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…
A universal and rapid bacteria detection method in diverse target media is imperative for food safety analysis and diagnosis of infectious diseases. Surface-enhanced Raman spectroscopy (SERS) shows prospective as a potentially practical method to rapidly 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…
We develop a wearable ear-EEG acquisition tool and directly compared its performance with a conventional 32-channel scalp-EEG setup in a multi-class speech imagery classification task. Our system uses Riemannian tangent space projection of EEG covariance 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…
Despite recent advances in the field of explainable artificial intelligence systems, a concrete quantitative measure for evaluating the usability of such systems is nonexistent. Ensuring the success of an explanatory interface in interacting with users Read more…
Visual information plays an essential role in enhancing neural activity during mental practices. This study therefore hypothesized that applying immersive virtual reality headsets, with their ability to provide rich immersion and illusion by presenting egocentrically 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…
Depression is a complicated mental illness that varies in symptoms and needs the cooperation of patients. We propose a novel data-driven attempt to diagnose Major Depression Disorder (MDD) based on clinical questionnaire. It includes deep 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 For hand robots that assist with activities of daily living, this study presents a paradigm for learning-based intention detection based methodologies that perceive user Read more…
Past research on recognizing human affect has made use of a variety of physiological sensors in many ways. Nonetheless, how affective dynamics are influenced in the context of human daily life has not yet been Read more…
This study proposes a computational framework for providing affective labels to real-life situations. We first define an affective situation as a specific arrangement of affective entities relevant to emotion elicitation in an affective situation. Then, Read more…
We investigate a method to decide the weight decay parameter values of a deep convolutional neural network that yields a good generalization. To obtain such a CNN in practice, numerical trials with different weight decay 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…
We propose a robust physiological model for the recognition of human emotions, which is based on a Convolutional Long Short-Term Memory network and a temporal margin-based loss function. Formulating the emotion recognition problem as Read more…
We investigate the path planning problem for autonomous 3D modeling of a large outdoor structure with micro-aerial vehicle. For this problem, there are two categories of the next-best-view algorithm: volumetric and surface-based approaches. The volumetric Read more…