Design of Virtual Interaction Experience Based on Multi-signal Extraction of Lower Lumbar Muscle

The Virtual interaction experience of sports such as surfing and skiing has considerable commercial prospect. Electromyography measurement can reflect the real time exertion. Two kinds of signal sources including motion and force on feet were measured together with the lumbar muscle exertion. The Identifiable muscles were selected by comparison between the measured exertion curve and two types of predictions. Both predictions were based on inverse dynamics algorithm provided by human finite element analysis. The recognizable time range of electromyography signals was identified by comparison. This study provided a precondition based on neural network for better mechanical interaction to improve experience during virtual interactions.