Hybrid model featuring CNN and LSTM architecture for Human Activity Recognition on smartphone sensor data

Samundra Deep, Xi Zheng

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)

Abstract

The traditional methods of recognizing human activities involve typical machine learning (ML) algorithms which uses heuristic engineered features. Human activities are dynamic in nature and are encoded with a sequence of actions. ML methods are able to perform activity recognition tasks but may not exploit the temporal correlations of the input data. Therefore, in this paper, we proposed and showed the effectiveness of employing a new combination of deep learning (DL) methods for human activity recognition (HAR). DL methods are capable of extracting discriminative features automatically from the raw sensor data. Specifically, in this paper, we proposed a hybrid architecture which features a combination of Convolutional neural networks (CNN) and Long short-term Memory (LSTM) networks for HAR task. The model is tested on UCI HAR dataset which is a benchmark dataset and comprises of accelerometer and gyroscope data obtained from a smartphone. Our experimental results showed that our proposed method outperformed the recent results which used pure LSTM and bidirectional LSTM networks on the same dataset.
Original languageEnglish
Title of host publicationInternational Conference on Parallel and Distributed Computing, Applications and Technologies
EditorsHui Tian, Hong Shen, Wee Lum Tan
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages262-267
Number of pages6
ISBN (Electronic)9781728126166
DOIs
Publication statusPublished - 2019
Event20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2019 - Gold Coast, Australia
Duration: 5 Dec 20197 Dec 2019

Conference

Conference20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2019
CountryAustralia
CityGold Coast
Period5/12/197/12/19

Keywords

  • Activity recognition
  • CNN
  • Deep learning
  • Hybrid model
  • LSTM
  • Neural network

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