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In this research, we examine the potential of measuring physiological variables, including heart rate (HR) and respiration rate (RR) on the upper arm using a wireless multimodal sensing system consisting of an accelerometer, a gyroscope, a three-wavelength photoplethysmography (PPG), single-sided electrocardiography (SS-ECG), and bioimpedance (BioZ). The study included collecting HR data when the subject was at rest and typing, and RR data when the subject was at rest. The data from three wavelengths of PPG and BioZ were collected and compared to the SS-ECG as the standard. The accelerometer and gyro signals were used to exclude data with excessive noise due to motion. The results showed that when the subject remained sedentary, the mean absolute error (MAE) for the HR calculation for all three wavelengths of the PPG modality was less than two bpm, while the BioZ was 3.5 bpm compared with SS-ECG HR. The MAE for typing increased for both modalities and was less than three bpm for all three wavelengths of the PPG but increased to 7.5 bpm for the BioZ. Regarding RR, both modalities resulted in RR within one breath per minute of the SS-ECG modality for the one breathing rate. Overall, all modalities on this upper arm wearable worked well when the subject was sedentary. Still, the SS-ECG and PPG showed less variability for the HR signal in the presence of motion during micro-motions such as typing. 

Background: Monitoring glucose excursions is important in diabetes management. This can be achieved using continuous glucose monitors (CGMs). However, CGMs are expensive and invasive. Thus, alternative low-cost noninvasive wearable sensors capable of predicting glycemic excursions could be a game changer to manage diabetes. Methods: In this article, we explore two noninvasive sensor modalities, electrocardiograms (ECGs) and accelerometers, collected on five healthy participants over two weeks, to predict both hypoglycemic and hyperglycemic excursions. We extract 29 features encompassing heart rate variability features from the ECG, and time- and frequency-domain features from the accelerometer. We evaluated two machine-learning approaches to predict glycemic excursions: a classification model and a regression model. Results: The best model for both hypoglycemia and hyperglycemia detection was the regression model based on ECG and accelerometer data, yielding 76% sensitivity and specificity for hypoglycemia and 79% sensitivity and specificity for hyperglycemia. This had an improvement of 5% in sensitivity and specificity for both hypoglycemia and hyperglycemia when compared with using ECG data alone. Conclusions: Electrocardiogram is a promising alternative not only to detect hypoglycemia but also to predict hyperglycemia. Supplementing ECG data with contextual information from accelerometer data can improve glucose prediction.