< external_image >
<(From left) Ph.D candidate Youngmin Sim, Ph.D candidate Do Yun Park, Dr. Chanho Park, Professor Kyeongha Kwon>
_%EC%8B%AC%EC%98%81%EB%AF%BC_%EC%84%9D%EB%B0%95%ED%86%B5%ED%95%A9%EA%B3%BC%EC%A0%95._%EB%B0%95%EB%8F%84%EC%9C%A4_%EB%B0%95%EC%82%AC%EA%B3%BC%EC%A0%95,_%EB%B0%95%EC%B0%AC%ED%98%B8_%EB%B0%95%EC%82%AC%ED%9B%84%EC%97%B0%EA%B5%AC%EC%9B%90,_%EA%B6%8C%EA%B2%BD%ED%95%98_%EA%B5%90%EC%88%98.jpg)
_%EC%8B%AC%EC%98%81%EB%AF%BC_%EC%84%9D%EB%B0%95%ED%86%B5%ED%95%A9%EA%B3%BC%EC%A0%95._%EB%B0%95%EB%8F%84%EC%9C%A4_%EB%B0%95%EC%82%AC%EA%B3%BC%EC%A0%95,_%EB%B0%95%EC%B0%AC%ED%98%B8_%EB%B0%95%EC%82%AC%ED%9B%84%EC%97%B0%EA%B5%AC%EC%9B%90,_%EA%B6%8C%EA%B2%BD%ED%95%98_%EA%B5%90%EC%88%98_1.jpg)
Miniaturization and weight reduction of medical wearable devices for continuous health monitor
_%EC%8B%AC%EC%98%81%EB%AF%BC_%EC%84%9D%EB%B0%95%ED%86%B5%ED%95%A9%EA%B3%BC%EC%A0%95._%EB%B0%95%EB%8F%84%EC%9C%A4_%EB%B0%95%EC%82%AC%EA%B3%BC%EC%A0%95,_%EB%B0%95%EC%B0%AC%ED%98%B8_%EB%B0%95%EC%82%AC%ED%9B%84%EC%97%B0%EA%B5%AC%EC%9B%90,_%EA%B6%8C%EA%B2%BD%ED%95%98_%EA%B5%90%EC%88%98_(1).jpg)
ing such as heart rate, blood oxygen saturation, and sweat component analysis remain major challenges. In particular, optical sensors consume a significant amount of power for LED operation and wireless transmission, requiring heavy and bulky batteries. To overcome these limitations, KAIST researchers have developed a next-generation wearable platform that enables 24-hour continuous measurement by using ambient light as an energy source and optimizing power management according to the power environment.
KAIST (President Kwang Hyung Lee) announced on the 30th that Professor Kyeongha Kwon's team from the School of Electrical Engineering, in collaboration with Dr. Chanho Park's team at Northwestern University in the U.S., has developed an adaptive wireless wearable platform that reduces battery load by utilizing ambient light.
To address the battery issue of medical wearable devices, Professor Kyeongha Kwon's research team developed an innovative platform that utilizes ambient natural light as an energy source. This platform integrates three complementary light energy technologies.
< external_image >
The first core technology, the Photometric Method, is a technique that adaptively adjusts LED brightness depending on the intensity of the ambient light source. By combining ambient natural light with LED light to maintain a constant total illumination level, it automatically dims the LED when natural light is strong and brightens it when natural light is weak.
Whereas conventional sensors had to keep the LED on at a fixed brightness regardless of the environment, this technology optimizes LED power in real time according to the surrounding environment. Experimental results showed that it reduced power consumption by as much as 86.22% under sufficient lighting conditions.
The second is the Photovoltaic Method using high-efficiency multijunction solar cells. This goes beyond simple solar power generation to convert light in both indoor and outdoor environments into electricity. In particular, the adaptive power management system automatically switches among 11 different power configurations based on ambient conditions and battery status to achieve optimal energy efficiency.
The third innovative technology is the Photoluminescent Method. By mixing strontium aluminate microparticles* into the sensor's silicone encapsulation structure, light from the surroundings is absorbed and stored during the day and slowly released in the dark. As a result, after being exposed to 500W/m² of sunlight for 10 minutes, continuous measurement is possible for 2.5 minutes even in complete darkness.
*Strontium aluminate microparticles: A photoluminescent material used in glow-in-the-dark paint or safety signs, which absorbs light and emits it in the dark for an extended time.
These three technologies work complementarily—during bright conditions, the first and second methods are active, and in dark conditions, the third method provides additional support—enabling 24-hour continuous operation.
The research team applied this platform to various medical sensors to verify its practicality. The photoplethysmography sensor monitors heart rate and blood oxygen saturation in real time, allowing early detection of cardiovascular diseases. The blue light dosimeter accurately measures blue light, which causes skin aging and damage, and provides personalized skin protection guidance. The sweat analysis sensor uses microfluidic technology to simultaneously analyze salt, glucose, and pH in sweat, enabling real-time detection of dehydration and electrolyte imbalances.
Additionally, introducing in-sensor data computing significantly reduced wireless communication power consumption. Previously, all raw data had to be transmitted externally, but now only the necessary results are calculated and transmitted within the sensor, reducing data transmission requirements from 400B/s to 4B/s—a 100-fold decrease.
To validate performance, the research tested the device on healthy adult subjects in four different environments: bright indoor lighting, dim lighting, infrared lighting, and complete darkness. The results showed measurement accuracy equivalent to that of commercial medical devices in all conditions A mouse model experiment confirmed accurate blood oxygen saturation measurement in hypoxic conditions.
< external_image >
Professor Kyeongha Kwon of KAIST, who led the research, stated, "This technology will enable 24-hour continuous health monitoring, shifting the medical paradigm from treatment-centered to prevention-centered shifting the medical paradigm from treatment-centered to prevention-centered," further stating that "cost savings through early diagnosis as well as strengthened technological competitiveness in the next-generation wearable healthcare market are anticipated."
This research was published on July 1 in the international journal Nature Communications, with Do Yun Park, a doctoral student in the AI Semiconductor Graduate Program, as co–first author.
※ Paper title: Adaptive Electronics for Photovoltaic, Photoluminescent and Photometric Methods in Power Harvesting for Wireless and Wearable Sensors
※ DOI: https://doi.org/10.1038/s41467-025-60911-1
※ URL: https://www.nature.com/articles/s41467-025-60911-1
This research was supported by the National Research Foundation of Korea (Outstanding Young Researcher Program and Regional Innovation Leading Research Center Project), the Ministry of Science and ICT and Institute of Information & Communications Technology Planning & Evaluation (IITP) AI Semiconductor Graduate Program, and the BK FOUR Program (Connected AI Education & Research Program for Industry and Society Innovation, KAIST EE).
