Control of Power Wheelchair using EMG Signals and
Guided-Underdetermined Source Signal Separation

                                                                             by Luis Rivera

 Introduction

    Surface Electromyographic signals (sEMG) find applications in many areas such as rehabilitation, prosthesis and human-machine interaction. Systems reliant on these muscle-generated electrical signals require various forms of machine learning algorithms for recognition of specific signatures. Those systems vary in terms of the signal detection methods, the feature selection and the classification algorithm used, however, in all those cases, the use of multiple sensors and complex analysis and classification algorithms are constant requirements. In this research, we developed a power wheelchair control system that relies on a single sEMG sensor and a new technique for signature recognition that we call Guided Under-determined Source Signal Separation (GUSSS). Compared to other approaches in the literature, the proposed technique relies on a much simpler classifier and uses a very small number of features to achieve reasonable results.

Hardware Description: The main components include the equipment for acquiring EMG signals, the control module mounted on the back of the wheelchair to process the signals and a display to provide visual feedback on the selected commands. The control module is connected to the existing joystick interface to move the wheelchair. In the video, you will see 3 boxes in the back of the wheelchair. These have been replaced by a single box with a commercial embedded device and two custom-made add-on cards -- including our own EMG sensor technology. The cost of all the required hardware is under $100. Hardware Video

Training: Unlike other technologies also reliant on EMG signals, we have developed a novel software system which requires only one electrode in order to recognize the same types of muscular patterns (e.g. hand gestures, eyebrow movements, etc) to control the movement of the wheelchair. During the training, the user selects the muscle movement to be associated with an intended wheelchair command. The use of only one signal allows our system to be deployed alternatively and/or simultaneously in multiple parts of the body – dependent on the user’s abilities. For this video, we have positioned the electrode on the forearm (shown in the driving video), thus selecting wrist/hand motions to control the wheelchair. Repeating the gesture during the training session allows the unique software system to learn the gesture and associate it with a motion command to the wheelchair. The video was edited for length. But from the sequence presented, one should be able to infer that the total time for training is under 5min. Training Video

Driving: The goal here was to present how gestures are used to drive the chair, as opposed to a more typical driving experience -- for ex, the current maximum speeds (turn and forward) were reduced for testing and demonstration purposes). The electrode is placed over the forearm and the hand/wrist motions used to control the wheelchair are the same as identified in the training video. The intended wheelchair command associated with that muscle movement is communicated through any joystick analog interface. Driving Video

User Feedback: This video was produced to demonstrate the visual feedback provided by the system to the user. Through this feedback, the user can visually confirm what motion command the system is sending to the wheelchair as a function of the detected gesture by the user. Feedback Video

    References

1. Rivera, L. A. and DeSouza, G. N. “A Power Wheelchair Controlled Using Hand Gestures, a Single sEMG Sensor, and Guided Under-determined Source Signal Separation”. IEEE 4th RAS and EMBS International Conference on Biomedical Robotics BioRob2012, pp.1535-1540, June, 2012. Rome, Italy.
   See this paper on IEEE.org 

2. Rivera, L. A. and DeSouza, G. N., “Recognizing Hand Movements from a Single sEMG Sensor using Guided Under-determined Source Signal Separation”, IEEE 12th International Conference on Rehabilitation Robotics, pp.1-6, June 29 - July 1, 2011, ETH Zurich, Switzerland. See this paper on IEEE.org