Islam, B.; McElwain, N.L.; Li, J.; Davila, M.I.; Hu, Y.; Hu, K.; Bodway, J.M.; Dhekne, A.; Roy Choudhury, R.; Hasegawa-Johnson, M. Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations. Sensors2024, 24, 901.
Islam, B.; McElwain, N.L.; Li, J.; Davila, M.I.; Hu, Y.; Hu, K.; Bodway, J.M.; Dhekne, A.; Roy Choudhury, R.; Hasegawa-Johnson, M. Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations. Sensors 2024, 24, 901.
Islam, B.; McElwain, N.L.; Li, J.; Davila, M.I.; Hu, Y.; Hu, K.; Bodway, J.M.; Dhekne, A.; Roy Choudhury, R.; Hasegawa-Johnson, M. Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations. Sensors2024, 24, 901.
Islam, B.; McElwain, N.L.; Li, J.; Davila, M.I.; Hu, Y.; Hu, K.; Bodway, J.M.; Dhekne, A.; Roy Choudhury, R.; Hasegawa-Johnson, M. Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations. Sensors 2024, 24, 901.
Abstract
Background: The use of wearable devices has burgeoned over the past decade, including wearables for infants and young children. Typically, such devices assess a single modality, and few have undergone scientific validation. To address this gap, we developed an infant wearable platform, LittleBeats™, that integrates electrocardiogram (ECG), motion, and audio sensors on a single printed circuit board to permit daylong remote assessments of infants and caregivers in home environments.
Objective: Complementing our prior reports focused on LittleBeats™ data collected among infants and children in the home context, our main objective here is to present a technical validation of each sensor modality against established laboratory protocols and gold-standard equipment.
Materials and Methods: We conducted five studies, including assessments of (a) interbeat interval (IBI) data obtained from the LittleBeats™ ECG sensor versus the gold-standard BIOPAC among adults (Study 1, N=16) and infants (Study 2, N=5), (b) performance of automated activity recognition (upright vs. walk vs. glide vs. squat ) among adults using accelerometer data obtained from LittleBeats™ versus Google Pixel 1 smartphone (Study 3, N=12), and (c) performance of speech emotion recognition (SER; Study 4, N=8) and automatic speech recognition (ASR; Study 5, N =12) algorithms among adult samples using audio data from LittleBeats™ versus smartphone.
Results: Results for IBI data obtained from the LittleBeats™ ECG sensor indicate acceptable mean absolute percent error (MAPE) rates for both the adults (MAPE = 5.29% to 5.97%; Study 1) and infants (MAPE =0.96% to 1.66%; Study 2) across low- and high-challenge sessions, as well as expected patterns of change in respiratory sinus arrythmia (RSA) across sessions. For activity recognition (Study 3), LittleBeats™ data showed good to excellent performance (Accuracy = 89%, F1-score=88%; Cohen’s kappa =.79), although the smartphone outperformed LittleBeats™ by less than 4%. Finally, performance on the SER applied to LittleBeats™ versus smartphone audio data (Study 4) indicated comparable performance, with a matched-pairs test indicating no significant difference in error rates (p=.26). On the ASR task (Study 5), the best performing algorithm yielded relatively low word error rates, although LittleBeats™ (4.16%) versus smartphone (2.73%) error rates are somewhat higher.
Discussion: Taken together, results from these controlled laboratory studies conducted predominantly with adults indicate that the LittleBeats™ sensors yield data quality that is largely comparable to those obtained from gold-standard devices and protocols that have been used extensively in prior research. The advantages of the LittleBeats™ platform is the (a) integration of multiple sensors into one platform that was (b) designed specifically for use with infants and young children. Leveraging data from the multiple modalities and fine-tuning postprocessing steps to further increase data quality, combined with assessing data quality among infants and young children in their natural environments, will be key next steps in this research program.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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