FlexEMG [1] is a system for EMG-based gesture classification combining a flexible high density electrode array, a dedicated biopotential acquisition device, and a brain-inspired classification algorithm. Large area coverage and dense electrode spacing ensures sufficient muscular coverage without requiring precise placement. Furthermore, the wireless and compact signal acquisition device promotes comfort and ease of use. Finally, the HD algorithm achieves high classification accuracy without substantial degradation over multiple sessions, and can be trained using minimal amounts of data.

(a) Flexible, printed electrode array. (b) Device worn on the forearm with a commercial patch electrode connected to the elbow as a recording reference. (c) Custom wireless neural signal acquisition device.



We have shown the classification of 21 finger and hand gestures with ~90% accuracy in various experiments and contexts [2].

(left) Graphical user interface (GUI) for running the experiments. (right) 21 finger and hand gestures used in the study.



As a key application driver, we have 3-D printed a prototype prosthetic arm with 3 degrees of freedom, and controlled it based on user’s hand gestures online [3]. We have also demonstrated other applications using our gesture recognition device such as controlling robotic arms.

Few applications using our EMG-based gesture recognition device: Controlling an in-house 3-D printed prosthetic arm and a commercial robotic arm.



This project was in collaboration with Prof. Alvaro Araujo group (Universidad Politecnica de Madrid) for the prosthetic arm, Prof. Luca Benini group (ETH Zurich, University of Bologna) on the algorithm, and Prof. Ana Arias group (UC Berkeley) for the flexible electrode array.

  1. An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier A. Moin, A. Zhou, A. Rahimi, S. Benatti, A. Menon, S. Tamakloe, J. Ting, N. Yamamoto, Y. Khan, F. Burghardt, L. Benini, A. C. Arias, and J. M. Rabaey In IEEE International Symposium on Circuits and Systems (ISCAS) 2018 [arXiv]
  2. A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition Ali Moin, Andy Zhou, Abbas Rahimi, Alisha Menon, Simone Benatti, George Alexandrov, Senam Tamakloe, Jonathan Ting, Natasha Yamamoto, Yasser Khan, Fred Burghardt, Luca Benini, Ana C. Arias, and Jan M. Rabaey Nature Electronics 2020 [Link]
  3. A Robust EMG-Based Hand Gesture Classifier Controlling a 3D-Printed Bionic Arm Actuator A. Moin, A. Zhou, A. Rahimi, S. Benatti, A. Menon, S. Tamakloe, J. Ting, N. Yamamoto, Y. Khan, F. Burghardt, L. Benini, A. C. Arias, A. Araujo, and J. M. Rabaey In Society for Neuroscience (SfN) annual meeting 2018 [PDF]