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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 3673 -
Professor Il-Doo Kim Receives the Science Minister’s Award
Professor Il-Doo Kim from the Department of Materials Science and Engineering received the Science and ICT Minister’s Award in recognition of his commercialization and technology transfer achievements during the Day of IP celebration.
Professor Kim, who has made over 222 patents application and registration home and abroad, has advanced toxic gas detection and breath gas sensor technology by arraying nanosensor fibers. His technological advances in micro-electro-mechanical systems (MEMS) helped to advance the commercialization of the MEMS-related sensor and improve its overall competitiveness.
He founded the Il-Doo Kim Research Center in 2019 and focuses on the commercialization of nanofiber manufacturing through electrospinning and highly efficient nanofiber filters. For instance, he succeeded in manufacturing a nano-filter recyclable mask that maintains excellent filtering efficiency even after hand washing through the development of proprietary technology that aligns nanofibers with a diameter of 100~500 nanometers in orthogonal or unidirectional directions.
Professor Kim also serves as an associate editor at ACS Nano. He said, “The importance of IP goes without saying. I look forward to the registration and application of more KAIST patents leading to commercialization, paving the way for national technological competitiveness.”
2021.09.15 View 6907 -
Antivirus Industry the Centerpiece of New Deal R&D Initiatives
- KAIST launches post-COVID-19 R&D initiatives for smart mobile medical systems. -
KAIST will make the antivirus industry the centerpiece of what it is touting as the KAIST New Deal R&D initiative, which will drive new growth engines for preparing for the post-coronavirus era.
According to the new initiative, KAIST will concentrate on creating antivirus technologies, infectious disease-related big data management, and non-contact services platforms as key future R&D projects.
President Sung-Chul Shin launched the COVID-19 R&D Initiative task force last month, composed of more than 50 professors from the Graduate School of Medical Science and Engineering, the Department of Biological Sciences, the College of Engineering, and the Department of Industrial Design. The task force came up with key research agendas that will promote smart mobile medical systems in the years ahead.
“We will devote all of our R&D capacities to pursue a smart healthcare society,” said President Shin. “Our competitiveness in the fields of AI, ICT, materials, and bio-technology holds significant potential for building a healthy society powered by smart medical systems in Korea,” he added.
The smart medical systems focus mainly on building an Epidemic Mitigating Mobile Module (EMMM). The EMMM will manage epidemics via the three phases of prevention, emergency response, and treatment, with the development of each phase’s technological modules. The EMMM will also build an AI big data platform to assist with clinical applications and epidemic management.
Technologies applicable for the prevention phase include developing recyclable antivirus masks, plasma virus sterilizers, and smart breathable protective gowns. KAIST researchers will also focus on developing diagnosis modules that will identify epidemics more quickly and accurately.
Most significantly, KAIST aims to develop technologies for anti-infection medical services such as the transformable negative pressure ambulance module and negative pressure room, which are specially developed for respiratory infections.
The new R&D initiatives will center on virus therapies and treatments, specifically pushing forward vaccine and robotics studies. As caring robots and delivery robots will become common as main caregivers via noncontact services, research focusing on robotics will be significantly enhanced.
Even before launching the new R&D initiatives, researchers have started to present new technologies to help address the pandemic. Professor Il-Doo Kim’s team in the Department of Materials Science and Engineering developed a washable nano-fiber filtered face mask that is preparing for commercialization.
GPS tracking of infections has expanded comprehensively to detect both indoor and outdoor activities of infected patients. Professor Dong-Soo Han from the School of Computing developed Wi-Fi positioning software built into mobile phones that can trace both activities and is now preparing to roll it out.
Virologist Ui-Cheol Shin from the Graduate School of Medical Science and Engineering is carrying out research on a universal T-cell vaccine that can block the Betacoronaviruses. It is reported that that new epidemics such as SARS, MERS, and COVID-19 carry Betacoronaviruses.
Research teams in the Graduate School of AI are conducting various research projects on building prediction models for outbreaks and spreads using big data.
(END)
2020.05.20 View 12497 -
Is it possible to identify rumors on SNS?
Rumors
sporadically spread with people with fewer followers in the centerResearched
over 100 rumors in the US from 2006 to 2009 Is it
possible to filter information on SNS such as Twitter and Facebook? A research
team led by Professor Mee-Young Cha from the Department of Cultural Technology
Graduate School at KAIST, Professor Kyo-Min Jung of Seoul National University,
Doctor Wei Chen and Yajun Wang of Microsoft Asia, has developed a technology
that can accurately filter out information on Twitter to 90% accuracy. The
research not only deduced a new mathematical model, network structure, and
linguistic characteristics on rumors from SNS data, but is also expected to
enhance the effort to make secure technology to regulate Internet rumors. The team
analysed the characteristics of rumors in over 100 widespread cases in the US
from 2006 to 2009 on Twitter. The team gathered data, which included a range of
areas such as politics, IT, health and celebrity gossips, and their analysis
could identify rumors to 90% accuracy. The filtering was more accurate in
rumors that included slanders or insults. The
research team identified three characteristics of the spread of rumors. Firstly,
rumors spread continuously. Normal news spreads widely once and is mentioned
rarely again on media, but rumors tend to continue for years. Secondly,
rumors spread through sporadic participation of random users with no
connections. Rumors start from people with fewer followers and spread to the
more popular. This phenomenon is often observed in rumors concerning
celebrities or politicians. Lastly,
rumors have unique linguistic characteristics. Rumors frequently include words
(such as “it may be true,” “although not certain, I think,” “although I cannot
fully remember”) related to psychological processes that question, deny, or
infer the reliability of the information. Professor
Cha said, “This research deduced not only a statistical and mathematical model
but also is an integrated research on social psychological theory on the
characteristics of rumors that attract great attention from the society based
on ample data.” The results
were made public in IEEE International Conference on Data Mining last December
in Texas, USA.
2014.02.03 View 10511 -
Sound of sex could alert internet porn filter by New Scientist, May 20, 2011
Software that can detect obscene contents from the internet has been developed by a research team at KAIST. The research team used a signal-processing technique, Randon Transform, to create spectrograms of a variety of audio clips, which can screen any pornographic sounds from websites. This audio-based screening method solves technological limits presented by automatic image-analysis systems that have already been used to catch unwanted pornography. New Scientist posted an online article on this development of new technology. Please copy and paste the following link to read more about the article.
http://www.newscientist.com/article/dn20498-sound-of-sex-could-alert-internet-porn-filter.html
2011.05.21 View 10112 -
H.Y.Choi won BSPA
H.Y.Choi won BSPA
Hyun-Young Choi, Doctor’s course at the Lightwave Systems Research Laboratory (LSRL) of Department of Electrical Engineering of KAIST (Professor in charge Yoonchul Jung), won the Best Student Paper Awards (BSPA) in the Asia-Pacific Optical Communications 2006.
BSPA is awarded to the most prospective paper in the field of Optical Transmission, Switching, and Subsystems. Choi suggested an OSNR monitoring technique among performance monitoring techniques for the efficient maintenance and management of optical network in her paper. Her technique is based on a polarization-nulling method using the polarization features of optical signals. It employs polarization mode dispersion compensator and acousto optic tunable filter (AOTF) to prevent monitoring errors arising from polarization mode dispersion (PMD) and non-linear double refraction, which considerably improves the monitoring technique and makes it possible to demonstrate a technique proposed at ultra long haul network.
2006.10.16 View 14928