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AI-Driven Wearable Blood Pressure Sensor for Continuous Health Monitoring – Published in Nature Reviews Cardiology
A KAIST research team led by Professor Keon Jae Lee has proposed an innovative theoretical framework and research strategies for AI-based wearable blood pressure sensors, paving the way for continuous and non-invasive cardiovascular monitoring.
Hypertension is a leading chronic disease affecting over a billion people worldwide and is a major risk factor for severe cardiovascular conditions such as myocardial infarction, stroke, and heart failure. Traditional blood pressure measurement relies on intermittent, cuff-based methods, which fail to capture real-time fluctuations and present challenges in continuous patient monitoring.
Wearable blood pressure sensors offer a non-invasive solution for continuous blood pressure monitoring, enabling real-time tracking and personalized cardiovascular health management. However, current technologies lack the accuracy and reliability required for medical applications, limiting their practical use. To address these challenges, advancements in high-sensitivity sensor technology and AI signal processing algorithms are essential.
Building on their previous study in Advanced Materials (doi.org/10.1002/adma.202301627), which validated the clinical feasibility of flexible piezoelectric blood pressure sensors, Professor Lee’s team conducted an in-depth review of the latest advancements in cuffless wearable sensors, focusing on key technical and clinical challenges. Their review highlights clinical aspects of clinical implementation, real-time data transmission, signal quality degradation, and AI algorithm accuracy.
Professor Keon Jae Lee said, “This paper systematically demonstrates the feasibility of medical-grade wearable blood pressure sensors, overcoming what was previously considered an insurmountable challenge. We propose theoretical strategies to address technical barriers, opening new possibilities for future innovations in this field. With continued advancements, we expect these sensors to gain trust and be commercialized soon, significantly improving quality of life.”
This review entitled “Wearable blood pressure sensors for cardiovascular monitoring and machine learning algorithms for blood pressure estimation” was published in the February 18 issue of Nature Reviews Cardiology (Impact Factor: 41.7). (doi.org/10.1038/s41569-025-01127-0)
< Figure 1. Overview of wearable blood pressure sensor technologies for cardiovascular health care >
[Reference] Min S. et al., (2025) “Wearable blood pressure sensors for
cardiovascular monitoring and machine learning algorithms for blood pressure estimation.” Nature Reviews Cardiology
(doi.org/10.1038/s41569-025-01127-0)
[Main Author] Seongwook Min (Korea Advanced Institute of Science and Technology), Jaehun An (Korea Advanced Institute of Science and Technology), Jae Hee Lee (Northwestern University),
* Contact email : Professor Keon Jae Lee (keonlee@kaist.ac.kr)
2025.03.04 View 438 -
KAIST develops a new, bone-like material that strengthens with use in collaboration with GIT
Materials used in apartment buildings, vehicles, and other structures deteriorate over time under repeated loads, leading to failure and breakage. A joint research team from Korea and the United States has successfully developed a bioinspired material that becomes stronger with use, taking inspiration from the way bones synthesize minerals from bodily fluids under stress, increasing bone density.
< (From left) Professor Sung Hoon Kang of the Department of Materials Science and Engineering, Johns Hopkins University Ph.D. candidates Bohan Sun and Grant Kitchen, Professor Yuhang Hu and Ph.D. candidate Dongjung He of Georgia Institute of Technology >
KAIST (represented by President Kwang Hyung Lee) announced on the 20th of February that a research team led by Professor Sung Hoon Kang from the Department of Materials Science and Engineering, in collaboration with Johns Hopkins University and the Georgia Institute of Technology, had developed a new material that strengthens with repeated use, similar to how bones become stronger with exercise.
Professor Kang’s team sought to address the issue of conventional materials degrading with repeated use. Inspired by the biological process where stress triggers cells to form minerals that strengthen bones, the team developed a material that synthesizes minerals under stress without relying on cellular activity. This innovation is expected to enable applications in a variety of fields.
To replace the function of cells, the research team created a porous piezoelectric substrate that converts mechanical force into electricity and actually generates more charge under greater force. They then synthesized a composite material by infusing it with an electrolyte containing mineral components similar to those in blood.
< Figure 1. Schematic diagram of the biomimetic concept based on bone and pitcher plants, the reversible strengthening mechanism, the process of fabricating porous composites, the mechanical property changes with increasing stiffness and energy dissipation after cyclic loading, and the reprogrammable self-folding mechanism and applications >
After subjecting the material to periodic forces and measuring changes in its properties, they observed that its stiffness increased proportionally with the frequency and magnitude of stress and that its energy dissipation capability improved.
The reason for such properties was found to be due to minerals forming inside the porous material under repeated stress, as observed through micro-CT imaging of its internal structure. When subjected to large forces, these minerals fractured and dissipated energy, only to reform under further cyclic stress.
Unlike conventional materials that weaken with repeated use, this new material simultaneously enhances stiffness and impact absorption over time.
< Figure 2. Comparison of the changes in properties of the newly developed new material (LIPPS) with other materials under cyclic loading. (A) Graph showing the relative change rate of energy dissipation after cyclic loading and the relative change rate of elastic modulus upon unloading. LIPPS is in a new area that existing materials have not reached, and shows the characteristics of simultaneous increases in elastic modulus and energy dissipation. (B) Graph comparing the performance of LIPPS with current state-of-the-art mechanically adaptive materials. (Left) The maximum property change rate compared to the baseline after cyclic loading, LIPPS shows much higher changes in elastic modulus, dissipated energy density and ratio, toughness (impact resistance), and stored energy density than the existing adaptive materials. (Right) The absolute value range of the reported properties before and after cyclic loading shows that LIPPS has higher elastic modulus and toughness than the existing adaptive materials. >
Moreover, because its properties improve in proportion to the magnitude and frequency of applied stress, it can self-adjust to achieve mechanical property distributions suitable for different structural applications. It also possesses self-healing capabilities.
Professor Kang stated, "This newly developed material, which strengthens and absorbs impact better with repeated use compared to conventional materials, holds great potential for applications in artificial joints, as well as in aircraft, ships, automobiles, and structural engineering."
This study, with Professor Sung Hoon Kang as the corresponding author, was published in Science Advances (Vol. 11, Issue 6, February).
(Paper title: “A material dynamically enhancing both load-bearing and energy-dissipation capability under cyclic loading”) DOI: 10.1126/sciadv.adt3979
This research was conducted as a joint effort with Johns Hopkins University's Extreme Materials Institute and the Georgia Institute of Technology, supported by the National Research Foundation of Korea’s Brain Pool Plus program.
2025.02.22 View 376 -
KAIST Research Team Develops an AI Framework Capable of Overcoming the Strength-Ductility Dilemma in Additive-manufactured Titanium Alloys
<(From Left) Ph.D. Student Jaejung Park and Professor Seungchul Lee of KAIST Department of Mechanical Engineering and , Professor Hyoung Seop Kim of POSTECH, and M.S.–Ph.D. Integrated Program Student Jeong Ah Lee of POSTECH. >
The KAIST research team led by Professor Seungchul Lee from Department of Mechanical Engineering, in collaboration with Professor Hyoung Seop Kim’s team at POSTECH, successfully overcame the strength–ductility dilemma of Ti 6Al 4V alloy using artificial intelligence, enabling the production of high strength, high ductility metal products. The AI developed by the team accurately predicts mechanical properties based on various 3D printing process parameters while also providing uncertainty information, and it uses both to recommend process parameters that hold high promise for 3D printing.
Among various 3D printing technologies, laser powder bed fusion is an innovative method for manufacturing Ti-6Al-4V alloy, renowned for its high strength and bio-compatibility. However, this alloy made via 3D printing has traditionally faced challenges in simultaneously achieving high strength and high ductility. Although there have been attempts to address this issue by adjusting both the printing process parameters and heat treatment conditions, the vast number of possible combinations made it difficult to explore them all through experiments and simulations alone.
The active learning framework developed by the team quickly explores a wide range of 3D printing process parameters and heat treatment conditions to recommend those expected to improve both strength and ductility of the alloy. These recommendations are based on the AI model’s predictions of ultimate tensile strength and total elongation along with associated uncertainty information for each set of process parameters and heat treatment conditions. The recommended conditions are then validated by performing 3D printing and tensile tests to obtain the true mechanical property values. These new data are incorporated into further AI model training, and through iterative exploration, the optimal process parameters and heat treatment conditions for producing high-performance alloys were determined in only five iterations. With these optimized conditions, the 3D printed Ti-6Al-4V alloy achieved an ultimate tensile strength of 1190 MPa and a total elongation of 16.5%, successfully overcoming the strength–ductility dilemma.
Professor Seungchul Lee commented, “In this study, by optimizing the 3D printing process parameters and heat treatment conditions, we were able to develop a high-strength, high-ductility Ti-6Al-4V alloy with minimal experimentation trials. Compared to previous studies, we produced an alloy with a similar ultimate tensile strength but higher total elongation, as well as that with a similar elongation but greater ultimate tensile strength.” He added, “Furthermore, if our approach is applied not only to mechanical properties but also to other properties such as thermal conductivity and thermal expansion, we anticipate that it will enable efficient exploration of 3D printing process parameters and heat treatment conditions.”
This study was published in Nature Communications on January 22 (https://doi.org/10.1038/s41467-025-56267-1), and the research was supported by the National Research Foundation of Korea’s Nano & Material Technology Development Program and the Leading Research Center Program.
2025.02.21 View 642 -
Ultralight advanced material developed by KAIST and U of Toronto
< (From left) Professor Seunghwa Ryu of KAIST Department of Mechanical Engineering, Professor Tobin Filleter of the University of Toronto, Dr. Jinwook Yeo of KAIST, and Dr. Peter Serles of the University of Toronto >
Recently, in advanced industries such as automobiles, aerospace, and mobility, there has been increasing demand for materials that achieve weight reduction while maintaining excellent mechanical properties. An international joint research team has developed an ultralight, high-strength material utilizing nanostructures, presenting the potential for various industrial applications through customized design in the future.
KAIST (represented by President Kwang Hyung Lee) announced on the 18th of February that a research team led by Professor Seunghwa Ryu from the Department of Mechanical Engineering, in collaboration with Professor Tobin Filleter from the University of Toronto, has developed a nano-lattice structure that maximizes lightweight properties while maintaining high stiffness and strength.
In this study, the research team optimized the beam shape of the lattice structure to maintain its lightweight characteristics while maximizing stiffness and strength.
Particularly, using a multi-objective Bayesian optimization algorithm*, the team conducted an optimal design process that simultaneously considers tensile and shear stiffness improvement and weight reduction. They demonstrated that the optimal lattice structure could be predicted and designed with significantly less data (about 400 data points) compared to conventional methods.
*Multi-objective Bayesian optimization algorithm: A method that finds the optimal solution while considering multiple objectives simultaneously. It efficiently collects data and predicts results even under conditions of uncertainty.
< Figure 1. Multi-objective Bayesian optimization for generative design of carbon nanolattices with high compressive stiffness and strength at low density. The upper is the illustration of process workflow. The lower part shows top four MBO CFCC geometries with their 2D Bézier curves. (The optimized structure is predicted and designed with much less data (approximately 400) than the conventional method >
Furthermore, to maximize the effect where mechanical properties improve as size decreases at the nanoscale, the research team utilized pyrolytic carbon* material to implement an ultralight, high-strength, high-stiffness nano-lattice structure.
*Pyrolytic carbon: A carbon material obtained by decomposing organic substances at high temperatures. It has excellent heat resistance and strength, making it widely used in industries such as semiconductor equipment coatings and artificial joint coatings, where it must withstand high temperatures without deformation.
For this, the team applied two-photon polymerization (2PP) technology* to precisely fabricate complex nano-lattice structures, and mechanical performance evaluations confirmed that the developed structure simultaneously possesses strength comparable to steel and the lightness of Styrofoam.
*Two-photon polymerization (2PP) technology: An advanced optical manufacturing technique based on the principle that polymerization occurs only when two photons of a specific wavelength are absorbed simultaneously.
Additionally, the research team demonstrated that multi-focus two-photon polymerization (multi-focus 2PP) technology enables the fabrication of millimeter-scale structures while maintaining nanoscale precision.
Professor Seunghwa Ryu explained, "This technology innovatively solves the stress concentration issue, which has been a limitation of conventional design methods, through three-dimensional nano-lattice structures, achieving both ultralight weight and high strength in material development."
< Figure 2. FESEM image of the fabricated nano-lattice structure and (bottom right) the macroscopic nanolattice resting on a bubble >
He further emphasized, "By integrating data-driven optimal design with precision 3D printing technology, this development not only meets the demand for lightweight materials in the aerospace and automotive industries but also opens possibilities for various industrial applications through customized design."
This study was led by Dr. Peter Serles of the Department of Mechanical & Industrial Engineering at University of Toronto and Dr. Jinwook Yeo from KAIST as co-first authors, with Professor Seunghwa Ryu and Professor Tobin Filleter as corresponding authors.
The research was published on January 23, 2025 in the international journal Advanced Materials (Paper title: “Ultrahigh Specific Strength by Bayesian Optimization of Lightweight Carbon Nanolattices”).
DOI: https://doi.org/10.1002/adma.202410651
This research was supported by the Multiphase Materials Innovation Manufacturing Research Center (an ERC program) funded by the Ministry of Science and ICT, the M3DT (Medical Device Digital Development Tool) project funded by the Ministry of Food and Drug Safety, and the KAIST International Collaboration Program.
2025.02.18 View 746 -
Formosa Group of Taiwan to Establish Bio R&D Center at KAIST Investing 12.5 M USD
KAIST (President Kwang-Hyung Lee) announced on February 17th that it signed an agreement for cooperation in the bio-medical field with Formosa Group, one of the three largest companies in Taiwan.
< Formosa Group Chairman Sandy Wang and KAIST President Kwang-Hyung Lee at the signing ceremony >
Formosa Group Executive Committee member and Chairman Sandy Wang, who leads the group's bio and eco-friendly energy sectors, decided to establish a bio-medical research center within KAIST and invest approximately KRW 18 billion or more over 5 years. In addition, to commercialize the research results, KAIST and Formosa Group will establish a joint venture in Korea with KAIST Holdings, a KAIST-funded company.
The cooperation between the two organizations began in early 2023 when KAIST signed a comprehensive exchange and cooperation agreement (MOU) with Ming Chi University of Science and Technology (明志科技大學), Chang Gung University (長庚大學), and Chang Gung Memorial Hospital (長庚記念醫院), which are established and supported by Formosa Group. Afterwards, Chairman Sandy Wang visited KAIST in May 2024 and signed a more specific business agreement (MOA).
KAIST Holdings is a holding company established by KAIST, a government-funded organization, to attract investment and conduct business, and will pursue the establishment of a joint venture with a 50:50 equity structure in cooperation with Formosa Group. KAIST Holdings will invest KAIST’s intellectual property rights, and Formosa Group will invest a corresponding amount of funds.
The KAIST-Formosa joint venture will provide research funds to the KAIST-Formosa Bio-Medical Research Center to be established in the future, secure the right to implement the intellectual property rights generated, and promote full-scale business.
The KAIST-Formosa Bio-Medical Research Center will establish a ‘brain organoid bank’ created by obtaining tissues from hundreds of patients with degenerative brain diseases, thereby securing high-dimensional data that will reveal the fundamental causes of aging and disease. It is expected that KAIST’s world-class artificial intelligence technology will analyze large-scale patient data to find the causes of aging and disease.
Through this business, it is expected that by 2030, five years from now, it will discover more than 10 types of intractable brain disease treatments and expand to more than 20 businesses, including human cell-centered diagnostics and preclinical businesses, and secure infrastructure and intellectual property rights that can create value worth approximately KRW 250 billion.
The Chang Gung Memorial Hospital in Taiwan has 10,000 beds and handles 35,000 patients per day, and systematically accumulates patient tissue and clinical data. Chang Gung Memorial Hospital will differentiate the tissues of patients with degenerative brain diseases and send them to the KAIST-Formosa Bio-Medical Research Center, which will then produce brain organoids to be used for disease research and new drug development. This will allow the world’s largest patient tissue data bank to be established.
Dean Daesoo Kim of the College of Life Science and Bioengineering at KAIST said, “This collaboration between KAIST and Formosa Group is a new research collaboration model that goes beyond joint research to establish a joint venture and global commercialization of developed technologies, and it is significant in that it can serve as an opportunity to promote biomedical research and development.”
With this agreement, KAIST, which has been promoting the KAIST Advanced Regenerative Medicine Engineering Center in Osong K-Bio Square, has secured a practical global partner.
< Representatives of the Formosa Group and KAIST >
KAIST’s Senior Vice President for Planning and Budget, Professor Kyung-Soo Kim emphasized, “KAIST has made great efforts to secure an edge in state-of-the-art biomedical fields such as stem cells and gene editing technology, by attracting the world’s best experts and discovering global cooperation partners, and these results can ultimately be linked to the Osong K-Bio Square project.”
SVP Kim then predicted, “In particular, the practical cooperation with Taiwan’s best Formosa Chang Gung Memorial Hospital, which has abundant clinical experience in stem cell treatment, will be an important axis of KAIST’s bio innovation strategy.”
Formosa Chairman Sandy Wang emphasized that this investment and cooperation is built on trust in KAIST’s R&D capabilities and the passion of its researchers. And added that through this, the Formosa Group will practice corporate social responsibility and take an important first step together with KAIST to protect the welfare and health of humanity. She also went on the say that she expects to see the cooperation expanded to various fields such as mobility and semiconductors based on the successes begotten from the cooperation in the bio field.
KAIST President Kwang-Hyung Lee said, “I evaluate this agreement as one of the most important events that will spearhead KAIST into overseas biotechnology stages,” and added, “I expect that this cooperation will be an opportunity for Taiwan and Korea, both of which have IT industry-centered structures, to create new growth engines in the bio industry.” Meanwhile, Formosa Group is a company founded by Chairman Sandy Wang’s father, Chairman Yung-Ching Wang. It is the world’s No. 1 plastic PVC producer and is leading core industries of the Taiwanese economy, including semiconductors, steel, heavy industry, bio, and batteries. Chairman Yung-Ching Wang was respected by the Taiwanese people for his exemplary return of wealth to society under the belief that the companies and assets he founded “belong to the people.”
2025.02.17 View 524 -
KAIST Holds 2025 Commencement Ceremony
KAIST (President Kwang-Hyung Lee) held its 2025 Commencement Ceremony at the Lyu Keun-Chul Sports Complex on the Daejeon Main Campus at 2 p.m. on the 14th of February.
< A scene from KAIST Commencement 2025 - Guests of Honor and Administrative Professors Entering the Stage headed by the color guards of the ELKA (Encouraging Leaders of KAIST) >
At this ceremony, a total of 3,144 degrees were conferred, including 785 doctorates, 1,643 masters, and 716 bachelors. With this, KAIST has produced a total of 81,156 advanced science and technology personnel, including 17,313 doctorates, 41,566 masters, and 22,277 bachelors since its establishment in 1971.
Changyu Lee from the School of Computing received the Minister of Science and ICT Award, and the Chairman of the KAIST Board of Trustees Award went to Lance Khizner Dabu Gragasin, an international student from the Philippines of the Department of Chemical and Biological Engineering. The President’s Award was given to Seoyeong Yang of the Department of Biological Sciences, and the Alumni Association President’s Award and the Development Foundation Chairman’s Award was given to Gahyeon Bae of the Department of Industrial Design and Buyeon Kim of the Department of Mechanical Engineering, respectively.
Minister of Science and ICT Sang-Im Yoo joined the ceremony to deliver a congratulatory speech and to present the awards to outstanding graduates.
< Minister Sang-Im Yoo of the Ministry of Science, Technology and ICT giving his congratulatory message at KAIST Commencement 2025 >
The valedictorian speeches were given by Minjae Kim of the School of Computing, who has practiced the value of sharing that learning is not competition but cooperation, and Mohammed Haruna Hamza of the Department of Aerospace Engineering, a Nigerian international student. Mr. Hamza is the first foreign student to represent the graduating class as valedictorian since the founding of KAIST.
Hamza lost his home and school in his home country due to a terrorist group’s bombing and moved south, but despite the adversity, he continued his studies while pursuing his dream of becoming an aerospace engineer. As a result of his efforts, Hamza was invited by the Korean government to study at KAIST. He expressed his determination to pursue his dream by saying, “I am grateful for the people and experiences that helped me overcome my adversity. The future is the result of the decisions we make today.”
A Pakistani international student was chosen as one of this year's "Most Talked about Graduates of the Year". It is Ali Syed Sheraz who wore his doctoral cap at this year’s commencement ceremony. Ali, a single father who left his one-year-old son behind in his home country, working as a university lecturer. He joined the Ph.D. program in mechanical engineering in 2019 with a passion for mechanical energy.
Ali’s academic journey was full of challenges and growth. Due to COVID-19, his research was suspended for six months, and he had difficulty continuing his studies undergoing three surgeries after a bicycle accident, including a surgery for a fractured elbow, a nose surgery, and removal of kidney stones.
However, he accepted these failure and hardship as a process of growth and participated in the ‘Failed Project Showcase’ and ‘Failure Essay Contest’ held by the KAIST Failure Society, sharing his experiences and growing into a more solid researcher.
< Most Talked about Graduate Graduate of the Year - Syed Sheraz Ali >
Despite experiencing various hardships, he found lessons to learn from them and changed his perspective, which made him unafraid of taking on new challenges. He showed through his own example that failure is not just stumbling blocks but can be a stepping stone to success by looking at his studies and personal life positively.
Furthermore, after becoming the president of the Muslim Student Association, Ali introduced halal menus to the cafeteria on campus so that more Muslim students could eat comfortably. Thanks to this change, his time at KAIST has become an opportunity to understand and experience various cultures more.
Ali is researching artificial muscles (soft actuators) with the world's highest bending strain using MXene, an artificial muscle nanomaterial that can move smoothly, in Professor Il-kwon Oh's lab.
Ali said, "After completing my Ph.D., I plan to develop soft robots, healthcare electronics, and next-generation tactile technology based on MXene, a next-generation 2D material. It is important for my juniors not to be afraid of failure and to have a challenging attitude."
Another 'Most Talked about Graduate of the Year', Mr. Sung-Hyun Jung, who graduated with a master's degree from the Graduate School of Bio Innvation Management, is the CEO of Promedius, a medical AI startup, and has commercialized an osteoporosis diagnosis software based on chest X-rays using AI, and grown it into a leading company in the bone health field.
CEO Jung's challenge shows that KAIST's management education is not just theoretical but practical enough to be applied immediately in the field. CEO Jung, who is also the father of three daughters, experienced business failure in China during the period when the conflict between Korea and China was intensifying.
He moved to Silicon Valley in the United States to revive his business and tried to acquire even small businesses, but the reality was not easy. He worked hard, standing 14 hours a day in a kimchi factory and a restaurant kitchen to make a living. After finishing his life in the United States, CEO Jung returned to Korea and had the opportunity to join Lunit, a global medical AI leader founded by KAIST graduates. CEO Jung experienced the growth of the global medical AI market firsthand with unit Chairman Seungwook Paek.
When he entered the Master's Program at the Graduate School of Bio Innvation Management in 2023 to acquire more specialized knowledge, CEO Jung had just transferred to Promedius and was in a crisis situation with only about 6 months left before the company's funds were exhausted.
While considering a change in business direction because he judged that it would be difficult to survive with existing business items, he learned keywords and investment review perspectives that venture capital (VC) pays attention to in Professor Hoonje Cho’s ‘Bio-innovation Business Startup Strategy and Practice’ class. He attracted 11.4 billion won in investment by applying the investment proposal he wrote based on what he learned from the class to actual practice.
< Most Talked about Graduate of the Year - Sung-Hyun Jung >
In addition, he applied the innovation strategy in the medical field he learned in Professor Kihwan Park’s ‘Innovation and Marketing in Bio and Pharmaceutics’ to the field of osteoporosis, and achieved the result of being selected as the first Asian company to be a corporate advisory committee member of the International Osteoporosis Foundation (IOF). Through this, he established the company as a representative global entity in the osteoporosis field in just one year.
CEO Jung, who applied what he learned from KAIST to actual management and achieved results in the global market in a short period of time, said, “I want to prove that KAIST education is not limited to theory, but is very practical.” He said, “I want to let people know that my life, once full of hardship, got on the track toward success after encountering KAIST,” and expressed his ambition, saying, “My long-term goal is to create a world-class company that is recognized globally.”
In addition, an honorary doctorate was awarded to Chairman Joong Keun Lee of Booyoung Group at the commencement ceremony.
Chairman Joong Keun Lee, who is an entrepreneur that led Booyoung Group, a leading general construction company, received the honorary doctorate in business administration, for leading the development of domestic housing welfare, education, and culture.
KAIST Provost Gyunmin Lee said, “Chairman Joong Keun Lee spared no effort in providing dedicated support for the development of domestic science and technology and the cultivation of future talents. He is awarded the honorary doctorate in recognition of his social responsibility in various fields, including scholarships and support for educational facilities, as well as domestic and international education, culture, veterans affairs, and overseas support.”
Since founding Booyoung Group in 1983, Chairman Lee has boldly entered the rental housing business, a field that large construction companies had avoided, and has played a significant role in improving the quality of life of ordinary citizens by supplying 230,000 households out of 383 complexes and approximately 300,000 households nationwide as rental housing, thereby contributing greatly to the stability of national housing.
< Chairman Joong Keun Lee giving his acceptance speech for his honorary Doctorate >
Chairman Joong Keun Lee, who has been offering hope for a sustainable future, said, “I am honored to receive an honorary doctorate from KAIST, and I hope that KAIST students will nurture their dreams and talents and grow into global talents who will contribute to national development.”
President Kwang-Hyung Lee said, “Chairman Joong Keun Lee has been carrying out various social contribution activities, and in particular, through supporting academic infrastructure, which is the core of national competitiveness, we can see his deep interest in and sense of responsibility for the development of science and technology in our country.” He added, “I am truly delighted to have him as a member of the KAIST family, and I congratulate him on behalf of all members, including our students.”
President Kwang-Hyung Lee also delivered a message of encouragement at the ceremony to charge the graduates to, “Find and keep a dream of your own, be on the lookout for opportunities, don’t be afraid of making mistakes, and do not shy away from taking on challenging tasks.” He added, “Even if you fail, don’t give up. Keep on trying so that you will get to that stage of radiate your own light on the stages where anything is possible.” (End)
2025.02.14 View 1272 -
KAIST Develops Wearable Carbon Dioxide Sensor to Enable Real-time Apnea Diagnosis
- Professor Seunghyup Yoo’s research team of the School of Electrical Engineering developed an ultralow-power carbon dioxide (CO2) sensor using a flexible and thin organic photodiode, and succeeded in real-time breathing monitoring by attaching it to a commercial mask
- Wearable devices with features such as low power, high stability, and flexibility can be utilized for early diagnosis of various diseases such as chronic obstructive pulmonary disease and sleep apnea
< Photo 1. From the left, School of Electrical Engineering, Ph.D. candidate DongHo Choi, Professor Seunghyup Yoo, and Department of Materials Science and Engineering, Bachelor’s candidate MinJae Kim >
Carbon dioxide (CO2) is a major respiratory metabolite, and continuous monitoring of CO2 concentration in exhaled breath is not only an important indicator for early detection and diagnosis of respiratory and circulatory system diseases, but can also be widely used for monitoring personal exercise status. KAIST researchers succeeded in accurately measuring CO2 concentration by attaching it to the inside of a mask.
KAIST (President Kwang-Hyung Lee) announced on February 10th that Professor Seunghyup Yoo's research team in the Department of Electrical and Electronic Engineering developed a low-power, high-speed wearable CO2 sensor capable of stable breathing monitoring in real time.
Existing non-invasive CO2 sensors had limitations in that they were large in size and consumed high power. In particular, optochemical CO2 sensors using fluorescent molecules have the advantage of being miniaturized and lightweight, but due to the photodegradation phenomenon of dye molecules, they are difficult to use stably for a long time, which limits their use as wearable healthcare sensors.
Optochemical CO2 sensors utilize the fact that the intensity of fluorescence emitted from fluorescent molecules decreases depending on the concentration of CO2, and it is important to effectively detect changes in fluorescence light.
To this end, the research team developed a low-power CO2 sensor consisting of an LED and an organic photodiode surrounding it. Based on high light collection efficiency, the sensor, which minimizes the amount of excitation light irradiated on fluorescent molecules, achieved a device power consumption of 171 μW, which is tens of times lower than existing sensors that consume several mW.
< Figure 1. Structure and operating principle of the developed optochemical carbon dioxide (CO2) sensor. Light emitted from the LED is converted into fluorescence through the fluorescent film, reflected from the light scattering layer, and incident on the organic photodiode. CO2 reacts with a small amount of water inside the fluorescent film to form carbonic acid (H2CO3), which increases the concentration of hydrogen ions (H+), and the fluorescence intensity due to 470 nm excitation light decreases. The circular organic photodiode with high light collection efficiency effectively detects changes in fluorescence intensity, lowers the power required light up the LED, and reduces light-induced deterioration. >
The research team also elucidated the photodegradation path of fluorescent molecules used in CO2 sensors, revealed the cause of the increase in error over time in photochemical sensors, and suggested an optical design method to suppress the occurrence of errors.
Based on this, the research team developed a sensor that effectively reduces errors caused by photodegradation, which was a chronic problem of existing photochemical sensors, and can be used continuously for up to 9 hours while existing technologies based on the same material can be used for less than 20 minutes, and can be used multiple times when replacing the CO2 detection fluorescent film.
< Figure 2. Wearable smart mask and real-time breathing monitoring. The fabricated sensor module consists of four elements (①: gas-permeable light-scattering layer, ②: color filter and organic photodiode, ③: light-emitting diode, ④: CO2-detecting fluorescent film). The thin and light sensor (D1: 400 nm, D2: 470 nm) is attached to the inside of the mask to monitor the wearer's breathing in real time. >
The developed sensor accurately measured CO2 concentration by being attached to the inside of a mask based on the advantages of being light (0.12 g), thin (0.7 mm), and flexible. In addition, it showed fast speed and high resolution that can monitor respiratory rate by distinguishing between inhalation and exhalation in real time.
< Photo 2. The developed sensor attached to the inside of the mask >
Professor Seunghyup Yoo said, "The developed sensor has excellent characteristics such as low power, high stability, and flexibility, so it can be widely applied to wearable devices, and can be used for the early diagnosis of various diseases such as hypercapnia, chronic obstructive pulmonary disease, and sleep apnea." He added, "In particular, it is expected to be used to improve side effects caused by rebreathing in environments where dust is generated or where masks are worn for long periods of time, such as during seasonal changes."
This study, in which KAIST's Department of Materials Science and Engineering's undergraduate student Minjae Kim and School of Electrical Engineering's doctoral student Dongho Choi participated as joint first authors, was published in the online version of Cell's sister journal, Device, on the 22nd of last month. (Paper title: Ultralow-power carbon dioxide sensor for real-time breath monitoring) DOI: https://doi.org/10.1016/j.device.2024.100681
< Photo 3. From the left, Professor Seunghyup Yoo of the School of Electrical Engineering, MinJae Kim, an undergraduate student in the Department of Materials Science and Engineering, and Dongho Choi, a doctoral student in the School of Electrical Engineering >
This study was supported by the Ministry of Trade, Industry and Energy's Materials and Components Technology Development Project, the National Research Foundation of Korea's Original Technology Development Project, and the KAIST Undergraduate Research Participation Project. This work was supported by the (URP) program.
2025.02.13 View 1322 -
KAIST Discovers Molecular Switch that Reverses Cancerous Transformation at the Critical Moment of Transition
< (From left) PhD student Seoyoon D. Jeong, (bottom) Professor Kwang-Hyun Cho, (top) Dr. Dongkwan Shin, Dr. Jeong-Ryeol Gong >
Professor Kwang-Hyun Cho’s research team has recently been highlighted for their work on developing an original technology for cancer reversal treatment that does not kill cancer cells but only changes their characteristics to reverse them to a state similar to normal cells. This time, they have succeeded in revealing for the first time that a molecular switch that can induce cancer reversal at the moment when normal cells change into cancer cells is hidden in the genetic network.
KAIST (President Kwang-Hyung Lee) announced on the 5th of February that Professor Kwang-Hyun Cho's research team of the Department of Bio and Brain Engineering has succeeded in developing a fundamental technology to capture the critical transition phenomenon at the moment when normal cells change into cancer cells and analyze it to discover a molecular switch that can revert cancer cells back into normal cells.
A critical transition is a phenomenon in which a sudden change in state occurs at a specific point in time, like water changing into steam at 100℃. This critical transition phenomenon also occurs in the process in which normal cells change into cancer cells at a specific point in time due to the accumulation of genetic and epigenetic changes.
The research team discovered that normal cells can enter an unstable critical transition state where normal cells and cancer cells coexist just before they change into cancer cells during tumorigenesis, the production or development of tumors, and analyzed this critical transition state using a systems biology method to develop a cancer reversal molecular switch identification technology that can reverse the cancerization process. They then applied this to colon cancer cells and confirmed through molecular cell experiments that cancer cells can recover the characteristics of normal cells.
This is an original technology that automatically infers a computer model of the genetic network that controls the critical transition of cancer development from single-cell RNA sequencing data, and systematically finds molecular switches for cancer reversion by simulation analysis. It is expected that this technology will be applied to the development of reversion therapies for other cancers in the future.
Professor Kwang-Hyun Cho said, "We have discovered a molecular switch that can revert the fate of cancer cells back to a normal state by capturing the moment of critical transition right before normal cells are changed into an irreversible cancerous state."
< Figure 1. Overall conceptual framework of the technology that automatically constructs a molecular regulatory network from single-cell RNA sequencing data of colon cancer cells to discover molecular switches for cancer reversion through computer simulation analysis. Professor Kwang-Hyun Cho's research team established a fundamental technology for automatic construction of a computer model of a core gene network by analyzing the entire process of tumorigenesis of colon cells turning into cancer cells, and developed an original technology for discovering the molecular switches that can induce cancer cell reversal through attractor landscape analysis. >
He continued, "In particular, this study has revealed in detail, at the genetic network level, what changes occur within cells behind the process of cancer development, which has been considered a mystery until now." He emphasized, "This is the first study to reveal that an important clue that can revert the fate of tumorigenesis is hidden at this very critical moment of change."
< Figure 2. Identification of tumor transition state using single-cell RNA sequencing data from colorectal cancer. Using single-cell RNA sequencing data from colorectal cancer patient-derived organoids for normal and cancerous tissues, a critical transition was identified in which normal and cancerous cells coexist and instability increases (a-d). The critical transition was confirmed to show intermediate levels of major phenotypic features related to cancer or normal tissues that are indicative of the states between the normal and cancerous cells (e). >
The results of this study, conducted by KAIST Dr. Dongkwan Shin (currently at the National Cancer Center), Dr. Jeong-Ryeol Gong, and doctoral student Seoyoon D. Jeong jointly with a research team at Seoul National University that provided the organoids (in vitro cultured tissues) from colon cancer patient, were published as an online paper in the international journal ‘Advanced Science’ published by Wiley on January 22nd.
(Paper title: Attractor landscape analysis reveals a reversion switch in the transition of colorectal tumorigenesis) (DOI: https://doi.org/10.1002/advs.202412503)
< Figure 3. Reconstruction of a dynamic network model for the transition state of colorectal cancer.
A new technology was established to build a gene network computer model that can simulate the dynamic changes between genes by integrating single-cell RNA sequencing data and existing experimental results on gene-to-gene interactions in the critical transition of cancer. (a). Using this technology, a gene network computer model for the critical transition of colorectal cancer was constructed, and the distribution of attractors representing normal and cancer cell phenotypes was investigated through attractor landscape analysis (b-e). >
This study was conducted with the support of the National Research Foundation of Korea under the Ministry of Science and ICT through the Mid-Career Researcher Program and Basic Research Laboratory Program and the Disease-Centered Translational Research Project of the Korea Health Industry Development Institute (KHIDI) of the Ministry of Health and Welfare.
< Figure 4. Quantification of attractor landscapes and discovery of transcription factors for cancer reversibility through perturbation simulation analysis. A methodology for implementing discontinuous attractor landscapes continuously from a computer model of gene networks and quantifying them as cancer scores was introduced (a), and attractor landscapes for the critical transition of colorectal cancer were secured (b-d). By tracking the change patterns of normal and cancer cell attractors through perturbation simulation analysis for each gene, the optimal combination of transcription factors for cancer reversion was discovered (e-h). This was confirmed in various parameter combinations as well (i). >
< Figure 5. Identification and experimental validation of the optimal target gene for cancer reversion. Among the common target genes of the discovered transcription factor combinations, we identified cancer reversing molecular switches that are predicted to suppress cancer cell proliferation and restore the characteristics of normal colon cells (a-d). When inhibitors for the molecular switches were treated to organoids derived from colon cancer patients, it was confirmed that cancer cell proliferation was suppressed and the expression of key genes related to cancer development was inhibited (e-h), and a group of genes related to normal colon epithelium was activated and transformed into a state similar to normal colon cells (i-j). >
< Figure 6. Schematic diagram of the research results. Professor Kwang-Hyun Cho's research team developed an original technology to systematically discover key molecular switches that can induce reversion of colon cancer cells through a systems biology approach using an attractor landscape analysis of a genetic network model for the critical transition at the moment of transformation from normal cells to cancer cells, and verified the reversing effect of actual colon cancer through cellular experiments. >
2025.02.05 View 9397 -
KAIST Develops AI-Driven Performance Prediction Model to Advance Space Electric Propulsion Technology
< (From left) PhD candidate Youngho Kim, Professor Wonho Choe, and PhD candidate Jaehong Park from the Department of Nuclear and Quantum Engineering >
Hall thrusters, a key space technology for missions like SpaceX's Starlink constellation and NASA's Psyche asteroid mission, are high-efficiency electric propulsion devices using plasma technology*. The KAIST research team announced that the AI-designed Hall thruster developed for CubeSats will be installed on the KAIST-Hall Effect Rocket Orbiter (K-HERO) CubeSat to demonstrate its in-orbit performance during the fourth launch of the Korean Launch Vehicle called Nuri rocket (KSLV-2) scheduled for November this year.
*Plasma is one of the four states of matter, where gases are heated to high energies, causing them to separate into charged ions and electrons. Plasma is used not only in space electric propulsion but also in semiconductor manufacturing, display processes, and sterilization devices.
On February 3rd, the research team from the KAIST Department of Nuclear and Quantum Engineering’s Electric Propulsion Laboratory, led by Professor Wonho Choe, announced the development of an AI-based technique to accurately predict the performance of Hall thrusters, the engines of satellites and space probes.
Hall thrusters provide high fuel efficiency, requiring minimal propellant to achieve significant acceleration of spacecrafts or satellites while producing substantial thrust relative to power consumption. Due to these advantages, Hall thrusters are widely used in various space missions, including the formation flight of satellite constellations, deorbiting maneuvers for space debris mitigation, and deep space missions such as asteroid exploration.
As the space industry continues to grow during the NewSpace era, the demand for Hall thrusters suited to diverse missions is increasing. To rapidly develop highly efficient, mission-optimized Hall thrusters, it is essential to predict thruster performance accurately from the design phase.
However, conventional methods have limitations, as they struggle to handle the complex plasma phenomena within Hall thrusters or are only applicable under specific conditions, leading to lower prediction accuracy.
The research team developed an AI-based performance prediction technique with high accuracy, significantly reducing the time and cost associated with the iterative design, fabrication, and testing of thrusters. Since 2003, Professor Wonho Choe’s team has been leading research on electric propulsion development in Korea. The team applied a neural network ensemble model to predict thruster performance using 18,000 Hall thruster training data points generated from their in-house numerical simulation tool.
The in-house numerical simulation tool, developed to model plasma physics and thrust performance, played a crucial role in providing high-quality training data. The simulation’s accuracy was validated through comparisons with experimental data from ten KAIST in-house Hall thrusters, with an average prediction error of less than 10%.
< Figure 1. This research has been selected as the cover article for the March 2025 issue (Volume 7, Issue 3) of the AI interdisciplinary journal, Advanced Intelligent Systems. >
The trained neural network ensemble model acts as a digital twin, accurately predicting the Hall thruster performance within seconds based on thruster design variables.
Notably, it offers detailed analyses of performance parameters such as thrust and discharge current, accounting for Hall thruster design variables like propellant flow rate and magnetic field—factors that are challenging to evaluate using traditional scaling laws.
This AI model demonstrated an average prediction error of less than 5% for the in-house 700 W and 1 kW KAIST Hall thrusters and less than 9% for a 5 kW high-power Hall thruster developed by the University of Michigan and the U.S. Air Force Research Laboratory. This confirms the broad applicability of the AI prediction method across different power levels of Hall thrusters.
Professor Wonho Choe stated, “The AI-based prediction technique developed by our team is highly accurate and is already being utilized in the analysis of thrust performance and the development of highly efficient, low-power Hall thrusters for satellites and spacecraft. This AI approach can also be applied beyond Hall thrusters to various industries, including semiconductor manufacturing, surface processing, and coating, through ion beam sources.”
< Figure 2. The AI-based prediction technique developed by the research team accurately predicts thrust performance based on design variables, making it highly valuable for the development of high-efficiency Hall thrusters. The neural network ensemble processes design variables, such as channel geometry and magnetic field information, and outputs key performance metrics like thrust and prediction accuracy, enabling efficient thruster design and performance analysis. >
Additionally, Professor Choe mentioned, “The CubeSat Hall thruster, developed using the AI technique in collaboration with our lab startup—Cosmo Bee, an electric propulsion company—will be tested in orbit this November aboard the K-HERO 3U (30 x 10 x 10 cm) CubeSat, scheduled for launch on the fourth flight of the KSLV-2 Nuri rocket.”
This research was published online in Advanced Intelligent Systems on December 25, 2024 with PhD candidate Jaehong Park as the first author and was selected as the journal’s cover article, highlighting its innovation.
< Figure 3. Image of the 150 W low-power Hall thruster for small and micro satellites, developed in collaboration with Cosmo Bee and the KAIST team. The thruster will be tested in orbit on the K-HERO CubeSat during the KSLV-2 Nuri rocket’s fourth launch in Q4 2025. >
This research was supported by the National Research Foundation of Korea’s Space Pioneer Program (200mN High Thrust Electric Propulsion System Development).
(Paper Title: Predicting Performance of Hall Effect Ion Source Using Machine Learning, DOI: https://doi.org/10.1002/aisy.202400555 )
< Figure 4. Graphs of the predicted thrust and discharge current of KAIST’s 700 W Hall thruster using the AI model (HallNN). The left image shows the Hall thruster operating in KAIST Electric Propulsion Laboratory’s vacuum chamber, while the center and right graphs present the prediction results for thrust and discharge current based on anode mass flow rate. The red lines represent AI predictions, and the blue dots represent experimental results, with a prediction error of less than 5%. >
2025.02.03 View 1654 -
KAIST Uncovers the Principles of Gene Expression Regulation in Cancer and Cellular Functions
< (From left) Professor Seyun Kim, Professor Gwangrog Lee, Dr. Hyoungjoon Ahn, Dr. Jeongmin Yu, Professor Won-Ki Cho, and (below) PhD candidate Kwangmin Ryu of the Department of Biological Sciences>
A research team at KAIST has identified the core gene expression networks regulated by key proteins that fundamentally drive phenomena such as cancer development, metastasis, tissue differentiation from stem cells, and neural activation processes. This discovery lays the foundation for developing innovative therapeutic technologies.
On the 22nd of January, KAIST (represented by President Kwang Hyung Lee) announced that the joint research team led by Professors Seyun Kim, Gwangrog Lee, and Won-Ki Cho from the Department of Biological Sciences had uncovered essential mechanisms controlling gene expression in animal cells.
Inositol phosphate metabolites produced by inositol metabolism enzymes serve as vital secondary messengers in eukaryotic cell signaling systems and are broadly implicated in cancer, obesity, diabetes, and neurological disorders.
The research team demonstrated that the inositol polyphosphate multikinase (IPMK) enzyme, a key player in the inositol metabolism system, acts as a critical transcriptional activator within the core gene expression networks of animal cells. Notably, although IPMK was previously reported to play an important role in the transcription process governed by serum response factor (SRF), a representative transcription factor in animal cells, the precise mechanism of its action was unclear.
SRF is a transcription factor directly controlling the expression of at least 200–300 genes, regulating cell growth, proliferation, apoptosis, and motility, and is indispensable for organ development, such as in the heart.
The team discovered that IPMK binds directly to SRF, altering the three-dimensional structure of the SRF protein. This interaction facilitates the transcriptional activity of various genes through the SRF activated by IPMK, demonstrating that IPMK acts as a critical regulatory switch to enhance SRF's protein activity.
< Figure 1. The serum response factor (SRF) protein, a key transcription factor in animal cells, directly binds to inositol polyphosphate multikinase (IPMK) enzyme and undergoes structural change to acquire DNA binding ability, and precisely regulates growth and differentiation of animal cells through transcriptional activation. >
The team further verified that disruptions in the direct interaction between IPMK and SRF lead to the reduced functionality and activity of SRF, causing severe impairments in gene expression.
By highlighting the significance of the intrinsically disordered region (IDR) in SRF, the researchers underscored the biological importance of intrinsically disordered proteins (IDPs). Unlike most proteins that adopt distinct structures through folding, IDPs, including those with IDRs, do not exhibit specific structures but play crucial biological roles, attracting significant attention in the scientific community.
Professor Seyun Kim commented, "This study provides a vital mechanism proving that IPMK, a key enzyme in the inositol metabolism system, is a major transcriptional activator in the core gene expression network of animal cells. By understanding fundamental processes such as cancer development and metastasis, tissue differentiation from stem cells, and neural activation through SRF, we hope this discovery will lead to the broad application of innovative therapeutic technologies."
The findings were published on January 7th in the international journal Nucleic Acids Research (IF=16.7, top 1.8% in Biochemistry and Molecular Biology), under the title “Single-molecule analysis reveals that IPMK enhances the DNA-binding activity of the transcription factor SRF" (DOI: 10.1093/nar/gkae1281).
This research was supported by the National Research Foundation of Korea's Mid-career Research Program, Leading Research Center Program, and Global Research Laboratory Program, as well as by the Suh Kyungbae Science Foundation and the Samsung Future Technology Development Program.
2025.01.24 View 5498 -
A Way for Smartwatches to Detect Depression Risks Devised by KAIST and U of Michigan Researchers
- A international joint research team of KAIST and the University of Michigan developed a digital biomarker for predicting symptoms of depression based on data collected by smartwatches
- It has the potential to be used as a medical technology to replace the economically burdensome fMRI measurement test
- It is expected to expand the scope of digital health data analysis
The CORONA virus pandemic also brought about a pandemic of mental illness. Approximately one billion people worldwide suffer from various psychiatric conditions. Korea is one of more serious cases, with approximately 1.8 million patients exhibiting depression and anxiety disorders, and the total number of patients with clinical mental diseases has increased by 37% in five years to approximately 4.65 million. A joint research team from Korea and the US has developed a technology that uses biometric data collected through wearable devices to predict tomorrow's mood and, further, to predict the possibility of developing symptoms of depression.
< Figure 1. Schematic diagram of the research results. Based on the biometric data collected by a smartwatch, a mathematical algorithm that solves the inverse problem to estimate the brain's circadian phase and sleep stages has been developed. This algorithm can estimate the degrees of circadian disruption, and these estimates can be used as the digital biomarkers to predict depression risks. >
KAIST (President Kwang Hyung Lee) announced on the 15th of January that the research team under Professor Dae Wook Kim from the Department of Brain and Cognitive Sciences and the team under Professor Daniel B. Forger from the Department of Mathematics at the University of Michigan in the United States have developed a technology to predict symptoms of depression such as sleep disorders, depression, loss of appetite, overeating, and decreased concentration in shift workers from the activity and heart rate data collected from smartwatches.
According to WHO, a promising new treatment direction for mental illness focuses on the sleep and circadian timekeeping system located in the hypothalamus of the brain, which directly affect impulsivity, emotional responses, decision-making, and overall mood.
However, in order to measure endogenous circadian rhythms and sleep states, blood or saliva must be drawn every 30 minutes throughout the night to measure changes in the concentration of the melatonin hormone in our bodies and polysomnography (PSG) must be performed. As such treatments requires hospitalization and most psychiatric patients only visit for outpatient treatment, there has been no significant progress in developing treatment methods that take these two factors into account. In addition, the cost of the PSG test, which is approximately $1000, leaves mental health treatment considering sleep and circadian rhythms out of reach for the socially disadvantaged.
The solution to overcome these problems is to employ wearable devices for the easier collection of biometric data such as heart rate, body temperature, and activity level in real time without spatial constraints. However, current wearable devices have the limitation of providing only indirect information on biomarkers required by medical staff, such as the phase of the circadian clock.
The joint research team developed a filtering technology that accurately estimates the phase of the circadian clock, which changes daily, such as heart rate and activity time series data collected from a smartwatch. This is an implementation of a digital twin that precisely describes the circadian rhythm in the brain, and it can be used to estimate circadian rhythm disruption.
< Figure 2. The suprachiasmatic nucleus located in the hypothalamus of the brain is the central biological clock that regulates the 24-hour physiological rhythm and plays a key role in maintaining the body’s circadian rhythm. If the phase of this biological clock is disrupted, it affects various parts of the brain, which can cause psychiatric conditions such as depression. >
The possibility of using the digital twin of this circadian clock to predict the symptoms of depression was verified through collaboration with the research team of Professor Srijan Sen of the Michigan Neuroscience Institute and Professor Amy Bohnert of the Department of Psychiatry of the University of Michigan.
The collaborative research team conducted a large-scale prospective cohort study involving approximately 800 shift workers and showed that the circadian rhythm disruption digital biomarker estimated through the technology can predict tomorrow's mood as well as six symptoms, including sleep problems, appetite changes, decreased concentration, and suicidal thoughts, which are representative symptoms of depression.
< Figure 3. The circadian rhythm of hormones such as melatonin regulates various physiological functions and behaviors such as heart rate and activity level. These physiological and behavioral signals can be measured in daily life through wearable devices. In order to estimate the body’s circadian rhythm inversely based on the measured biometric signals, a mathematical algorithm is needed. This algorithm plays a key role in accurately identifying the characteristics of circadian rhythms by extracting hidden physiological patterns from biosignals. >
Professor Dae Wook Kim said, "It is very meaningful to be able to conduct research that provides a clue for ways to apply wearable biometric data using mathematics that have not previously been utilized for actual disease management." He added, "We expect that this research will be able to present continuous and non-invasive mental health monitoring technology. This is expected to present a new paradigm for mental health care. By resolving some of the major problems socially disadvantaged people may face in current treatment practices, they may be able to take more active steps when experiencing symptoms of depression, such as seeking counsel before things get out of hand."
< Figure 4. A mathematical algorithm was devised to circumvent the problems of estimating the phase of the brain's biological clock and sleep stages inversely from the biodata collected by a smartwatch. This algorithm can estimate the degree of daily circadian rhythm disruption, and this estimate can be used as a digital biomarker to predict depression symptoms. >
The results of this study, in which Professor Dae Wook Kim of the Department of Brain and Cognitive Sciences at KAIST participated as the joint first author and corresponding author, were published in the online version of the international academic journal npj Digital Medicine on December 5, 2024. (Paper title: The real-world association between digital markers of circadian disruption and mental health risks) DOI: 10.1038/s41746-024-01348-6
This study was conducted with the support of the KAIST's Research Support Program for New Faculty Members, the US National Science Foundation, the US National Institutes of Health, and the US Army Research Institute MURI Program.
2025.01.20 View 2418 -
KAIST Develops Neuromorphic Semiconductor Chip that Learns and Corrects Itself
< Photo. The research team of the School of Electrical Engineering posed by the newly deveoped processor. (From center to the right) Professor Young-Gyu Yoon, Integrated Master's and Doctoral Program Students Seungjae Han and Hakcheon Jeong and Professor Shinhyun Choi >
- Professor Shinhyun Choi and Professor Young-Gyu Yoon’s Joint Research Team from the School of Electrical Engineering developed a computing chip that can learn, correct errors, and process AI tasks
- Equipping a computing chip with high-reliability memristor devices with self-error correction functions for real-time learning and image processing
Existing computer systems have separate data processing and storage devices, making them inefficient for processing complex data like AI. A KAIST research team has developed a memristor-based integrated system similar to the way our brain processes information. It is now ready for application in various devices including smart security cameras, allowing them to recognize suspicious activity immediately without having to rely on remote cloud servers, and medical devices with which it can help analyze health data in real time.
KAIST (President Kwang Hyung Lee) announced on the 17th of January that the joint research team of Professor Shinhyun Choi and Professor Young-Gyu Yoon of the School of Electrical Engineering has developed a next-generation neuromorphic semiconductor-based ultra-small computing chip that can learn and correct errors on its own.
< Figure 1. Scanning electron microscope (SEM) image of a computing chip equipped with a highly reliable selector-less 32×32 memristor crossbar array (left). Hardware system developed for real-time artificial intelligence implementation (right). >
What is special about this computing chip is that it can learn and correct errors that occur due to non-ideal characteristics that were difficult to solve in existing neuromorphic devices. For example, when processing a video stream, the chip learns to automatically separate a moving object from the background, and it becomes better at this task over time.
This self-learning ability has been proven by achieving accuracy comparable to ideal computer simulations in real-time image processing. The research team's main achievement is that it has completed a system that is both reliable and practical, beyond the development of brain-like components.
The research team has developed the world's first memristor-based integrated system that can adapt to immediate environmental changes, and has presented an innovative solution that overcomes the limitations of existing technology.
< Figure 2. Background and foreground separation results of an image containing non-ideal characteristics of memristor devices (left). Real-time image separation results through on-device learning using the memristor computing chip developed by our research team (right). >
At the heart of this innovation is a next-generation semiconductor device called a memristor*. The variable resistance characteristics of this device can replace the role of synapses in neural networks, and by utilizing it, data storage and computation can be performed simultaneously, just like our brain cells.
*Memristor: A compound word of memory and resistor, next-generation electrical device whose resistance value is determined by the amount and direction of charge that has flowed between the two terminals in the past.
The research team designed a highly reliable memristor that can precisely control resistance changes and developed an efficient system that excludes complex compensation processes through self-learning. This study is significant in that it experimentally verified the commercialization possibility of a next-generation neuromorphic semiconductor-based integrated system that supports real-time learning and inference.
This technology will revolutionize the way artificial intelligence is used in everyday devices, allowing AI tasks to be processed locally without relying on remote cloud servers, making them faster, more privacy-protected, and more energy-efficient.
“This system is like a smart workspace where everything is within arm’s reach instead of having to go back and forth between desks and file cabinets,” explained KAIST researchers Hakcheon Jeong and Seungjae Han, who led the development of this technology. “This is similar to the way our brain processes information, where everything is processed efficiently at once at one spot.”
The research was conducted with Hakcheon Jeong and Seungjae Han, the students of Integrated Master's and Doctoral Program at KAIST School of Electrical Engineering being the co-first authors, the results of which was published online in the international academic journal, Nature Electronics, on January 8, 2025.
*Paper title: Self-supervised video processing with self-calibration on an analogue computing platform based on a selector-less memristor array ( https://doi.org/10.1038/s41928-024-01318-6 )
This research was supported by the Next-Generation Intelligent Semiconductor Technology Development Project, Excellent New Researcher Project and PIM AI Semiconductor Core Technology Development Project of the National Research Foundation of Korea, and the Electronics and Telecommunications Research Institute Research and Development Support Project of the Institute of Information & communications Technology Planning & Evaluation.
2025.01.17 View 2666