Human Enhancement & Assistive Technology Research Division
- Physical Augmentation Research SectionThe Physical Augmentation Research Section develops core technologies for physical augmentation and assistive systems by integrating artificial intelligence, human behavior understanding, and wearable technologies. The section focuses on understanding human movement, behavior, and physical conditions and developing intelligent technologies that support and enhance physical capabilities.
Key research areas include human-centered behavior and cognition modeling, adaptive AI for strength assistance and physical augmentation, and soft wearable platforms that naturally conform to the human body. The section also explores optical computing-based AI technologies for real-time and energy-efficient processing of physical AI systems.
By developing personalized physical augmentation technologies that adapt to users’ intentions and physical conditions, the section aims to support daily activities, reduce physical burden, assist people with disabilities and older adults, and improve safety and efficiency in industrial environments. Ultimately, the section seeks to realize human-centered physical AI technologies that enable people to perform physical activities more safely, independently, and effectively.
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- Sensory-Perception AX Research SectionThe Sensory-Perception AX Research Section develops core technologies for restoring, substituting, and enhancing human sensory and cognitive capabilities by integrating artificial intelligence, human-computer interaction (HCI), and brain-computer interface (BCI) technologies.
Key research areas include multimodal AI for sensory and cognitive understanding, neural foundation models, and intelligent BCI technologies. The section analyzes multimodal information from visual, auditory, physiological, and environmental sensors to understand human sensory, perceptual, and cognitive states. It also addresses key challenges in real-world AI applications, including limited training data, model bias, performance degradation caused by individual and environmental changes, and computational efficiency.
The section further integrates neural signals with sensory and behavioral data to understand users’ intentions and cognitive states and develops technologies that adapt information and sensory feedback accordingly. Through intelligent BCI and neural foundation models, it aims to enable more natural interaction between humans and intelligent systems while expanding human sensory and cognitive capabilities.
Ultimately, the section seeks to create inclusive and intelligent sensory-cognitive environments that enable people to perceive, understand, and interact with the world beyond their physical and environmental limitations.
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- Life Care AX Research SectionThe Life Care AX Research Section develops AI technologies that integrate physiological, medical, and everyday behavioral data to understand individual health conditions and life contexts, predict health changes and risks, and provide personalized prevention, management, and intervention.
Key research areas include robust physiological and medical data analysis, explainable medical large language models (LLMs), physical and mental health prediction, personal context memory, active sensing, and health agents. By continuously observing individuals through low-burden sensing technologies such as wearables and smartphones, the section develops AI systems that understand changes in health and daily life and determine when additional sensing, clinical testing, expert review, coaching, or early intervention is needed.
The section applies these technologies to emergency medicine, chronic disease management, mental health, medical imaging, and treatment support. Ultimately, it aims to realize agentic life-care AI that continuously understands individual needs, proactively coordinates personalized care, and supports healthier and more independent lives throughout everyday life.
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- Human Precision Sensing Research SectionThe Human Precision Sensing Research Section develops technologies for precisely measuring and analyzing human physiological signals and physical responses to objectively understand and predict individual health and functional states.
Key research areas include high-precision biosensors, multimodal physiological sensing, AI-based human state analysis and prediction, and precision stimulation technologies. The section develops sensors, sensing modules, and integrated sensing systems capable of reliably measuring subtle electrical, optical, and physical signals generated by the human body. It also develops technologies for processing and integrating multimodal physiological signals to quantitatively characterize human conditions and functions.
The section further develops AI-based prediction and visualization technologies and precision stimulation technologies using physical modalities such as light, electric fields, and ultrasound. By integrating sensing, analysis, prediction, and stimulation according to individual physiological responses, it aims to establish personalized human enhancement systems based on precision sensing.
Ultimately, the section seeks to provide advanced technological foundations for more accurate understanding, prediction, and enhancement of human health and physical functions, contributing to personalized healthcare and human enhancement technologies.
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- Brain Links Research SectionThe Brain Links Research Section develops implantable, bidirectional brain-computer interface (BCI) technologies that directly connect the human brain with digital devices. The section aims to restore impaired functions caused by neurological disorders and extend human sensory and cognitive capabilities through next-generation neural interface technologies.
Key research areas include high-precision neural signal acquisition and decoding, on-device AI for real-time BCI, multimodal neural decoding, brain stimulation, and Social BCI. The section develops ultra-low-power, on-device AI technologies for real-time neural signal processing, as well as neural decoding and stimulation technologies for artificial sensory feedback, cognitive enhancement, and neurorehabilitation.
Beyond decoding motor intentions, the section explores Social BCI technologies that understand complex emotional and social states, including affective conditions and social contexts. These technologies aim to enable intelligent systems and robots to provide context-aware and empathetic feedback, supporting more natural human-machine interaction.
Ultimately, the section seeks to create human-centered neural interfaces that restore impaired functions, expand human capabilities, and enable natural communication and interaction between humans and intelligent machines.
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- Tangible Interface Research SectionThe Tangible Interface Research Section develops next-generation high-resolution tangible interfaces that generate and dynamically transform three-dimensional shapes and tactile sensations on flat surfaces. The section aims to create intuitive and immersive interaction technologies that allow users to physically touch, feel, and interact with digital information.
Key research areas include light-driven shape generation, variable physical properties, high-resolution tactile interfaces, and physics-based large-area control technologies. The section utilizes the physical properties of light-responsive materials to precisely control surface height, elasticity, and temperature, enabling the creation and reconstruction of Braille, characters, three-dimensional shapes, and diverse textures. By controlling infrared light intensity, the section develops energy-efficient interfaces capable of generating finely tunable physical forms and maintaining their shape without continuous power consumption.
The section further develops physics-based modeling and large-area control technologies to precisely regulate the physical properties of individual cells while improving spatial resolution and energy efficiency. These technologies enable the reproduction of realistic three-dimensional surfaces, terrain, and textures.
Ultimately, the section seeks to expand human-computer interaction beyond conventional visual and auditory interfaces into rich visual-tactile experiences, with applications in accessible information delivery, adaptive automotive interfaces, tactile education, and human-to-human communication.
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