Human Gait State Prediction Using Cellular Automata and Classification Using ELM
Human Gait State Prediction Using Cellular Automata and Classification Using ELM
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DOI：
10.1007/978-981-13-0923-6
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发表时间：
2018-08
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通讯作者：
Vijay Bhaskar Semwal;Neha Gaud;G. Nandi
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作者：
Vijay Bhaskar Semwal;Neha Gaud;G. Nandi
在这篇研究文章中，我们报道了使用极限机器学习(ELM)对步态数据进行不同步态预测和分类的周期性元胞自动机规则。这项研究是首次尝试使用元胞自动机来理解两足行走的复杂性。由于步态周期的非线性，以及被动关节位于双足步行的单侧足-地接触处，导致人体步态的动力学描述和控制律随阶段的变化而变化，使得两足步行状态的预测变得困难。我们设计了元胞自动机规则，它将根据前两个相邻状态预测两足步态的下一步步态。我们设计了正常行走的元胞自动机规则。状态预测将有助于正确设计两足步行。正常的步行取决于接下来的两个状态，总共有八个状态。我们已经考虑了当前状态和以前的状态来预测下一状态。因此，我们使用元胞自动机制定了16条规则，每条腿8条规则。如果右腿处于摆动阶段，那么左腿将处于站立阶段，这一事实保持了优先顺序。为了验证该模型，我们使用ELM对步态数据进行了分类(Huang等人。2004年IEEE国际神经网络联席会议论文集，第二卷，IEEE，2004，[1])，准确率达到60%。我们探索了步态轨迹，并与另一种步态轨迹进行了比较。最后给出了不同接头的误差分析。
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.