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EMG Muscle Sensor V3.0 With Cable And Electrodes

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SKU : EMSEN8001

  1. Analog EMG (Electromyography) Muscle Signal Sensor
  2. Includes connector cable and adhesive electrodes
  3. Reads muscle electrical activity for biofeedback & control
₹ 1,180.00 ₹ 1,180.00

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Product Description

The EMG Muscle Sensor V3.0 is a ready-to-use electromyography sensor module designed to detect electrical activity produced by skeletal muscles. This sensor measures the voltage generated when muscles contract, making it ideal for biofeedback systems, prosthesis control, gesture systems, rehabilitation interfaces, robotics, and interactive wearables. The module includes a connection cable and adhesive electrodes for attaching to skin safely.

This sensor outputs analog signals representing muscle activity, which can be read by analog-capable microcontrollers like Arduino, ESP32, STM32, and others. With onboard amplification and filtering circuitry, it produces stable signals suitable for real-time muscle detection and control applications.

Key Features

  • Detects muscle electrical activity (EMG)


  • Provides analog output for microcontrollers


  • Includes 5-wire cable and electrodes for attachment


  • Onboard signal amplification & filtering


  • Adjustable gain potentiometer


  • Easy-to-use with Arduino/ESP32/MCU boards


  • Useful for biofeedback, prosthetics research, gesture control


Package Includes

  • 1 × EMG Muscle Sensor V3.0 board


  • 1 × Connection cable (5 wires)


  • 2 × Adhesive electrodes


Technical Specifications

Parameter

Specification

Sensor Type

Electromyography (EMG)

Output

Analog voltage proportional to muscle activity

Power Supply

~3.3 V – 5 V DC

Interface

Analog signal to microcontroller ADC

Electrodes

Adhesive disposable pads included

Gain Control

Adjustable (onboard)

Compatible With

Arduino / ESP32 / STM32 / PIC

Board Dimensions

~40 × 30 mm (approx)

Applications

  • Muscle-controlled robotics


  • Prosthetic limb signal interfaces


  • Wearable biofeedback systems


  • Rehabilitation & health monitoring


  • Gesture recognition and interactive control


  • Biomedical signal acquisition projects


  • Brain-computer / body-computer experiments