Book Description
Virtual Reality describes a 3-D computer generated environment, controlled by the user from a remote location. VR has applications in robotics, entertainment and medical field. Virtual Reality robotic systems have been a major help in hazardous environments and in areas which need a high degree precision such as nuclear plants and tele-surgery. An ideal VR system immerses the user in the virtual environment. This condition is termed as telepresence. The components of a VR system are human operator, interface system and teleoperator. VR system relies on human interface performance for its high accuracy. Commercially available interfaces such as Data Gloves and exoskeleton devices provide less accuracy and restricted motion. A biocontrol interface utilizing human physiological signals such as Electromyogram (EMG) would be a natural and synergistic way of controlling a remote teleoperator. Previous studies (Suryanarayan and Reddy) have shown that surface EMG (SEMG) from flexor muscle can be effectively used as a human interface for controlling teleoperators for dynamic motion of elbow joints. The goal of the present study was to investigate the use of SEMG from extensor muscle to control real time dynamic movement of index finger at various speeds for full range. Normal subjects were asked to rhythmically flex and extend the index finger at different speeds. The actual angle was measured using a miniature accelerometer. SEMG from extensor muscle (Extensor Digitorum Superficialis (EDS)) was used to correlate with angle made by index finger at various speeds, with all other fingers at constant position. Parameters were extracted from SEMG. Neural networks were trained with input as extracted parameters and targets as measured angles. Best five networks were recruited for each committee. Two committees for each speed were formed. The committees were evaluated using data from new subject and the errors between actual and predicted joint angle was calculated. The committees were able to predict the joint angle at different speeds. The RMS errors between the predicted and the actual angle were found to be between 3-27%. The errors were more in the flexion region as compared to the extensor region. The study demonstrated the use of SEMG from EDS for the prediction of joint angle at different speeds. It also demonstrated the use of committee neural networks (CNN) in control related prediction problems. The study has taken a step forward in the direct biocontrol of telemanipulator and VR environments using SEMG. The study would find an application in medicine and control of robotic assist devices.
