Self-maintenance and automatic identification of the fatigue status of the human body based on Internet of Things technology

摘要

This paper designs and manufactures a complete set of intelligent recognition system based on the Internet of Things (IoT), which can evaluate the fatigue status of the leg muscles based on the surface EMG signals of multiple parts of the leg muscles. The data set is pre-processed by slicing and other pre-processing to obtain a set of fatigue examples suitable for model training input. The fatigue examples can be used as input to build and train a multi-layer two-way leg muscle fatigue status recognition model based on Long Short-Term Memory (LSTM). The experimental results on the test set show that the overall recognition system works stably during running, but its ability to recognize and generalize the fatigue status of the legs is not good, after the fatigue status is stabilized, the discrimination accuracy is improved, the model can make highly accurate status recognition judgments on the fatigue instance set, with an accuracy of 87.54%.