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KAIST Professor Uichin Lee Receives Distinguished Paper Award from ACM
< Photo. Professor Uichin Lee (left) receiving the award >
KAIST (President Kwang Hyung Lee) announced on the 25th of October that Professor Uichin Lee’s research team from the School of Computing received the Distinguished Paper Award at the International Joint Conference on Pervasive and Ubiquitous Computing and International Symposium on Wearable Computing (Ubicomp / ISWC) hosted by the Association for Computing Machinery (ACM) in Melbourne, Australia on October 8.
The ACM Ubiquitous Computing Conference is the most prestigious international conference where leading universities and global companies from around the world present the latest research results on ubiquitous computing and wearable technologies in the field of human-computer interaction (HCI).
The main conference program is composed of invited papers published in the Proceedings of the ACM (PACM) on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), which covers the latest research in the field of ubiquitous and wearable computing.
The Distinguished Paper Award Selection Committee selected eight papers among 205 papers published in Vol. 7 of the ACM Proceedings (PACM IMWUT) that made outstanding and exemplary contributions to the research community. The committee consists of 16 prominent experts who are current and former members of the journal's editorial board which made the selection after a rigorous review of all papers for a period that stretched over a month.
< Figure 1. BeActive mobile app to promote physical activity to form active lifestyle habits >
The research that won the Distinguished Paper Award was conducted by Dr. Junyoung Park, a graduate of the KAIST Graduate School of Data Science, as the 1st author, and was titled “Understanding Disengagement in Just-in-Time Mobile Health Interventions”
Professor Uichin Lee’s research team explored user engagement of ‘Just-in-Time Mobile Health Interventions’ that actively provide interventions in opportune situations by utilizing sensor data collected from health management apps, based on the premise that these apps are aptly in use to ensure effectiveness.
< Figure 2. Traditional user-requested digital behavior change intervention (DBCI) delivery (Pull) vs. Automatic transmission (Push) for Just-in-Time (JIT) mobile DBCI using smartphone sensing technologies >
The research team conducted a systematic analysis of user disengagement or the decline in user engagement in digital behavior change interventions. They developed the BeActive system, an app that promotes physical activities designed to help forming active lifestyle habits, and systematically analyzed the effects of users’ self-control ability and boredom-proneness on compliance with behavioral interventions over time.
The results of an 8-week field trial revealed that even if just-in-time interventions are provided according to the user’s situation, it is impossible to avoid a decline in participation. However, for users with high self-control and low boredom tendency, the compliance with just-in-time interventions delivered through the app was significantly higher than that of users in other groups.
In particular, users with high boredom proneness easily got tired of the repeated push interventions, and their compliance with the app decreased more quickly than in other groups.
< Figure 3. Just-in-time Mobile Health Intervention: a demonstrative case of the BeActive system: When a user is identified to be sitting for more than 50 mins, an automatic push notification is sent to recommend a short active break to complete for reward points. >
Professor Uichin Lee explained, “As the first study on user engagement in digital therapeutics and wellness services utilizing mobile just-in-time health interventions, this research provides a foundation for exploring ways to empower user engagement.” He further added, “By leveraging large language models (LLMs) and comprehensive context-aware technologies, it will be possible to develop user-centered AI technologies that can significantly boost engagement."
< Figure 4. A conceptual illustration of user engagement in digital health apps. Engagement in digital health apps consists of (1) engagement in using digital health apps and (2) engagement in behavioral interventions provided by digital health apps, i.e., compliance with behavioral interventions. Repeated adherences to behavioral interventions recommended by digital health apps can help achieve the distal health goals. >
This study was conducted with the support of the 2021 Biomedical Technology Development Program and the 2022 Basic Research and Development Program of the National Research Foundation of Korea funded by the Ministry of Science and ICT.
< Figure 5. A conceptual illustration of user disengagement and engagement of digital behavior change intervention (DBCI) apps. In general, user engagement of digital health intervention apps consists of two components: engagement in digital health apps and engagement in behavioral interventions recommended by such apps (known as behavioral compliance or intervention adherence). The distinctive stages of user can be divided into adoption, abandonment, and attrition. >
< Figure 6. Trends of changes in frequency of app usage and adherence to behavioral intervention over 8 weeks, ● SC: Self-Control Ability (High-SC: user group with high self-control, Low-SC: user group with low self-control) ● BD: Boredom-Proneness (High-BD: user group with high boredom-proneness, Low-BD: user group with low boredom-proneness). The app usage frequencies were declined over time, but the adherence rates of those participants with High-SC and Low-BD were significantly higher than other groups. >
2024.10.25 View 16102 -
AI to Determine When to Intervene with Your Driving
(Professor Uichin Lee (left) and PhD candidate Auk Kim)
Can your AI agent judge when to talk to you while you are driving? According to a KAIST research team, their in-vehicle conservation service technology will judge when it is appropriate to contact you to ensure your safety.
Professor Uichin Lee from the Department of Industrial and Systems Engineering at KAIST and his research team have developed AI technology that automatically detects safe moments for AI agents to provide conversation services to drivers.
Their research focuses on solving the potential problems of distraction created by in-vehicle conversation services. If an AI agent talks to a driver at an inopportune moment, such as while making a turn, a car accident will be more likely to occur.
In-vehicle conversation services need to be convenient as well as safe. However, the cognitive burden of multitasking negatively influences the quality of the service. Users tend to be more distracted during certain traffic conditions. To address this long-standing challenge of the in-vehicle conversation services, the team introduced a composite cognitive model that considers both safe driving and auditory-verbal service performance and used a machine-learning model for all collected data.
The combination of these individual measures is able to determine the appropriate moments for conversation and most appropriate types of conversational services. For instance, in the case of delivering simple-context information, such as a weather forecast, driver safety alone would be the most appropriate consideration. Meanwhile, when delivering information that requires a driver response, such as a “Yes” or “No,” the combination of driver safety and auditory-verbal performance should be considered.
The research team developed a prototype of an in-vehicle conversation service based on a navigation app that can be used in real driving environments. The app was also connected to the vehicle to collect in-vehicle OBD-II/CAN data, such as the steering wheel angle and brake pedal position, and mobility and environmental data such as the distance between successive cars and traffic flow.
Using pseudo-conversation services, the research team collected a real-world driving dataset consisting of 1,388 interactions and sensor data from 29 drivers who interacted with AI conversational agents. Machine learning analysis based on the dataset demonstrated that the opportune moments for driver interruption could be correctly inferred with 87% accuracy.
The safety enhancement technology developed by the team is expected to minimize driver distractions caused by in-vehicle conversation services. This technology can be directly applied to current in-vehicle systems that provide conversation services. It can also be extended and applied to the real-time detection of driver distraction problems caused by the use of a smartphone while driving.
Professor Lee said, “In the near future, cars will proactively deliver various in-vehicle conversation services. This technology will certainly help vehicles interact with their drivers safely as it can fairly accurately determine when to provide conversation services using only basic sensor data generated by cars.”
The researchers presented their findings at the ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp’19) in London, UK. This research was supported in part by Hyundai NGV and by the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.
(Figure: Visual description of safe enhancement technology for in-vehicle conversation services)
2019.11.13 View 25235 -
Object Identification and Interaction with a Smartphone Knock
(Professor Lee (far right) demonstrate 'Knocker' with his students.)
A KAIST team has featured a new technology, “Knocker”, which identifies objects and executes actions just by knocking on it with the smartphone. Software powered by machine learning of sounds, vibrations, and other reactions will perform the users’ directions.
What separates Knocker from existing technology is the sensor fusion of sound and motion. Previously, object identification used either computer vision technology with cameras or hardware such as RFID (Radio Frequency Identification) tags. These solutions all have their limitations. For computer vision technology, users need to take pictures of every item. Even worse, the technology will not work well in poor lighting situations. Using hardware leads to additional costs and labor burdens.
Knocker, on the other hand, can identify objects even in dark environments only with a smartphone, without requiring any specialized hardware or using a camera. Knocker utilizes the smartphone’s built-in sensors such as a microphone, an accelerometer, and a gyroscope to capture a unique set of responses generated when a smartphone is knocked against an object. Machine learning is used to analyze these responses and classify and identify objects.
The research team under Professor Sung-Ju Lee from the School of Computing confirmed the applicability of Knocker technology using 23 everyday objects such as books, laptop computers, water bottles, and bicycles. In noisy environments such as a busy café or on the side of a road, it achieved 83% identification accuracy. In a quiet indoor environment, the accuracy rose to 98%.
The team believes Knocker will open a new paradigm of object interaction. For instance, by knocking on an empty water bottle, a smartphone can automatically order new water bottles from a merchant app. When integrated with IoT devices, knocking on a bed’s headboard before going to sleep could turn off the lights and set an alarm. The team suggested and implemented 15 application cases in the paper, presented during the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2019) held in London last month.
Professor Sung-Ju Lee said, “This new technology does not require any specialized sensor or hardware. It simply uses the built-in sensors on smartphones and takes advantage of the power of machine learning. It’s a software solution that everyday smartphone users could immediately benefit from.” He continued, “This technology enables users to conveniently interact with their favorite objects.”
The research was supported in part by the Next-Generation Information Computing Development Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT and an Institute for Information & Communications Technology Promotion (IITP) grant funded by the Ministry of Science and ICT.
Figure: An example knock on a bottle. Knocker identifies the object by analyzing a unique set of responses from the knock, and automatically launches a proper application or service.
2019.10.02 View 35069 -
Professor Junehwa Song Appointed as the General Chair of the Organizing Committee of ACM SenSys
Professor Junehwa Song from the Schooling of Computing at KAIST has been appointed the general chair of the organizing committee of ACM SenSys—the American Computing Machine (ACM) Conference on Embedded Networked Sensor Systems.
ACM SenSys held its first conference in 2003 to promote research on wireless sensor networks and embedded systems. Since then, it has expanded into an influential international conference especially with the increasing importance in sensor technologies. Recently the committee has expanded its field of interest to mobile sensors, the Internet of Things, smart device system, and security.
Professor Song is considered a world-renown researcher in mobile and ubiquitous computing system. He presented numerous research papers at various conferences organized by ACM. He is also a member of the editorial committee of the Institute of Electrical and Electronics Engineers (IEEE) Transactions on Mobile Computing journal. For his achievements in the field and flair for coordinating and planning conferences, he is now the first Korean researcher to be appointed the chair of ACM SenSys.
Professor Song said that, as the chair, he would help discover new technology in and applications of networked, wireless sensors that would meet the demands of our modern society.
The 13th ACM SenSys will take place in Seoul—the first one to be held in Asia. The event will begin on November 1, 2015 and last four days. More information about this year’s event can be found at http://sensys.acm.org/2015/.
2015.10.02 View 11589 -
KAIST developed an extremely low-powered, high-performance head-mounted display embedding an augmented reality chip
Walking around the streets searching for a place to eat will be no hassle when a head-mounted display (HMD) becomes affordable and ubiquitous. Researchers at the Korea Advanced Institute of Science and Technology (KAIST) developed K-Glass, a wearable, hands-free HMD that enables users to find restaurants while checking out their menus. If the user of K-Glass walks up to a restaurant and looks at the name of the restaurant, today’s menu and a 3D image of food pop up. The Glass can even show the number of tables available inside the restaurant. K-Glass makes this possible because of its built-in augmented reality (AR) processor. Unlike virtual reality which replaces the real world with a computer-simulated environment, AR incorporates digital data generated by the computer into the reality of a user. With the computer-made sensory inputs such as sound, video, graphics or GPS data, the user’s real and physical world becomes live and interactive. Augmentation takes place in real-time and in semantic context with surrounding environments, such as a menu list overlain on the signboard of a restaurant when the user passes by it, not an airplane flight schedule, which is irrelevant information, displayed. Most commonly, location-based or computer-vision services are used in order to generate AR effects. Location-based services activate motion sensors to identify the user’s surroundings, whereas computer-vision uses algorithms such as facial, pattern, and optical character recognition, or object and motion tracking to distinguish images and objects. Many of the current HMDs deliver augmented reality experiences employing location-based services by scanning the markers or barcodes printed on the back of objects. The AR system tracks the codes or markers to identify objects and then align them with virtual reality. However, this AR algorithm is difficult to use for the objects or spaces which do not have barcodes, QR codes, or markers, particularly those in outdoor environments and thus cannot be recognized. To solve this problem, Hoi-Jun Yoo, Professor of Electrical Engineering at KAIST and his team developed, for the first time in the world, an AR chip that works just like human vision. This processor is based on the Visual Attention Model (VAM) that duplicates the ability of human brain to process visual data. VAM, almost unconsciously or automatically, disentangles the most salient and relevant information about the environment in which human vision operates, thereby eliminating unnecessary data unless they must be processed. In return, the processor can dramatically speed up the computation of complex AR algorithms. The AR processor has a data processing network similar to that of a human brain’s central nervous system. When the human brain perceives visual data, different sets of neurons, all connected, work concurrently on each fragment of a decision-making process; one group’s work is relayed to other group of neurons for the next round of the process, which continues until a set of decider neurons determines the character of the data. Likewise, the artificial neural network allows parallel data processing, alleviating data congestion and reducing power consumption significantly. KAIST’s AR processor, which is produced using the 65 nm (nanometers) manufacturing process with the area of 32 mm2, delivers 1.22 TOPS (tera-operations per second) peak performance when running at 250 MHz and consumes 778 miliWatts on a 1.2V power supply. The ultra-low power processor shows 1.57 TOPS/W high efficiency rate of energy consumption under the real-time operation of 30fps/720p video camera, a 76% improvement in power conservation over other devices. The HMDs, available on the market including the Project Glass whose battery lasts only for two hours, have revealed so far poor performance. Professor Yoo said, “Our processor can work for long hours without sacrificing K-Glass’s high performance, an ideal mobile gadget or wearable computer, which users can wear for almost the whole day.” He further commented:“HMDs will become the next mobile device, eventually taking over smartphones. Their markets have been growing fast, and it’s really a matter of time before mobile users will eventually embrace an optical see-through HMD as part of their daily use. Through augmented reality, we will have richer, deeper, and more powerful reality in all aspects of our life from education, business, and entertainment to art and culture.” The KAIST team presented a research paper at the International Solid-State Circuits Conference (ISSCC) held on February 9-13, 2014 in San Francisco, CA, which is entitled “1.22TOPS and 1.52mW/MHz Augmented Reality Multi-Core Processor with Neural Network NoC for HMD Applications.”Youtube Link: http://www.youtube.com/watch?v=wSqY30FOu2s&feature=c4-overview&list=UUirZA3OFhxP4YFreIJkTtXw
2014.02.20 View 21637 -
3rd Ubiquitous Fashionable Computer Contest
KAIST will be receiving until May 31, Thursday, applications for ‘the 3rd Ubiquitous Fashionable Computer (UFC)’ Contest, which will take place under the title of ‘Enjoy U-life with UFC’.
The contest has begun in 2005 by KAIST and the Korean Society for Next-Generation Computing to raise people’s concern over next-generation computing and to prepare for the upcoming ubiquitous era.
‘UFC’ refers to a wearable computer small and light enough to be worn on human bodies or clothes so that users can use computers with no restriction while moving. This terminology was created by Korea.
The contest includes designated items division and free items division, and not only university students but also general public can participate in the free items division. Teams qualified for the final contest in the designated items division will be offered wearable computer platform and 1.5 million won of production cost. The final contest will take place at the UFC fashion show stage ‘Next-Generation Computing Exhibition’ at KOEX in November.
Hee-Joon You, Co-president of the Contest Committee and a professor of Electrical Engineering, stressed on the future life made joyful by IT technologies by saying, “Considering the title of the contest, we’ve selected ‘games enjoyed with UFCs’ as a mission of the designated items division to combine games, rising software, and wearing computers, hardware.”
UFC is a brand-new field that fuses IT technologies and fashion, seeking the improvement of computer technologies and fashion creation. UFC, a further advanced wearable computer than existing ones, is an important advanced field that leads computer industries in the ubiquitous era.
2007.04.23 View 20903 -
Final competition of 'UFC' contest
Final competition of ‘UFC’ contest
Joint university team D-M2 won first prize
The final winner of ‘the 2nd Ubiquitous Fashionable Computer (UFC) contest‘, co-hosted by KAIST and the Korean Society for Next-Generation Computing (KSNGC), was determined.
At the final competition of November 17 among 9 qualified teams, the first prize went to D-M2, composed of students from Seokyeong University, Kookmin University, Hongik University, and Sungshin Women’s University. D-M2 manufactured a work utilizing a user’s motion information by applying motion capture technologies to UFC. Particularly, the work gained a high score at the item of the functional perfection by controlling the robot according to a user’s motion.
The gold prize went to the smart jacket by Jjik-eo-cha-ki (Kwangwoon University and Duksung Women’s University). The smart jacket is embedded with an intelligent clothes function in terms of checking a user’s status in a real-time basis and delivering it to a doctor, etc.
The silver and bronze prizes went to Samsung Software Membership (SSM) and Hanse University, respectively. SSM manufactured a training suit with sensors for grasping the movement of each articulation of a user built-in, and Hanse University developed a system enabling blinders to get a voice service of general documents or books regardless of time and place.
The participants composed of university students or graduate students have passed the severe qualifying contest through the examination of written plan and presentation of last April and manufactured creative works that realize the fusion of IT technologies and fashion.
At the contest, that fact that all winners of the first, gold, and silver prizes were the members of SSM gained more attention.
UFC is a new field that pursuits the enhancement of computer technologies and the creation of fashion simultaneously by fusing IT technologies and fashions. UFC is a one-step advanced field of the existing wearable computer and an important cutting-edge field that leads a computer industry in the era of ubiquitous.
“The level of the works exhibited was higher than I’d expected and the cooperation between the departments of Closing Textile and Electronics appeared to be so positive, which made me expect more brilliant future of the next-generation computing industry,” said Hoijoon You, Co-chairman of the contest and professor of the department of Electrical Engineering.
2006.11.27 View 23058