• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.69-80
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• 2019

This paper proposes a multimodal sensor platform which supports plug and play (PnP) technology. PnP technology automatically recognizes a connected sensor module and an application program easily controls a sensor. To verify a multimodal platform for PnP technology, we build up a firmware and have the experiment on a sensor system. When a sensor module is connected to the platform, a firmware recognizes the sensor module and reads sensor data. As a result, it provides PnP technology to simply plug sensors without any software configuration. Measured sensor raw data suffer from various distortions such as gain, offset, and non-linearity errors. Therefore, we introduce a polynomial calculation to compensate for sensor distortions. To find the optimal coefficients for sensor calibration, we apply a genetic algorithm which reduces the calibration time. It achieves reasonable performance using only a few data points with reducing 97% error in the worst case. The platform supports various protocols for multimodal sensors, i.e., UART, I2C, I2S, SPI, and GPIO.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.181-190
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• 2009

This paper presents a multimodal emergency recognition system based on visual information, audio information and gravity sensor information. It consists of video processing module, audio processing module, gravity sensor processing module and multimodal integration module. The video processing module and gravity sensor processing module respectively detects actions such as moving, stopping and fainting and transfer them to the multimodal integration module. The multimodal integration module detects emergency by fusing the transferred information and verifies it by asking a question and recognizing the answer via audio channel. The experiment results show that the recognition rate of video processing module only is 91.5% and that of gravity sensor processing module only is 94%, but when both information are combined the recognition result becomes 100%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.407-408
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• 2023

In this paper, we intend to develop a multimodal algorithm that secures recognition performance of over 95% in daytime illumination environments and secures recognition performance of over 90% in bad weather (rainfall and snow) and night illumination environments.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.6
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• pp.603-610
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• 2015

This paper presents a multimodal sensor system that can determine the location of house space by analyzing the postures and heights of the residents. It consists of two sensors: a tilt sensor and an altimeter sensor. The tilt sensor measures the static and dynamic postures of the residents, and the altimeter sensor measures their heights. The sensor system includes a Bluetooth transmitter, and the server receives the measured data and determines the location in the house. We describe the process determining the locations of the residents after analyzing their postures and behaviors from the measured data. We also demonstrate the usefulness of the proposed system by applying it to a real environment.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.21-26
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• 2009

A sensor system capable of automatically recognize activities would allow many potential Ubiquitous applications. This paper presents a new system for recognizing the activities of daily living(ADL) like walking, running, standing, sitting, lying etc. The system based on the state-dependent motion analysis using Tri-Accelerometer and Zigbee tag. Two accelerometers are used for the classification of body and hand activities. Classification of the environment and instrumental activities is performed based on the hand interaction with an object ID using.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.757-758
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• 2008

This paper aims to propose an emergency recognition system using multimodal information extracted by an image processing module, a voice processing module, and a gravity sensor processing module. Each processing module detects predefined events such as moving, stopping, fainting, and transfer them to the multimodal integration module. Multimodal integration module recognizes emergency situation by using the transferred events and rechecks it by asking the user some question and recognizing the answer. The experiment was conducted for a faint motion in the living room and bathroom. The results of the experiment show that the proposed system is robust than previous methods and effectively recognizes emergency situations at various situations.

• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.71-78
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• 2022

Wearable devices have the potential to revolutionize future medical diagnostics and personal healthcare. The integration of biosensors into scalable form factors allow continuous and noninvasive monitoring of key biomarkers and various physiological indicators. However, conventional wearable devices have critical limitations owing to their rigid and obtrusive interfaces. Recent developments in functional biocompatible materials, micro/nanofabrication methods, multimodal sensor mechanisms, and device integration technologies have provided the foundation for novel skin-interfaced bioelectronics for advanced and user-friendly wearable devices. Nonetheless, it is a great challenge to satisfy a wide range of design parameters in fabricating an authentic skin-interfaced device while maintaining its edge over conventional devices. This review highlights recent advances in skin-compatible materials, biosensor performance, and energy-harvesting methods that shed light on the future of wearable devices for digital health and personalized medicine.

• 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.

• 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 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.