Human activity recognition with inertial sensors using a deep learning approach

Research output: Research - peer-reviewConference contribution


Our focus in this research is on the use of deep learning approaches for human activity recognition (HAR) scenario, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are predefined human activities. Here, we present a feature learning method that deploys convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. The influence of various important hyper-parameters such as number of convolutional layers and kernel size on the performance of CNN was monitored. Experimental results indicate that CNNs achieved significant speed-up in computing and deciding the final class and marginal improvement in overall classification accuracy compared to the baseline models such as Support Vector Machines and Multi-layer perceptron networks.

Bibliographical metadata

Original languageEnglish
Title of host publicationProceedings of IEEE SENSORS 2016
PublisherInstitute of Electrical and Electronics Engineers
Number of pages3
ISBN (Electronic)978-1-4799-8287-5
StatePublished - 1 Jan 2017

Publication series

NameProceedings of IEEE SENSORS 2016 (Orlando, FL USA)