Background: In virtual reality (VR) systems, the user’s finger and hand positions are sensed and used to control the virtual envi-ronments. Direct biocontrol of VR environments using surface electromyography (SEMG) signals may be more synergistic and uncons-training to the user. The purpose of the present investigation was to develop a technique to predict the finger joint angle from the surface EMG measurements of the extensor muscle using neural network models.
Methodology: SEMG together with the actual joint angle measurements were obtained while the subject was performing flexion-exten-sion rotation of the index finger at three speeds. Several neural networks were trained to predict the joint angle from the para-meters extracted from the SEMG signals. The best networks were selected to form six committees. The neural network committees were evaluated using data from new subjects.
Results: There was hysteresis in the measured SMEG signals during the flexion-extension cycle. However, neural network committees were able to predict the joint angle with reasonable accuracy. RMS errors ranged from 0.085 ± 0.036 for fast speed finger-extension to 0.147 ± 0.026 for slow speed finger extension, and from 0.098 ± 0.023 for the fast speed finger flexion to 0.163 ± 0.054 for slow speed finger flexion.
Conclusion: Although hysteresis was observed in the measured SEMG signals, the committees of neural networks were able to predict the finger joint angle from SEMG signals.

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