Blood glucose monitoring is an essential aspect of disease management for individuals with diabetes. Unfortunately, traditional methods require collecting a blood sample and thus are invasive and inconvenient. Recent developments in minimally invasive continuous glucose monitors have provided a more convenient alternative for people with diabetes to track their glucose levels 24/7. Despite this progress, many challenges remain to establish a noninvasive monitoring technique that works accurately and reliably in the wild. This review encompasses the current advancements in noninvasive glucose sensing technology in vivo, delves into the common challenges faced by these systems, and offers an insightful outlook on existing and future solutions.

Kangaroo Mother Care (KMC), involving chest-to-chest skin contact between an infant and caregiver, is proven to be an effective intervention for preterm and full-term infants. Accurate monitoring of KMC duration and infant's vital signs during KMC is clinically important. Existing monitoring methods, however, rely on manual efforts and require rigid sensors or wires/electrodes on the infant's body. We propose Joey, a fabric-based approach to continuously monitor KMC duration and two vital signs essential to an infant's well-being: heart rate and respiration rate. Joey is a soft fabric necklace worn by the caregiver. It leverages the transmission of electrocardiogram (ECG) signals across individuals during skin-to-skin contact. With a minimalist fabric sensor structure, Joey measures KMC duration via the presence of mixed ECG signals. It then isolates the infant's ECG from this mixture with a proposed signal extraction algorithm and employs a diffusion-based denoising model to mitigate motion artifacts, enabling reliable inference of infant's vital signs. We fabricate Joey prototypes with off-the-shelf hardware and evaluate its performance with user studies. Results demonstrate that Joey achieves an average F1 score of 96% for KMC duration measurement, and clinically-acceptable accuracy in infant's vital sign estimation with a mean absolute error of 2.3 beats per minute and 2.9 breaths per minute in estimating heart rate and respiration rate. Clinical interviews further confirm the usability of Joey's sensing fabric for infant skin. A demonstration video of Joey is available at: mobilex.cs.columbia.edu/joey

Diabetes affects millions in the US, causing elevated blood glucose levels that could lead to complications like kidney failure and heart disease. Recent development of continuous glucose monitors has enabled a minimally invasive option, but the discomfort and social factors highlight the need for noninvasive alternatives in diabetes management. We propose a portable noninvasive glucose sensing system based on the glucose’s optical activity property which rotates linearly polarized light depending on its concentration level. To enable a portable form factor, a light trap mechanism is used to capture unwanted specular reflection from the palm and the enclosure itself. We fabricate four sensing prototypes and conduct a 363-day multi-session clinical evaluation in real-world settings. 30 participants are provided with a prototype for a 5-day home monitoring study, collecting on average 8 data points per day. We identify the error caused by differences between the sensing boxes and the participants’ improper usage. We utilize a machine learning pipeline together with Bayesian Ridge Regressor models and multiple-step data processing techniques to deal with the noisy data. Over 95% of the predictions fall within Zone A (clinically accurate) or B (clinically acceptable) of the Consensus Error Grid with a 0.24 mean absolute relative differences.

Sleep is a vital physiological state that significantly impacts overall health. Continuous monitoring of sleep posture, heart rate, respiratory rate, and body movement is crucial for diagnosing and managing sleep disorders. Current monitoring solutions often disrupt natural sleep due to discomfort or raise privacy and instrumentation concerns. We introduce PillowSense, a fabric-based sleep monitoring system seamlessly integrated into a pillowcase. PillowSense utilizes a dual-layer fabric design. The top layer comprises conductive fabrics for sensing electrocardiogram (ECG) and surface electromyogram (sEMG), while the bottom layer features pressure-sensitive fabrics to monitor sleep location and movement. The system processes ECG and sEMG signals sequentially to infer multiple sleep variables and incorporates an adversarial neural network to enhance posture classification accuracy. We fabricate prototypes using off-the-shelf hardware and conduct both lab-based and in-the-wild longitudinal user studies to evaluate the system's effectiveness. Across 151 nights and 912.2 hours of real-world sleep data, the system achieves an F1 score of 88% for classifying seven sleep postures, and clinically-acceptable accuracy in vital sign monitoring. PillowSense's comfort, washability, and robustness in multi-user scenarios underscore its potential for unobtrusive, large-scale sleep monitoring.

Previous work by Li et al.[5] has shown promising results in sensing blood glucose levels using polarized light. In this paper, we miniaturize the glucose sensing prototype into a portable device. The process of prototype miniaturization has revealed challenges in dealing with specular reflected light caused by multiple reflections within the sensing box. We propose a new structural design that traps the light inside a chamber. Our preliminary results with a single user without diabetes over 5 days showed a 9.5% mean absolute relative difference (MARD). For this participant, 92% of our results are clinically accurate (within Zone A) and the remaining are clinically acceptable (within Zone B) of the Clarke Error Grid.

Loose-fitting clothing is an ideal form factor for unobtrusive electrocardiogram (ECG) monitoring but has long been deemed unviable due to severe motion artifacts caused by fabric shifting. We present a hardware-software co-design that enables continuous cardiac tracking within a standard, loose t-shirt. Physically, the shirt's weight presses a thin sensor against the skin, trapping perspiration to form a stable fluidic anchor via capillary pull and negative pressure, while a structural kirigami pattern mechanically absorbs lateral shear. Computationally, a hybrid electrode simultaneously captures ECG and capacitive micro-motion variances to adaptively filter noise during activity. In a pilot study with six participants, the system achieved a 92.7% full waveform detection rate at rest (r = 0.89) and an 84.5% R-peak detection rate during walking with a 4.58 bpm heart rate mean absolute error. Participants preferred the shirt over commercial patches and chest straps, demonstrating the feasibility of continuous ECG monitoring in a loose-fitting shirt.