Feature learning for human activity recognition using convolutional neural networks: A case study for inertial measurement unit and audio data


The use of Convolutional Neural Networks (CNNs) as a feature learning method for Human Activity Recognition (HAR) is becoming more and more common. Unlike conventional machine learning methods, which require domain-specific expertise, CNNs can extract features automatically. On the other hand, CNNs require a training phase, making them prone to the cold-start problem. In this work, a case study is presented where the use of a pre-trained CNN feature extractor is evaluated under realistic conditions. The case study consists of two main steps: (1) different topologies and parameters are assessed to identify the best candidate models for HAR, thus obtaining a pre-trained CNN model. The pre-trained model (2) is then employed as feature extractor evaluating its use with a large scale real-world dataset. Two CNN applications were considered: Inertial Measurement Unit (IMU) and audio based HAR. For the IMU data, balanced accuracy was 91.98% on the UCI-HAR dataset, and 67.51% on the real-world Extrasensory dataset. For the audio data, the balanced accuracy was 92.30% on the DCASE 2017 dataset, and 35.24% on the Extrasensory dataset.


open access article


Convolutional Neural Networks, Human Activity Recognition, Deep learning


F. Cruciani, A. Vafeiadis, C. Nugent, I. Cleland, P. McCullagh, K. Votis, D. Giakoumis, D. Tzovaras, L. Chen, R. Hamzaoui. (2020) Feature learning for Human Activity Recognition using Convolutional Neural Networks. CCF Transactions on Pervasive Computing and Interaction.


Research Institute

Cyber Technology Institute (CTI)