Our lab have proposed deep learning models in various research areas such as robotics, medical diagnosis, animal research. Also, we investigate how to train deep neural networks efficiently.
We develope a novel and user-friendly system that analyzes tennis shots using a sensor placed on the passive arm. Collecting Inertial Measurement Unit sensor data from 20 recreational tennis players, we implemented neural networks that Read more…
For stroke survivors, diminished hand functions limit their ability to perform Activities of Daily Living (ADLs). To help stroke survivors perform rehabilitation exercises, soft robotic gloves have been utilized to assist them in actively practicing Read more…
We report a substrate-less nanomesh receptor that is coupled with an unsupervised meta-learning framework and can provide user-independent, data-efficient recognition of different hand tasks. The nanomesh, which is made from biocompatible materials and can be Read more…
This study addresses an image-matching problem in challenging cases, such as large scene variations or textureless scenes. To gain robustness to such situations, most previous studies have attempted to encode the global contexts of a Read more…
We propose a novel view path-planning method based on an online Multi-View Stereo (MVS) system. This method aims to incrementally construct the target three-dimensional (3D) model in real time. View paths are continually planned based Read more…
Soft wearable robotic gloves based on tendon-sheath mechanism are widely developed for assisting people with a loss of hand mobility. For these robots, knowing the fingertip forces applied to deformable objects is crucial in successfully Read more…
While data-driven approaches could model complex sensor patterns, the required amount of labeled data increases exponentially as the number of contact points or the number of sensor subelements increase. We propose a novel deep learning Read more…
We propose a new Riemannian-based deep learning network to generate more discriminative features for electroencephalogram (EEG) classification. Our key innovation lies in learning the Riemannian barycenter for each class within a Riemannian geometric space. The Read more…
We develop an ultra-sensitive skin-like sensor that discovers previously undetectable signals far from the joint, assisted by a deep neural network that deciphers patterns within the signals. By utilizing a single sensor capable of extracting Read more…
Soft wearable hand robots with tendon-sheath mechanisms are being actively developed to assist people with lost hand mobility. For these robots, accurately estimating fingertip forces leads to successful object grasping. An approach can utilize information Read more…
Developing Soft Robotics To Assist Users With Hand-Object Interactions [의도 파악하는 웨어러블 손 로봇 관련 기사들] For hand robots that assist with activities of daily living, this study presents a paradigm for learning-based intention Read more…
We present a gait motion generating method using only two microfluidic sensors. We select appropriate sensor positions with consideration of the deformation patterns of the lower-limb skins and mutual interference with soft actuators. A Read more…
We propose use of deep learning for full-body motion sensing, which significantly increases efficiency in calibration and estimation. The sensing suit is made of stretchable fabric and contains 20 soft strain sensors distributed on Read more…
Soft sensors made of highly deformable materials are one of the enabling technologies to various soft robotic systems. However, major drawbacks of soft sensors compared with traditional sensors are their nonlinearity and hysteresis in response, Read more…