Human Gait State Prediction Using Cellular Automata and Classification Using ELM

In this research article, we have reported periodic cellular automata rules for different gait state prediction and classification of the gait data using Extreme Machine Leaning (ELM). This research is the first attempt to use cellular automaton to understand the complexity of bipedal walk. Due to nonlinearity, varying configurations throughout the gait cycle and the passive joint located at the unilateral foot-ground contact in bipedal walk resulting variation of dynamic descriptions and control laws from phase to phase for human gait is making difficult to predict the bipedal walk states. We have designed the cellular automata rules which will predict the next gait state of bipedal steps based on the previous two neighbor states. We have designed cellular automata rules for normal walk. The state prediction will help to correctly design the bipedal walk. The normal walk depends on next two states and has total eight states. We have considered the current and previous states to predict next state. So we have formulated 16 rules using cellular automata, eight rules for each leg. The priority order maintained using the fact that if right leg in swing phase then left leg will be in stance phase. To validate the model we have classified the gait Data using ELM (Huang et al. Proceedings of 2004 IEEE international joint conference on neural networks, vol 2. IEEE, 2004, [1]) and achieved accuracy 60%. We have explored the trajectories and compares with another gait trajectories. Finally we have presented the error analysis for different joints.