Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring: Part 1 wearable technology

Grooby, Ethan; Sitaula, Chiranjibi; Kwok, T'ng Chang; Sharkey, Don; Marzbanrad, Faezeh; Malhotra, Atul

doi:10.1038/s41390-022-02416-x

Cited by 9 publications

(4 citation statements)

References 96 publications

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“…Commercially available infant wearables designed for home use that monitor physiological signals tend to lack rigorous validation (see [20,27]). Further, with respect to cardiac monitoring specifically, the quality signal of ECG makes it the gold standard compared with more noisy sensor signals used in wearables (e.g., phonocardiogram, photoplethysmography [28]). These latter sensors yield limited or gross cardiac measures (e.g., heart rate), whereas ECG data can capture a greater array of indices (e.g., cardiac vagal tone [29], which is assessed through R-R peak detection).…”

Section: Contribution Of the Littlebeats™ Platformmentioning

confidence: 99%

Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Islam,

McElwain,

et al. 2024

Sensors

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Across five studies, we present the preliminary technical validation of an infant-wearable platform, LittleBeats™, that integrates electrocardiogram (ECG), inertial measurement unit (IMU), and audio sensors. Each sensor modality is validated against data from gold-standard equipment using established algorithms and laboratory tasks. Interbeat interval (IBI) data obtained from the LittleBeats™ ECG sensor indicate acceptable mean absolute percent error rates for both adults (Study 1, N = 16) and infants (Study 2, N = 5) across low- and high-challenge sessions and expected patterns of change in respiratory sinus arrythmia (RSA). For automated activity recognition (upright vs. walk vs. glide vs. squat) using accelerometer data from the LittleBeats™ IMU (Study 3, N = 12 adults), performance was good to excellent, with smartphone (industry standard) data outperforming LittleBeats™ by less than 4 percentage points. Speech emotion recognition (Study 4, N = 8 adults) applied to LittleBeats™ versus smartphone audio data indicated a comparable performance, with no significant difference in error rates. On an automatic speech recognition task (Study 5, N = 12 adults), the best performing algorithm yielded relatively low word error rates, although LittleBeats™ (4.16%) versus smartphone (2.73%) error rates were somewhat higher. Together, these validation studies indicate that LittleBeats™ sensors yield a data quality that is largely comparable to those obtained from gold-standard devices and established protocols used in prior research.

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Section: Contribution Of the Littlebeats™ Platformmentioning

confidence: 99%

Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Islam,

McElwain,

et al. 2024

Sensors

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“…Video monitoring using face ROIs has received increasing interest for infant cardio-respiratory monitoring recently [1]- [3]. Cardio-respiratory monitoring is crucial for early detection, prediction of prognosis, and continued monitoring of major complications during the neonatal period [4]. It assists clinicians in providing timely and appropriate care to possibly minimise morbidity and mortality [4].…”

Section: Introductionmentioning

confidence: 99%

Neonatal Face and Facial Landmark Detection from Video Recordings

Grooby¹,

Sitaula²,

Ahani³

et al. 2023

Preprint

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This paper explores automated face and facial landmark detection of neonates, which is an important first step in many video-based neonatal health applications, such as vital sign estimation, pain assessment, sleep-wake classification, and jaundice detection. Utilising three publicly available datasets of neonates in the clinical environment, 366 images (258 subjects) and 89 (66 subjects) were annotated for training and testing, respectively. Transfer learning was applied to two YOLO-based models, with input training images augmented with random horizontal flipping, photo-metric colour distortion, translation and scaling during each training epoch. Additionally, the re-orientation of input images and fusion of trained deep learning models was explored. Our proposed model based on YOLOv7Face outperformed existing methods with a mean average precision of 84.8% for face detection, and a normalised mean error of 0.072 for facial landmark detection. Overall, this will assist in the development of fully automated neonatal health assessment algorithms.Clinical relevance-Accurate face and facial landmark detection provides an automated and non-contact option to assist in video-based neonatal health applications.

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“…For recording cardiac activity and body movement, commercially available wireless options, such as the Gabi Smartcare armband (Gabi Smartcare, Belgium), have been specifically designed for young infants, and more easily meet the criteria for wearability and unobtrusiveness. However, many of these options rely on photoplethysmography, which is prone to noise caused by motion, environmental light, loose contact with the skin, and also poses issues for dark skin (e.g., [ 13 , 96 ]). In many cases, such as the Gabi Smartcare armband (Gabi Smartcare, Belgium), the devices are not validated on infants and young children during active states [ 97 ].…”

Section: Introductionmentioning

confidence: 99%

EgoActive: Integrated Wireless Wearable Sensors for Capturing Infant Egocentric Auditory–Visual Statistics and Autonomic Nervous System Function ‘in the Wild’

Geangu,

Smith,

Mason

et al. 2023

Sensors

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There have been sustained efforts toward using naturalistic methods in developmental science to measure infant behaviors in the real world from an egocentric perspective because statistical regularities in the environment can shape and be shaped by the developing infant. However, there is no user-friendly and unobtrusive technology to densely and reliably sample life in the wild. To address this gap, we present the design, implementation and validation of the EgoActive platform, which addresses limitations of existing wearable technologies for developmental research. EgoActive records the active infants’ egocentric perspective of the world via a miniature wireless head-mounted camera concurrently with their physiological responses to this input via a lightweight, wireless ECG/acceleration sensor. We also provide software tools to facilitate data analyses. Our validation studies showed that the cameras and body sensors performed well. Families also reported that the platform was comfortable, easy to use and operate, and did not interfere with daily activities. The synchronized multimodal data from the EgoActive platform can help tease apart complex processes that are important for child development to further our understanding of areas ranging from executive function to emotion processing and social learning.

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Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring: Part 1 wearable technology

Cited by 9 publications

References 96 publications

Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations

Neonatal Face and Facial Landmark Detection from Video Recordings

EgoActive: Integrated Wireless Wearable Sensors for Capturing Infant Egocentric Auditory–Visual Statistics and Autonomic Nervous System Function ‘in the Wild’

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