Erik Mischiatti

Index

EMG-based muscle activity detection

Studying and processing electromyographic (EMG) signals to correlate muscle activation with applied force during upper-limb exercises.

My contribution: author of the bachelor's thesis.

1 Overview

The project follows the EMG signal from the muscle to the microcontroller: amplification, filtering, digitization and visualization. The aim is to understand how muscle contraction intensity can be monitored with MyoWare sensors, an Arduino Due and real-time plotting in Processing.

Table 1 Key features, as documented
FeatureDescription
MyoWare EMG sensor All-in-one analog signal processing (amplification, rectification, integration) with a direct signal output
Arduino Due (12-bit ADC) Real-time analog-to-digital conversion for high-resolution acquisition
Signal analysis Feature extraction: RMS, ARV, zero-crossings, slope changes and variance
Muscle groups studied Biceps brachii, triceps brachii and flexor carpi ulnaris

2 System architecture

  1. Surface electrodes
  2. MyoWare sensor
  3. Arduino Due
  4. Serial link to PC
  5. Processing visualization
Fig. 1 Signal chain: the MyoWare sensor feeds preprocessed analog signals to the Arduino Due, which sends the data over serial to a PC running Processing for real-time visualization and analysis.
EMG system setup: wiring of the Arduino Due, the MyoWare muscle sensor and its cable shield, and three surface electrodes connected through the sensor cable.
Fig. 2 EMG system setup: Arduino Due, MyoWare muscle sensor with cable shield, and surface electrodes (labels in Italian).

3 Results

The collected signals showed clear activation patterns during weightlifting tasks. Feature extraction confirmed an increase in EMG envelope amplitude with applied force, although a linear correlation was not strictly observable. Baseline noise stayed within 1–3.5 mV, and signal integrity was checked through frequency and offset inspections.

4 Technologies

Table 2 Technologies used
AreaTechnology
Signal acquisition MyoWare EMG sensors, surface electrodes, analog pre-processing
Microcontroller Arduino Due with 12-bit ADC for real-time digitization and sampling
Visualization Custom Processing interface for live signal plotting and monitoring
Signal processing Low-pass filters, differential amplification, baseline noise analysis

5 Resources

  1. No public resources linked yet