• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.106-111
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• 2020

In this paper, we propose the non-contact gesture recognition system and algorithm using proximity-based sensors. The system uses four IR receiving photodiode embedded on a single chip and an IR LED for small area. The goal of this paper is to use the proposed algorithm to solve the problem associated with bringing the four IR receivers close to each other and to implement a gesture sensor capable of recognizing eight directional gestures from a distance of 10cm and above. The proposed system was implemented on a FPGA board using Verilog HDL with Android host board. As a result of the implementation, a 2-D swipe gesture of fingers and palms of 3cm and 15cm width was recognized, and a recognition rate of more than 97% was achieved under various conditions. The proposed system is a low-power and non-contact HMI system that recognizes a simple but accurate motion. It can be used as an auxiliary interface to use simple functions such as calls, music, and games for portable devices using batteries.

• The Research Journal of the Costume Culture
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• v.25 no.4
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• pp.529-542
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• 2017

The purpose of this study is to utilize technology as basic data for smart clothing product research and development. This technology can recognize user's motion according to characteristics types and functions of wearable smart clothing products. In order to analyze the case of motion recognition products, we searched for previous research data and cases referred to as major keywords in leading search engines, Google and Naver. Among the searched cases, information on the characteristics and major functions of the 42 final products selected on the market are examined in detail. Motion recognition for smart clothing products is classified into four body types: head & face, body, arms & hands, and legs & feet. Smart clothing products was developed with various items, such as hats, glasses, bras, shirts, pants, bracelets, rings, socks, shoes, etc., It was divided into four functions health care type for prevention of injuries, health monitor, posture correction, sports type for heartbeat and exercise monitor, exercise coaching, posture correction, convenience for smart controller and security and entertainment type for pleasure. The function of the motion recognition smart clothing product discussed in this study will be a useful reference when designing a motion recognition smart clothing product that is blended with IT technology.

• Smart Media Journal
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• v.5 no.3
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• pp.25-29
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• 2016

This paper presents research for non-contact gesture recognition system using EPS(Electronic Potential Sensor) for measuring the human body of electromagnetic fields. It implemented a signal acquisition and signal processing system for designing a system suitable for motion recognition using the data coming from the sensors. we transform AC-type data into DC-type data by applying a 10Hz LPF considering H/W sampling rate. in addition, we extract 2-dimensional movement information by taking difference value between two cross-diagonal deployed sensor.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.3
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• pp.1-11
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• 2017

There is a Growing Interest in Motion Recognition for Recognizing Human Motion in Camera Images. As a Result, Researches are Being Actively Conducted to Control Digital Devices with Gestures at a Long Distance. The Interface Using Gesture can be Effectively Used in the Digital Signage Industry Where the Advertisement Effect is Expected to be Exposed to the Public in Various Places. Since the Digital Signage Contents can be Easily Controlled through the Non-contact Hand Operation, it is Possible to Provide the Advertisement Information of Interest to a Large Number of People, Thereby Providing an Opportunity to Lead to Sales. Therefore, we Propose a Digital Signage Content Control System Based on Hand Movement at a Certain Distance, which can be Effectively Used for the Development of Interactive Advertizing Media.

• Journal of Sensor Science and Technology
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• v.24 no.3
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• pp.175-180
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• 2015

Worldwide, studies on intelligent vehicles for the convenience of drivers have been actively conducted as the number of cars has increased. However, vehicle convenience enabled by buttons lowers the concentration on driving and hence poses as a huge threat to the safety of the driver. The use of one of the convenient features, impaired driving auxiliary equipment, is limited because of its complex usage, and this device also hinders the front view of the driver. This paper proposes a vehicle-control device for controlling the convenient features as well as changes in speed and direction using gestures and motions of the driver. This device consists of an ultrasonic sensor for recognizing movement, an arduino for accepting signal control functions and servo and DC motors apply to various vehicle parts. Firstly, the vehicle-control device was designed using a 3D CAD program known as Solid-works based on the size of the steering wheel. Then, through simulations, a suitable length for minimizing the absorbent between ultrasonic sensors was confirmed using a program known as COMSOL Multiphysics. Finally, simulation results were verified through experiments, and the optimal size of the device was identified through the number of errors.

• Journal of Advanced Navigation Technology
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• v.25 no.5
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• pp.426-432
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• 2021

Continuous wave (CW) Doppler radar has the advantage of being able to solve the privacy problem unlike camera and obtains signals in a non-contact manner. Therefore, this paper proposes a human activity recognition (HAR) system using CW Doppler radar, and presents the hardware design and implementation results for acceleration. CW Doppler radar measures signals for continuous operation of human. In order to obtain a single motion spectrogram from continuous signals, an algorithm for counting the number of movements is proposed. In addition, in order to minimize the computational complexity and memory usage, binarized neural network (BNN) was used to classify human motions, and the accuracy of 94% was shown. To accelerate the complex operations of BNN, the FPGA-based BNN accelerator was designed and implemented. The proposed HAR system was implemented using 7,673 logics, 12,105 registers, 10,211 combinational ALUTs, and 18.7 Kb of block memory. As a result of performance evaluation, the operation speed was improved by 99.97% compared to the software implementation.