• Title/Summary/Keyword: arm movement detection

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A Human Arm Movement Detection System Using Electrical Bioimpedance Measurement (생체 임픽던스 측정에 의한 상지 운동 감지 시스템)

  • Kim, Jong-Chan;Kim, Su-Chan;Nam, Gi-Chang;Park, Min-Yong;Kim, Gyeong-Hwan;Kim, Deok-Won
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.51 no.8
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    • pp.374-379
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    • 2002
  • In this study, we developed a new human arm movement detection system using electrical bio-impedance method with several skin-electrodes. The correlation coefficients of the joint angle and the impedance change from human arm movement was obtained using a goniometer and impedance measurement system developed in this study. The correlation coefficients of the wrist and the elbow movements were 0.94 and -0.99, respectively. This system was applied to control a robotic arm by converting the measured impedance to joint angle to confirm the validity of the proposed system. In conclusion, we confirmed that this system can control the robotic arm according to arm movement without any limitation of movement. This system showed possibility that upper arm movement could be easily measured by impedance measurement system with a few skin-electrodes.

A Study on Robot Arm Control System using Detection of Foot Movement (발 움직임 검출을 통한 로봇 팔 제어에 관한 연구)

  • Ji, H.;Lee, D.H.
    • Journal of rehabilitation welfare engineering & assistive technology
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    • v.9 no.1
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    • pp.67-72
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    • 2015
  • The system for controlling the robotic arm through the foot motion detection was implemented for the disabled who not free to use of the arm. In order to get an image on foot movement, two cameras were setup in front of both foot. After defining multiple regions of interest by using LabView-based Vision Assistant from acquired images, we could detect foot movement based on left/right and up/down edge detection within the left/right image area. After transferring control data which was obtained according to left/right and up/down edge detection numbers from two foot images of left/right sides through serial communication, control system was implemented to control 6-joint robotic arm into up/down and left/right direction by foot. As a result of experiment, we was able to get within 0.5 second reaction time and operational recognition rate of more 88%.

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Movement Intention Detection of Human Body Based on Electromyographic Signal Analysis Using Fuzzy C-Means Clustering Algorithm (인체의 동작의도 판별을 위한 퍼지 C-평균 클러스터링 기반의 근전도 신호처리 알고리즘)

  • Park, Kiwon;Hwang, Gun-Young
    • Journal of Korea Multimedia Society
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    • v.19 no.1
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    • pp.68-79
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    • 2016
  • Electromyographic (EMG) signals have been widely used as motion commands of prosthetic arms. Although EMG signals contain meaningful information including the movement intentions of human body, it is difficult to predict the subject's motion by analyzing EMG signals in real-time due to the difficulties in extracting motion information from the signals including a lot of noises inherently. In this paper, four Ag/AgCl electrodes are placed on the surface of the subject's major muscles which are in charge of four upper arm movements (wrist flexion, wrist extension, ulnar deviation, finger flexion) to measure EMG signals corresponding to the movements. The measured signals are sampled using DAQ module and clustered sequentially. The Fuzzy C-Means (FCMs) method calculates the center values of the clustered data group. The fuzzy system designed to detect the upper arm movement intention utilizing the center values as input signals shows about 90% success in classifying the movement intentions.

Evaluation and Verification of Optimal Electrode Configurations for Detection of Arm Movement Using Bio-Impedance (생체임피던스에 의한 상지운동 감지를 위한 최적 전극 위치의 평가 및 검증)

  • Ahn, Seon-Hui;Kim, Soo-Chan;Nam, Ki-Chang;Kim, Deok-Won
    • Proceedings of the KIEE Conference
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    • 2002.11c
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    • pp.399-402
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    • 2002
  • In this study, we constructed a four-channel impedance measurement system including a two-channel goniometer to analyze human arm movement. Impedances and joint angles were simultaneously measured for wrist and elbow movements. As the impedance changes resulting from wrist and elbow movements depended heavily on electrode placement, we determined the optimal electrode configurations for those movements by searching for high correlation coefficients, large impedance changes, and minimum interferences in ten subjects (age: 29+6). Our optimal electrode configurations showed very strong relationships between the wrist joint angle and forearm impedance (correlation coefficient = 0.95+0.04), and between the elbow joint angle and upper arm impedance (correlation coefficient = -0.98+0.02). Although the measured impedances changes of the wrist (1.1+1.5 ohm) and elbow (-5.0+2.9 ohm) varied among individuals, the reproducibilities of wrist and elbow impedance changes of five subjects were 5.8+1.8 % and 4.6+1.4 % for the optimal electrode pairs, respectively. We propose that this optimal electrode configuration would be useful for future studies involving the measurement of accurate arm movements by impedance method.

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Upper Limb Motion Detection Including Fingers Using Flex Sensors and Inertial Sensors (휘어짐센서와 관성센서를 이용한 손가락을 포함한 상지 운동 검출)

  • Kim, Yeon-Jun;Yoo, Jae-Ha;Kim, Dong-Yon;Kim, Soo-Chan
    • Journal of the Institute of Convergence Signal Processing
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    • v.21 no.3
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    • pp.101-106
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    • 2020
  • The utilization of virtual reality is increasing not only in games but also in medical care such as rehabilitation. Due to the convenience, the motion of the upper limb is detected using a non-contact method using video or a handheld type mouse, etc. In this paper, we implemented a glove which can measure finger movements and upper limb movements by using flex sensors whose resistance value changes according to the degree of folding and inertial sensors which can obtain direction information in space. We showed the upper arm movements including finger movements with signals obtained from the implemented glove on the open software platform, Processing. The sensitivity of each finger movement was 0.5deg, and the sensitivity of the upper limb motion was 0.6deg.

The design of 6-axis robot arm with intelligent object detection and object movement function (지능적 객체검출과 물체이동 기능을 갖는 6축 로봇 팔의 설계)

  • Kim, Kyu-Tae;Koo, Mo-Se;Ko, Young-Jun;Park, Myeong-Suk;Kim, Sang-Hoon
    • Proceedings of the Korea Information Processing Society Conference
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    • 2021.11a
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    • pp.417-420
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    • 2021
  • 본 논문은 서비스 로봇 분야에서 활용 가능한, ROS기반의 객체검출과 이동 기능을 갖는 6축 로봇 팔의 설계 방법 및 성능 개선결과를 제시한다. 기구설계, 물체검출, 3D좌표생성을 통한 실시간 역 기구학 해석 방법 및 지능적 모터 및 센서 제어 방법 등에 대해 제시하였다. 특히 영상과 센서기반 처리를 통해 고정된 작업반경 내 물체를 지능적으로 검출하고 목표지점까지 이동시키며, ROS기반의 추출된 정보를 이용하여 동작의 오차를 최소화하기 위해 다관절 로봇 팔의 운동을 최적화하여 설계하였으며 다양한 관련 실험을 통해 주요성능을 검증하였다.

A Study on u-CCTV Fire Prevention System Development of System and Fire Judgement (u-CCTV 화재 감시 시스템 개발을 위한 시스템 및 화재 판별 기술 연구)

  • Kim, Young-Hyuk;Lim, Il-Kwon;Li, Qigui;Park, So-A;Kim, Myung-Jin;Lee, Jae-Kwang
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2010.05a
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    • pp.463-466
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    • 2010
  • In this paper, CCTV based fire surveillance system should aim to development. Advantages and Disadvantages analyzed of Existing sensor-based fire surveillance system and video-based fire surveillance system. To national support U-City, U-Home, U-Campus, etc, spread the ubiquitous environment appropriate to fire surveillance system model and a fire judgement technology. For this study, Microsoft LifeCam VX-1000 using through the capturing images and analyzed for apple and tomato, Finally we used H.264. The client uses the Linux OS with ARM9 S3C2440 board was manufactured, the client's role is passed to the server to processed capturing image. Client and the server is basically a 1:1 video communications. So to multiple receive to video multicast support will be a specification. Is fire surveillance system designed for multiple video communication. Video data from the RGB format to YUV format and transfer and fire detection for Y value. Y value is know movement data. The red color of the fire is determined to detect and calculate the value of Y at the fire continues to detect the movement of flame.

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