent metrics, such as energy expenditure that relies on multiple independent biometrics. This open-source approach increases the availability and applicability of our work in public health settings.

/pdf/a-sliding-scale-signal-quality-metric-of-5qh30svu.pdf

A Sliding Scale Signal Quality Metric of Photoplethysmography Applicable to Measuring Heart Rate across Clinical Contexts with Chest Mounting as a Case Study

Photoplethysmography (PPG) is a viable, low-cost method of measuring heart rate (HR) and can be collected anywhere there is blood flow. PPG is most frequently used to measure HR from wrist-based monitors. However, wrist-based monitors may be distracting for children, and alternative placements, such as the chest, may be less obtrusive. PURPOSE: The current study aimed to examine the accuracy of children’s HR estimates derived from chest-placed PPG. METHODS: Forty-seven children ages 3-8 (mean = 5.4 ± 1.7 years) completed a 40-minute protocol while wearing a chest-placed PPG monitor and a research-grade HR monitor (Actiheart 5, criterion). The protocol consisted of the following activities: 10 minutes laying supine, 15 minutes of sitting, 5 minutes of standing, 5 minutes of walking, and 5 minutes of running. Error in HR between chest-placed PPG and the Actiheart were summarized using mean absolute error (MAE), root mean square error (RMSE) and mean percent error (M%E). Errors are presented as median (inner quartile range [IQR]). One-way ANOVA was used to examine differences in errors by protocol activity type. RESULTS: The overall correlation between the Actiheart and chest-placed PPG was r = 0.85 (95%CI .85-.86). Across all 47 participant trials, the median MAE was 4.1 beats per minute (bpm) (IQR 2.4-7.6), median RMSE was 5.4 bpm (IQR 3.8-7.2), and median M%E was 3.8% (IQR 2.3%-7.0%). During the running portion of the protocol MAE (mean = 23.7 ± 22.7) and RMSE (mean = 26.7 ± 18.4), were significantly higher compared to the other activities p < .05, but M%E was not (mean = 17.8% ± 27%), p = .24. Overall, 75% of the chest-placed PPG values were within 5% of the Actiheart, 12% within 5% to 10%, 7% within 10% to 20%, and the remaining 5% within 20% or more of the Actiheart. CONCLUSIONS: For children, chest-placed PPG is accurate within the clinically acceptable 5 bpm range. HR monitoring using chest-placed PPG may be a viable alternative to wrist-based estimation in children. Next steps include refining chest-placed PPG to increase its accuracy during high intensity activities such as running and testing the accuracy of HR estimation in a free-living setting. Supported by NIH Grant 5R21DK131387 

Accuracy Of Chest Placed Photoplethysmography In Measuring Heart Rate In Children

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A Sliding Scale Signal Quality Metric of Photoplethysmography Applicable to Measuring Heart Rate across Clinical Contexts with Chest Mounting as a Case Study

Development and Calibration of a PATCH Device for Monitoring Children's Heart Rate and Acceleration.

Accuracy Of Chest Placed Photoplethysmography In Measuring Heart Rate In Children