Computing and Electronic Engineering
Ahmed Kattan portrait image


GP- Muscle Fatigue Predictor

GP- Detecting Localised Muscle Fatigue

In this research we investigate the idea of predicting localised muscle fatigue by identifying a transition state which resides between the non-fatigue and the fatigue stages within the EMG signal. A collection of statistical measures have been used to generate new composite, higher-level features and correlate muscle activity with particular patterns. Genetic Programming (GP) has been used to automate this process.

The proposed approach was able to give an early warning before the onset of fatigue in most of the experimental cases. Therefore, this approach shows potential applications such as ergonomics, sports physiology, and physiotherapy.


This research was contributed by:

· Mohammed Al-Mulla

· Ahmed Kattan



Here you can download the test files that have been testing the performance of the system and compare them with other methods.

· EMG signals from 3 different individuals



· Classification of localised muscle fatigue with Genetic Programming on sEMG during isometric contraction.

· Detecting Localised Muscle Fatigue during Isometric Contraction using Genetic Programming