Accelerometer-based human activity recognition using 1D convolutional neural network

Abstract

Human activity recognition (HAR) is an important research field with a variety of applications in healthcare monitoring, fitness tracking and in user-adaptive systems in smart environments. The performance of the activity recognition system is highly dependent on the features extracted from the sensor data which makes the selection of appropriate features a very important part of HAR. A 1D CNN model trained on accelerometer data is suggested in the paper for automatic feature extraction in a HAR system. A semi-automatic approach is used that effectively and efficiently determines the number of convolutional layers in the network, the number of kernels and the size of the kernels. The experimental results show that the suggested model outperforms several existing recognition approaches that use the same data set.