Tamon Miyake 교수

One can roughly estimate eye-gaze direction of a person or a portrait. In particular, it seems likely that one can accurately recognize whether or not one meets a person's eyes directly. If a machine realizes such visual information processing as a man does, one can tell one's intention to the machine with one's eyes. Most previous methods of eye-gaze estimation use a special light source or a hardware device. These methods however are not user friendly because they require the user to restrict

The shortage of nurses and the increasing elderly population demand robots in nursing that can carry out care tasks safely and intelligently. In this study, the method of skeleton recognition-based motion generation of the humanoid robot for the human range-of-motion training with dual 7-DOF arm manipulation is developed. Mediapipe-based skeleton recognition is installed with humanoid robot to recognize the human pose even though the whole of body is not seen by a camera. The 7-DOF arm was contr

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection alg

Hand-gripping training is important for improving the fundamental functions of human physical activity. Bernstein's idea of "repetition without repetition" suggests that motor control function should be trained under changing states. The randomness level of load should be visualized for self-administered screening when repeating various training tasks under changing states. This study aims to develop a sensing methodology of random loads applied to both the agonist and antagonist skeletal muscle

Controlling minimum toe clearance (MTC) is considered an important factor in preventing tripping. In the current study, we investigated modifications of neuro-muscular control underlying toe clearance during steady locomotion induced by repeated exposure to tripping-like perturbations of the right swing foot. Fourteen healthy young adults (mean age 26.4 ± 3.1 years) participated in the study. The experimental protocol consisted of three identical trials, each involving three phases: steady walki

Gait phase detection and quantitative evaluation are significant for synchronous robotic assistance of human walking, rehabilitation training, or diagnosis of human motion state. Especially, accurate heel-contact detection in a gait cycle is a key requirement for gait analysis applications. Some techniques have been proposed by utilizing wearable devices, however, existing systems typically require precise and continuous time-series data at every single timestep for calibration, which largely in

The detection of gait events with wearable sensors is necessary for a robotic system interacting with walking people. Conventional gait phase detection methods are based on machine learning. However, this method cannot detect a gait event every gait cycle because it is difficult to extract characteristic points. Additionally, using only angular information for detection is beneficial because angular information is needed for the control and evaluation of the robots. This paper proposes a novel a

The adaptive control of gait training robots is aimed at improving the gait performance by assisting motion. In conventional robotics, it has not been possible to adjust the robotic parameters by predicting the toe motion, which is considered a tripping risk indicator. The prediction of toe clearance during walking can decrease the risk of tripping. In this paper, we propose a novel method of predicting toe clearance that uses a radial basis function network. The input data were the angles, angu

Adaptive assistance of gait training robots has been determined to improve gait performance through motion assistance. An important control role during walking is to avoid tripping by controlling minimum toe clearance (MTC), which is an indicator of tripping risk, to avoid its decrease among gait cycles. No conventional gait training robots can adjust assistance timing based on MTC. In this paper, we propose a system that applies force intermittently based on the MTC prediction algorithm to enco

Gait assistance robots are used to improve gait performance ability or perform gait motion with an assistance for several articular motions. The sparing use of a gait assistance robot to decrease the duration of the robot’s assistance is important for keeping the ability to perform a movement when the robot assists walking. In previous research, methods of ensuring a compliance mechanism and control method have been studied, and assistance for articular motions has been conducted independently u

Gait training robots are useful for changing gait patterns and decreasing risk of trip. Previous research has reported that decreasing duration of the assistance or guidance of the robot is beneficial for efficient gait training. Although robotic intermittent control method for assisting joint motion has been established, the effect of the robot intervention timing on change of toe clearance is unclear. In this paper, we tested different timings of applying torque to the knee, employing the inte

An ability of visually-guided and anticipatory adjustments of locomotion corresponding to upcoming obstacles is important to avoid trip-induced fall. For establishing gait training based on visually-guided and anticipatory adjustments, techniques reproducing realistic training environment are essential. Although some previous works proposed virtual obstacles using mixed reality, the feasibility of virtual obstacles encouraging people to perform realistic obstacle negotiation on a treadmill, wher