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A research on applying IMUs to predict fall of a humanoid robot based on the Neural Network model
knee angle measurement
In a society where ageing problems have become more serious in recent years, a complete course of treatment is not just the end of surgery, it includes follow-up exercises as well as tracking and observation, so it requires close attention when patients are rehabilitated at home. Cooperate, but many patients will not be able to achieve the desired effect because of various factors, so it is very important to have a home mechanism that allows patients and physicians to communicate with each other and record patient information. The research system mainly uses a six-axis inertial sensor to measure the knee angle and transmit the information to the computer, and uses the Internet to store the data in the back-end database, and applies the system to the robot walking trajectory to simulate The doctors of the hospital watched, instantly grasped and monitored the rehabilitation situation of each patient, and gave the patients feedback, and the robots replaced the patients to achieve complementary effects with the doctors. This study proposes a fall prediction method based on a six-axis inertial sensing device. The fall detection is a signal after a fall, and the fall prediction is a signal before the fall and the signal before the ground has occurred. To avoid the occurrence of falls. Since the fall is a continuous time action, it can be regarded as a classification problem of continuous action. In this study, we use the neural network to do this analysis to complete the fall prediction, and we mainly use the Bioloid robot to simulate the fall action. The occurrence of this is to avoid the objectivity of experimenting with the human body. Because falling forward is easy to cause serious injury, this article takes the forward fall as our research. In the experimental part, in order to complete the reading of the inertial sensor. Take, we use arduino as the development environment to pass the read value to python for the nerve-like experiment. In addition, we have established a neural network with more than 60% accuracy, in addition to good results. It is also true that the effect of the fall prediction can be completed early.
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