An evolutionary approach to the design of convolutional neural networks for human activity recognition

Abstract

Automated human activity recognition has a number of applications such as in elderly healthcare monitoring, fitness tracking and in various smart home systems that can adapt to the inhabitants’ behavior. Deep learning using Convolutional Neural Networks (CNNs) is increasingly being used for recognition of human activities. However, the CNNs performance is highly dependent on the network architecture and usually the hyper-parameters are manually selected. Various approaches have been used to automate the design of CNN architectures. The paper proposes an evolutionary based approach for optimizing the architecture of one dimensional CNNs for human activity recognition. The suggested approach is tested on three accelerometric data sets. The experimental results show that the CNNs designed using the evolutionary based approach have comparable accuracy on the WISDM Actitracker data set and significantly better performance on the Smartphone-Based Recognition of Human Activities and Postural Transitions data set compared to other deep CNNs.