Mobile Phone Behavior Recognition Based on Convolutional Neural Network
Keywords:
Behavior Recognition, MLP, CNN, Batch NormalizationAbstract
With the continuous enhancement of computational capabilities in smartphones and the increasing integration of various sensors into them, smartphones have transcended their conventional role in communication and gained significant application value in areas like human-computer interaction, fall detection, and information security. Utilizing smartphone sensor data for human activity recognition has also become a prominent research focus. Based on mobile phone behavior recognition data from Baidu Feizi, this paper constructs convolutional neural network and uses batch normalization method to accelerate the convergence speed, and the final accuracy rate reaches 96.7. The algorithms proposed in this paper are effective in recognizing six distinct behaviors. The study concludes with an analysis of various models, discussing their strengths and weaknesses in the context of behavior recognition applications.
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