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This is

MATCH

the sEMG+IMU sports sensor

 

Specifications >

Wearable sports sensor, MATCH

3D Avatar with sEMG sensors

K arm grasping motion with sEMG sensors

Rock-paper-scissors with Robot Hand

MATCH

captures
Motion and Muscle activity
in realtime.

MATCH

captures
Motion and Muscle activity
in realtime.

Applications

If you are interested in collaboration with us

Sports

Medical

Biointerface

If you are interested in collaboration with us

Specification

Sensor Size
Sensor weight

23mm(W) x 52mm(H) x 18.5mm(D)
20g

General Specifications

RF Frequnecy Band 2400MHz~2480MHz
Full-charge Operation Time 90min
Near Real-time 8ms Sampling Latency
Vibration Motor

Motion Sensor Specifications

Sampling Rate 125Hz
Number of Axis 9 DOF
Gyro Full Scale Range / Resolution 2000 dps / 16bit
Accel Full Scale Range / Resolution +8 g/ 16bit
Compass Sensitivity / Resolution +48004T / 12bit

Motion Sensor Specifications

Sampling Rate 1kHz
Resolution 0~3.3V 12bit
Range 15~1kHz

Software

MATCH Set 구매시 제공되는 소프트웨어 입니다

MATCH Human

MATCH Viewer & Human

• MATCH Viewer & Human are DAQ software for motion capturing and sEMG data acquisition from 8-MATCH Sensor.
• Data acquisition sampling time is 1 millisecond via USB 2.0 (High Speed).
• MATCH Viewer provides real-time graphs of 8-MATCH Sensors without data loss.
• MATCH Human provides real-time visualization of the user’s motion and Muscle activity.
• Support for Win 7 SP1 /Win8 /Win8.1 /Win10.

MATCH Viewer

MATCH SDK

• Includes MATCH API for Windows and sample codes.
• MATCH API can be linked with C/C++, C#, Unity3D.
• Data acquisition sampling time is 1 millisecond via USB 2.0 (High Speed).
• Support for Win7 SP1/Win8/Win8.1 /Win10.

On sale

Contact to khk@postech.ac.kr

– 8 MATCH sensors
– MATCH cradle
– Band kit
– MATCH software
– Adapter
– USB data cable
– Hardcase

 

$10,000

(Excluding VAT)

Must Read

 1. Seongsik Park, Wan Kyun Chung, and Keehoon Kim, “Training-Free Bayesian Self-Adaptive Classification for sEMG Pattern Recognition Including Motion Transition,” IEEE transactions on Biomedical Engineering, Oct. 2020 10.1109/TBME.2019.2947089. 

 2. Minjae Kim, Wan Kyun Chung, Keehoon Kim, “Subject-Independent sEMG Pattern Recognition by Using a Muscle Source Activation Model,” IEEE Robotics and Automation Letters, vol.5, no.4, pp. 5175-5180, 2020.

 3. Jaemin Lee, Min Kyu Kim, and Keehoon Kim, “A Control Scheme to Minimize Muscle Energy for Power Assistant Robotic Systems under Unknown External Perturbation,” IEEE transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 12, December, 2017. (SCIE, IF 3.41, JCR 6.1%, 2016)

 4. Seongsik Park, Woongyong Lee, Wan Kyun Chung, and Keehoon Kim, “Programming by Demonstration using the Tele-Impedance Control Scheme: Verification by an sEMG-Controlled Ball-Trapping Robot,” IEEE Transactions on Industrial Informatics, vol. 15, issue 2, pp. 998-1006, Feb. 2019. (SCI, IF 5.430, JCR 1.064%)

 5. Seongsik Park, Wan Kyun Chung, and Keehoon Kim, “Hierarchical Segmentation of Continuous Motions through sEMG Signal Analysis,” IEEE Robotics and Automation Letters, vol. 4, issue 4, pp. 4402-4409, Oct., 2019.

 6. Minjae Kim, Keehoon Kim, Wan Kyun Chung, “Simple and Fast Compensation of sEMG Interface Rotation for Robust Hand Motion Recognition,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 12, pp. 2397-2406, Dec. 2018. (SCIE, IF 3.972, JCR 3.846%)

 7. Minjae Kim, Yaejin Moon, Jasmine Hunt, Kelly A. McKenzie, Adam Horin, Matt McGuire, Keehoon Kim, Levi J. Hargrove, Arun Jayaraman, “A Novel Technique to Reject Artifact Components for Surface EMG Signals Recorded During Walking with Transcutaneous Spinal Cord Stimulation: A Pilot Study,” Frontiers in Human Neuroscience, 2021.

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