Single-Channel sEMG Hand Gesture Classification Using an Artificial Neural Network Implemented on an ESP32 Microcontroller

The implementation of embedded systems for myoelectric signal classification is key to the development of prosthetic and other real-time assistive technologies. This work presents a hand gesture classification system based on an artificial neural network (ANN) deployed on an ESP32 microcontroller. In contrast to approaches that require multiple acquisition channels or complex configurations, the proposed system uses a single surface electromyography (sEMG) channel, reducing cost and complexity. Data were collected from eight participants performing eight hand and wrist gestures. The system achieved classification accuracies of up to 93.05% for six gestures, including rest. Furthermore, the ESP32 implementation demonstrated inference times compatible with real-time operation, achieving an average inference time of 0.17 ms. When using a majority voting strategy, classification accuracy increased to 99%, confirming the feasibility of implementing efficient and accurate gesture recognition on low-cost embedded hardware. These results support its integration into portable and autonomous systems for prosthetic control and other interactive applications.