Grove - EMG Detector
EMG detector is a bridge connects human body and electrical, the sensor gathers small muscle signal then process with 2th amplify and filter, the output signal can be recognized by Arduino.
- 10+: $20.00
PRODUCT DETAILS
EMG detector is a bridge connects human body and electrical, the sensor gathers small muscle signal then process with 2th amplify and filter, the output signal can be recognized by Arduino.
The partlist of this product has been updated recently. The connecting cable you receive may have two specifications. Their functions are exactly the same, only the colors are different (corresponding relationship is as shown in the picture)
Features
- 6 Disposable Surface Electrodes: Help users to measure more accurately
- Grove unified socket: The product ensures "Plug and Play" and could be versatile for various applications.
- Power supply voltage: 3.3V-5V
- 1000mm Cable Leads
- No additional power supply
- 3.5mm Connector
Description
EMG detector is a bridge connects human body and electrical, the sensor gathers small muscle signal then process with 2th amplify and filter, the output signal can be recognized by Arduino. You can add this signal into your control system. In standby mode, the output voltage is 1.5V. When detect muscle active, the output signal rise up, the maximum voltage is 3.3V. You can use this sensor in 3.3V or 5V system.
Attention
The sensor cannot be used for medical purposes.
What is EMG?
Electromyography (EMG) is a science that originated in Europe and the United States, and began to be used in clinical practice abroad in the 1950s and in China around the 1980s. It is now recognized as an extension of clinical examination of neuromuscular diseases, and can provide important information for the determination of the localization, characteristics and degree of lesions of neuromuscular diseases in clinical diagnosis. Histochemistry, molecular biology, genetic testing and imaging cannot replace the examination techniques.
We have developed more than 400 Grove modules, covering a wide range of applications that can fulfill various needs. Click to explore the Grove Sensor family and get started to Grove your Idea. For all Grove users (especially beginners), we provide you with guidance PDF documents. Please download and read through Preface - Getting Started and Introduction to Grove before your use of the product.
Application
- Detection of neuromuscular function
Hardware Overview
J2: grove interface, connect to analog I/O.
J1: EMG Disposable Surface Electrodes connector.
U1: INA331IDGKT, difference amplifier.
U2, U3: OPA333, Zero drift amplifier.
Technical details
| Dimensions | 140mm x100mm x30mm |
| Weight | G.W 45g |
| Battery | Exclude |
Part List
| Grove - EMG Detector | 1 |
| Grove Cable | 1 |
| One-off electrode | 6 |
| DC jacket to button connector cable 1000mm | 1 |
ECCN/HTS
| HSCODE | 9031809090 |
| USHSCODE | 9026104000 |
| UPC | 841454108443 |
| EUHSCODE | 9013101000 |
| COO | CHINA |
| EU DoC | 1 |
| RoHS | 1 |
| UK DoC | 1 |
LEARN AND DOCUMENTS
Documentations
SHARED BY USERS
REVIEWS
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from order viewThis user did not leave any comments.
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from order viewThe detectors are excellent. I ordered 10 items and all of them provide the signal that is good enough to detect any activity (I've tested them for upper-limb movements). Also, it is very suitable to use them with Grove Base shield for Arduino.
But I encountered some issues with the doc - initially, we got stuck on determining which of the wired electrodes is a reference electrode. The documentation does not give any information, except for one photo with electrode placement. But as I found out with the help of a multimeter, the black wire is a reference (or GND), the red one is left channel and the white is right. With such a configuration I was able to acquire a good signal. But in the image in the doc there is a red wire that seems to be a reference, and I suppose this is wrong, also we weren't able to acquire a good signal using such placement. -
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We confirm the Grove-EMG Detector can be used along with a GrovePi+ and a Raspberry Pi2.