Rupom, Farhan FuadJannat, ShafaitulTamanna, Farjana FerdousiAl Johan, Gazi MusaIslam, Md. Motaharul2026-09-032026-09-032020-06-05F. F. Rupom, S. Jannat, F. F. Tamanna, G. M. Al Johan and M. M. Islam, "EMG Controlled Bionic Robotic Arm using Artificial Intelligence and Machine Learning," 2020 IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh, 2020, pp. 334-339, doi: 10.1109/TENSYMP50017.2020.9230885.97817281736652-s2.0-85096420786https://hdl.handle.net/10361/29739The fundamental and main goal of gesture recognition research applied to Human-Computer Interaction (HCI) is making systems to identify and classify some specific human gestures and use them to transfer information and control devices. Surface Electromyography (sEMG) based gesture interfaces need quick and accurate detection, and gesture recognition in real time. We have mainly worked with four hand gestures which are Rock, Paper, Spherical grip, All right. This report proposes a solution to do real-time gesture recognition with the use of various machine learning algorithms and allowing its applications in a vast range of human-computer interfaces. We have used sEMG recordings recorded from muscles of hand which will constantly transmit those data to microcontroller. We will collect data from the microcontroller and then store those data in offline server.en-USfalseGesture recognitionMachine learningMicro-controllerSurface EMGConference Proceeding