Embedded Learning for Smart Functional Electrical Stimulation

This paper presents a smart embedded Functional Electrical Stimulator (FES), able to stimulate a muscle only when a specific movement pattern occurs. This pattern is detected using an inertial measurement unit (IMU) coupled with a feature detector and a neural classifier. Architecture of the FES is first presented, then embedded processing algorithms composed of feature extraction and neural network classification are detailed. Results show that the muscle vibration happening when stimulation is needed can be recognized in more than 90% of cases using less than 3% of average embedded processor resources on a ARM M4F.