It is important to maintain the sensation of moving our bodies as we wish and feeling various things for ourselves in order to enjoy our daily lives even as we age. Tools and information technology have the potential to overcome the barriers and limitations of our physical abilities and bring us unknown experiences. We are developing human-enhancement assistive devices that extend and improve human movement and sensation functions and capabilities to realize this.

Tactile sensation, one of the components of texture, significantly impacts product evaluation, so a quantitative evaluation method for tactile sensation is needed. Conventional tactile evaluation techniques, however, require the preparation of actual samples to be measured by sensors, which is costly in terms of sample production. To solve this problem, we are developing a Digital Haptics Design (DSD) tool that supports the creation of texture patterns on a computer by predicting the tactile response in advance.

We aim to apply engineering and information processing technologies to different fields and develop devices that support discoveries and specialists. In particular, we focus on applications to the medical, nursing, and healthcare fields and conduct joint research with medical professionals.

We are conducting research and development of remote cockpits and interfaces for operating unmanned construction equipment remotely, with the aim of accelerating technological innovation in construction equipment and the on-site environment in which construction equipment operates, thereby contributing to the realization of a sustainable society.

Advances in computer technology are making it possible to reproduce the musculoskeletal system's physiological, mechanistic, and mechanical properties using computers. By applying these body modeling techniques, we are researching methods to evaluate the characteristics of human movement and sensation with clear physical evidence and to link this to assistive technologies for smarter human support and more user-friendly product design.

TMS is widely used in the treatment of psychiatric disorders and post-stroke rehabilitation, currently relies on skilled professionals for accurate delivery. This study aims to develop an automated TMS system using AI and robotics to enhance precision and reproducibility. By enabling intelligent control that identifies and adjusts the optimal stimulation site in real time based on individual brain anatomy, we seek to facilitate future applications in clinical and home-based settings.

Techniques that visualize and guide brain states, such as neurofeedback using EEG and neuromodulation via electrical or magnetic stimulation, are gaining increasing attention. This research explores non-invasive methods to enhance motor and cognitive functions by combining these approaches. With potential applications in education, medicine, and industry, the project seeks to establish scientifically grounded brain enhancement technologies.

Using 126 electrodes placed on the bottom of an aquarium, we measured the bioelectrical signals of zebrafish (Danio rerio), which are about 3 cm long. By extracting respiratory and motor information from the bioelectrical signals and discriminating them using a neural network, we have succeeded in estimating the emotional states of the fish, such as fear/anxiety and pleasure states.

This study proposed a neural network model of Caenorhabditis elegans, for which the connectome has been elucidated. The connectome-based neural network model enables the simulation of chemotaxis, and is thus expected to contribute to the design of guidelines for actual biological experiments that aim to determine the information processing mechanism of chemotaxis.

This study focused on the bioelectric (ventilatory) signals of small fish and proposed a bioassay system for detecting water pollution by simultaneously monitoring fish behavior and ventilatory signals.

To predict the perceptual similarities of odor pairs from neural activities of the olfactory system, we proposed a neural net model based on the olfactory system structure.

A phenomenon known as stochastic resonance (SR) improves the perceivable threshold based on the application of weak vibration to receptors. We proposed the concept of surgical grasping forceps with enhanced sensorimotor capability by attaching a vibrator to the forceps’ grip and evoking the SR effect.

We developed a haptic force display system that renders different levels of stiffness using electrical muscle stimulation (EMS). We analyzed the performance of our system by measuring the perceived stiffness in human experiments. The experimental results show a positive correlation between the target stiffness and perceived stiffness.

The purpose of this study was to develop a sensorimotor-enhancing suit (SEnS)—unpowered assistive clothing without actuators or electrical devices—and examine its efficacy for reducing voluntary muscle activation and improving force reproduction in the upper limbs of healthy young adults.