Publication

Soft, wireless, technology-enhanced-dreaming patch for at-home sleep monitoring

Soo — Mon, 06 Apr 2026 14:15:34 +0900

Here, we report Tedream (technology-enhanced-dreaming), a soft and wireless patch system designed for at-home sleep monitoring without the need for clinical supervision or wired lab equipment. Using a set of three wearable devices mounted on the forehead, chest, and forearm, the platform measures physiological signals to perform sleep staging and detect sleep apnea. Clinical evaluations demonstrate 80% accuracy compared to traditional polysomnography, offering a reliable and biocompatible solution for the widespread screening and management of sleep disorders.

Flex. Print. Electron., 11, 015013, 2026

Continuous real-time assessment of acute cognitive stress from cardiac mechanical signals captured by a skin-like patch

Soo — Wed, 12 Feb 2025 16:49:52 +0900

Here, we introduce a skin-like, wireless sternal patch that captures changes in cardiac mechanics due to stress manifesting in the seismocardiogram (SCG) signals. Judicious optimization of the device’s micro-structured interconnections and elastomer integration yields a device that sufficiently matches the skin’s mechanics, robustly yet gently adheres to the skin without aggressive tapes, and captures planar and longitudinal SCG waves well.

Biosensors and Bioelectronics, 248, 115983, 2024

Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics

Soo — Wed, 12 Feb 2025 16:38:59 +0900

Here, this study reports a wireless multimodal wearable system with machine learning for automatic, accurate clinical assessment of swallowing behavior and diagnosis of silent aspirations from dysphagia patients. The device includes a kirigami-structured electrode that suppresses changes in skin contact impedance caused by movements and a microphone with a gel layer that effectively blocks external noise for measuring high-quality electromyograms and swallowing sounds. The deep learning algorithm offers the classification of swallowing patterns while diagnosing silent aspirations, with an accuracy of 89.47%

Advanced Science, 2404211, 2024

All-in-One, Wireless, Multi-Sensor Integrated Athlete Health Monitor for Real-Time Continuous Detection of Dehydration and Physiological Stress

Soo — Wed, 12 Feb 2025 16:03:22 +0900

Here, an all-in-one, multi-sensor integrated wearable system utilizing a set of nanomembrane soft sensors and electronics, enabling wireless, real-time, continuous monitoring of saliva osmolality, skin temperature, and heart functions is introduced. This system, using a soft patch and a sensor-integrated mouthguard, provides comprehensive monitoring of an athlete's hydration and physiological stress levels.

Advanced Science, 2403238, 2024

Soft wireless sternal patch to detect systemic vasoconstriction using photoplethysmography

Soo — Wed, 12 Feb 2025 14:20:28 +0900

Here, we report a wireless, fully integrated, soft sternal patch to capture PPG signals from the sternum, an anatomical region that exhibits a robust vasoconstrictive response. With healthy controls, the device is highly capable of detecting vasoconstriction induced endogenously and exogenously. Furthermore, in overnight trials with patients with sleep apnea, the device shows a high agreement (r2 = 0.74) in vasoconstriction detection with a commercial system, demonstrating its potential use in portable, continuous, long-term vasoconstriction monitoring.

iScience, 26, 3, 106184, 2023

Fully portable continuous real-time auscultation with a soft wearable stethoscope designed for automated disease diagnosis

wp — Fri, 05 Jan 2024 13:46:41 +0900

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.

Science Advances, 8, eabo5867, 2022.

At-home wireless sleep monitoring patches for the clinical assessment of sleep quality and sleep apnea

wp — Fri, 05 Jan 2024 13:44:59 +0900

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.

Science Advances, 9, eadg9671, 2023