Standard clinical care in neonatal and pediatric intensive-care units (NICUs and PICUs, respectively) involves continuous monitoring of vital signs with hard-wired devices that adhere to the skin and, in certain instances, can involve catheter-based pressure sensors inserted into the arteries. These systems entail risks of causing iatrogenic skin injuries, complicating clinical care and impeding skin-to-skin contact between parent and child. Here we present a wireless, non-invasive technology that not only offers measurement equivalency to existing clinical standards for heart rate, respiration rate, temperature and blood oxygenation, but also provides a range of important additional features, as supported by data from pilot clinical studies in both the NICU and PICU. These new modalities include tracking movements and body orientation, quantifying the physiological benefits of skin-to-skin care, capturing acoustic signatures of cardiac activity, recording vocal biomarkers associated with tonality and temporal characteristics of crying and monitoring a reliable surrogate for systolic blood pressure. These platforms have the potential to substantially enhance the quality of neonatal and pediatric critical care.

Although many people suffer from sleep disorders, most are undiagnosed, leading to impairments in health. The existing polysomnography method is not easily accessible; it's costly, burdensome to patients, and requires specialized facilities and personnel. Here, we report an at-home portable system that includes wireless sleep sensors and wearable electronics with embedded machine learning. We also show its application for assessing sleep quality and detecting sleep apnea with multiple patients. Unlike the conventional system using numerous bulky sensors, the soft, all-integrated wearable platform offers natural sleep wherever the user prefers. In a clinical study, the face-mounted patches that detect brain, eye, and muscle signals show comparable performance with polysomnography. When comparing healthy controls to sleep apnea patients, the wearable system can detect obstructive sleep apnea with an accuracy of 88.5%. Furthermore, deep learning offers automated sleep scoring, demonstrating portability, and point-of-care usability. At-home wearable electronics could ensure a promising future supporting portable sleep monitoring and home healthcare.

Modern auscultation, using digital stethoscopes, provides a better solution than conventional methods in sound recording and visualization. However, current digital stethoscopes are too bulky and nonconformal to the skin for continuous auscultation. Moreover, motion artifacts from the rigidity cause friction noise, leading to inaccurate diagnoses. Here, we report a class of technologies that offers real-time, wireless, continuous auscultation using a soft wearable system as a quantitative disease diagnosis tool for various diseases. The soft device can detect continuous cardiopulmonary sounds with minimal noise and classify real-time signal abnormalities. A clinical study with multiple patients and control subjects captures the unique advantage of the wearable auscultation method with embedded machine learning for automated diagnoses of four types of lung diseases: crackle, wheeze, stridor, and rhonchi, with a 95% accuracy. The soft system also demonstrates the potential for a sleep study by detecting disordered breathing for home sleep and apnea detection.

The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper extremities. Here, we introduce an intelligent upper-limb exoskeleton system that utilizes deep learning to predict human intention for strength augmentation. The embedded soft wearable sensors provide sensory feedback by collecting real-time muscle activities, which are simultaneously computed to determine the user’s intended movement. Cloud-based deep learning predicts four upper-limb joint motions with an average accuracy of 96.2% at a 500–550 ms response rate, suggesting that the exoskeleton operates just by human intention. In addition, an array of soft pneumatics assists the intended movements by providing 897 newtons of force while generating a displacement of 87 mm at maximum. The intent-driven exoskeleton can reduce human muscle activities by 3.7 times on average compared to the unassisted exoskeleton.

Achieving large-scale, cost-effective, and reproducible manufacturing of stem cells with the existing devices is challenging. Traditional single-use cell-bag bioreactors, limited by their rigid and single-point sensors, struggle with accuracy and scalability for high-quality cell manufacturing. Here, we introduce a smart bioreactor system that enables multi-spatial sensing for real-time, wireless culture monitoring. This scalable system includes a low-profile, label-free thin-film sensor array and electronics integrated with a flexible cell bag, allowing for simultaneous assessment of culture properties such as pH, dissolved oxygen, glucose, and temperature, to receive real-time feedback for up to 30 days. The experimental results show the accurate monitoring of time-dynamic and spatial variations of stem cells and myoblast cells with adjustable carriers from a plastic dish to a 2-liter cell bag. These advances open up the broad applicability of the smart sensing system for large-scale, lower-cost, reproducible, and high-quality engineered cell manufacturing for broad clinical use.