Human Activity Recognition based on MEMS Sensors Using Convolutional Neural Network

Human Activity Recognition was a key part of medical and mobile applications in today’s world, with important applications in old-age homes and monitoring daily activities. In this paper, we proposed an automated Human Activity Recognition model using a 1-Dimensional Convolutional Neural Network (CNN). To select the optimal parameters for the CNN model, we used the Grid Search Cross Validation method. On one hand, we used a manual feature extraction and CNN method. Starting from the physical significance of accelerometer and gyroscope signals, we extracted a new set of features. Experimental results show that the new features we extracted perform well in common human activity recognition tasks using SVM, MLP, and CNN. On the other hand, we used a method where CNN automatically extracts features and performs recognition. Experimental results indicate that CNN's automatic feature extraction is more effective than manual extraction. The model proposed in this paper achieved a recognition rate of up to 98.53%, which is 2% higher than the current accuracy.