• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.304-313
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• 2006

This paper describes a novel way of applying capacitive sensing technology to a mobile user interface. The key idea is to use grip-pattern, which is naturally produced when a user tries to use the mobile device, as a clue to determine an application to be launched. To this end, a capacitive touch sensing system is carefully designed and installed underneath the housing of the mobile device to capture the information of the user's grip-pattern. The captured data is then recognized by dedicated recognition algorithms. The feasibility of the proposed user interface system is thoroughly evaluated with various recognition tests.

• Journal of Sensor Science and Technology
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• v.32 no.1
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• pp.22-30
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• 2023

Innovative and advanced technologies, including robots, augmented reality, virtual reality, the Internet of Things, and wearable medical equipment, have largely emerged as a result of the rapid evolution of modern society. For these applications, pressure monitoring is essential and pressure sensors have attracted considerable interest. To improve the sensor performance, several new designs of pressure sensors have been researched based on resistive, capacitive, piezoelectric, optical, and triboelectric types. In particular, optical pressure sensors have been actively studied owing to their advantages, such as robustness to noise and remote sensing capability. Herein, a review of recent research on optical pressure sensors with self-powered sensing, remote sensing, high spatial resolution, and multimodal sensing capabilities is presented from the viewpoints of design, fabrication, and signal processing.

• Investigative Magnetic Resonance Imaging
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• v.25 no.4
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• pp.218-228
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• 2021

Due to their high degree of freedom to transfer and acquire light, fiber optics can be used in the presence of strong magnetic fields. Hence, optical sensing and imaging based on fiber optics can be integrated with magnetic resonance imaging (MRI) diagnostic systems to acquire valuable information on biological tissues and organs based on a magnetic field. In this article, we explored the combination of MRI and optical sensing/imaging techniques by classifying them into the following topics: 1) functional near-infrared spectroscopy with functional MRI for brain studies and brain disease diagnoses, 2) integration of fiber-optic molecular imaging and optogenetic stimulation with MRI, and 3) optical therapeutic applications with an MRI guidance system. Through these investigations, we believe that a combination of MRI and optical sensing/imaging techniques can be employed as both research methods for multidisciplinary studies and clinical diagnostic/therapeutic devices.

• Journal of Information Processing Systems
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• v.13 no.1
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• pp.8-25
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• 2017

This survey paper explores the application of multimodal feedback in automated systems for motor learning. In this paper, we review the findings shown in recent studies in this field using rehabilitation and various motor training scenarios as context. We discuss popular feedback delivery and sensing mechanisms for motion capture and processing in terms of requirements, benefits, and limitations. The selection of modalities is presented via our having reviewed the best-practice approaches for each modality relative to motor task complexity with example implementations in recent work. We summarize the advantages and disadvantages of several approaches for integrating modalities in terms of fusion and frequency of feedback during motor tasks. Finally, we review the limitations of perceptual bandwidth and provide an evaluation of the information transfer for each modality.

• ETRI Journal
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• v.44 no.3
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• pp.426-437
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• 2022

To understand the multilateral characteristics of human behavior and physiological markers related to physical, emotional, and environmental states, extensive lifelog data collection in a real-world environment is essential. Here, we propose a data collection method using multimodal mobile sensing and present a long-term dataset from 22 subjects and 616 days of experimental sessions. The dataset contains over 10 000 hours of data, including physiological, data such as photoplethysmography, electrodermal activity, and skin temperature in addition to the multivariate behavioral data. Furthermore, it consists of 10 372 user labels with emotional states and 590 days of sleep quality data. To demonstrate feasibility, human activity recognition was applied on the sensor data using a convolutional neural network-based deep learning model with 92.78% recognition accuracy. From the activity recognition result, we extracted the daily behavior pattern and discovered five representative models by applying spectral clustering. This demonstrates that the dataset contributed toward understanding human behavior using multimodal data accumulated throughout daily lives under natural conditions.

• Journal of Applied and Pure Mathematics
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• v.5 no.5_6
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• pp.389-406
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• 2023

In the pursuit of enhancing image fusion techniques, this research presents a novel approach for fusing multimodal images, specifically infrared (IR) and visible (VIS) images, utilizing a combination of partial differential equations (PDE) and discrete cosine transformation (DCT). The proposed method seeks to leverage the thermal and structural information provided by IR imaging and the fine-grained details offered by VIS imaging create composite images that are superior in quality and informativeness. Through a meticulous fusion process, which involves PDE-guided fusion, DCT component selection, and weighted combination, the methodology aims to strike a balance that optimally preserves essential features and minimizes artifacts. Rigorous evaluations, both objective and subjective, are conducted to validate the effectiveness of the approach. This research contributes to the ongoing advancement of multimodal image fusion, addressing applications in fields like medical imaging, surveillance, and remote sensing, where the marriage of IR and VIS data is of paramount importance.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.355-372
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• 2021

Recently, high-performance HMDs (Head-Mounted Display) are becoming wireless due to the growth of virtual reality technology. Accordingly, environmental constraints on the hardware usage are reduced, enabling multiple users to experience virtual reality within a single space simultaneously. Existing multi-user virtual reality platforms use the user's location tracking and motion sensing technology based on vision sensors and active markers. However, there is a decrease in immersion due to the problem of overlapping markers or frequent matching errors due to the reflected light. Goal of this study is to develop a multi-user virtual reality moving platform in a single space that can resolve sensing errors and user immersion decrease. In order to achieve this goal hybrid sensing technology was developed, which is the convergence of vision sensor technology for position tracking, IMU (Inertial Measurement Unit) sensor motion capture technology and gesture recognition technology based on smart gloves. In addition, integrated safety operation system was developed which does not decrease the immersion but ensures the safety of the users and supports multimodal feedback. A 6 m×6 m×2.4 m test bed was configured to verify the effectiveness of the multi-user virtual reality moving platform for four users.

• Korean Journal of Remote Sensing
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• v.1 no.1
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• pp.89-97
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• 1985

An algorithm for the Hill-Sliding Clustering (HSC) method was developed using the BASIC language for Apple II personal computer. It was designed for initialization of clusters from multivariate multimodal Gaussian data. Landsat multispectral imagery data of a Korean coastal area were used for its performance test. The test showed encouraging results.

• Journal of Adhesion and Interface
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• v.21 no.4
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• pp.162-167
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• 2020

The skin-inspired multimodal soft sensors have been developed through multidisciplinary approaches to mimic the sensing ability with high sensitivity and mechanical durability of human skin. For practical application, although the stimulus discriminability against a complex stimulus composed of various mechanical and thermal stimuli experienced in daily life is essential, it still shows a low level actually. In this paper, we first introduce the operating mechanisms and representative studies of the unimodal soft sensor, and then discuss the recent research trend in the multimodal soft sensors and the stimulus discriminability.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.2
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• pp.173-181
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• 2001

This paper presents an artificial life algorithm which is remarkable in the area of engineering for functions optimization. As artificial life organisms have a sensing system, they can find the resource which they want to find and metabolize. And the characteristics of artificial life are emergence and dynamic interaction with environment. In other words, the micro-interaction with each other in the artificial lifes group results in emergent colonization in the whole system. In this paper, therefore, artificial life algorithm by using above characteristics is employed into functions optimization. The optimizing ability and convergent characteristics of this proposed algorithm is verified by using three test functions. The numerical results also show that the proposed algorithm is superior to genetic algorithms and immune algorithms for the multimodal functions.