research-article

Activity Recognition Using Dual-ConvLSTM Extracting Local and Global Features for SHL Recognition Challenge

Authors:

Katsuhiko Kaji,

Nobuo KawaguchiAuthors Info & Claims

UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers

Pages 1643 - 1651

https://doi.org/10.1145/3267305.3267533

Published: 08 October 2018 Publication History

Abstract

For high precision estimation with SHL recognition challenge, we use a deep learning framework based on convolutional layers and LSTM recurrent units (ConvLSTM). We, UCLab(submission 2), propose the model combined two different ConvLSTMs. One ConvLSTM of convolution layers has large kernel size and the other has small kernel size. We expect that these two ConvLSTMs extract the different kind of features like global features and local features. Then we concatenate each ConvLSTM output, and input to fully connected layer. Finally, we convert output of fully connected layer to probability distribution by applying soft-max function. We finally determine that we input 10 axes of sensors to our model. The axes we use are 3 axes of linear acceleration, 3 axes of gyroscope, 3 axes of normalize gravitational acceleration and pressure of difference from previous value. As a result, using last 20% of lines for validation, predictions have around 0.931 of F1-score.

References

[1]

Baoqi Huang Xiaomin Kang and Guodong Qi. A Novel Walking Detection and Step Counting Algorithm Using Unconstrained Smartphones. Sensors, 18(1), 2018.

[2]

Mustafa Kose, Ozlem Incel, and Cem Ersoy. Online Human Activity Recognition on Smart Phones. In Workshop on Mobile Sensing: From Smartphones and Wearables to Big Data, pages 11--15, 01 2012.

[3]

Akram Bayat, Marc Pomplun, and Duc A. Tran. A Study on Human Activity Recognition Using Accelerometer Data from Smartphones. Procedia Computer Science, 34:450--457, 2014. The 9th International Conference on Future Networks and Communications (FNC'14)/The 11th International Conference on Mobile Systems and Pervasive Computing (MobiSPC'14)/Affiliated Workshops.

[4]

H. Gjoreski, M. Ciliberto, F. J. Ordóñez Morales, D. Roggen, S. Mekki, and S. Valentin. A Versatile Annotated Dataset for Multimodal Locomotion Analytics with Mobile Devices. In Proc. ACM Conference on Embedded Networked Sensor Systems. ACM, 2017.

Digital Library

[5]

M. Ciliberto, F. J. Ordóñez Morales, H. Gjoreski, D. Roggen, S.Mekki, and S.Valentin. High Reliability Android Application for Multidevice Multimodal Mobile Data Acquisition and Annotation. In Proc. ACM Conference on Embedded Networked Sensor Systems. ACM, 2017.

Digital Library

[6]

H. Gjoreski, M. Ciliberto, L. Wang, F. J. Ordóñez Morales, S.Mekki, S.Valentin, and D. Roggen. The University of Sussex-Huawei Locomotion and Transportation Dataset for Multimodal Analytics with Mobile Devices. IEEE Access, pages 1--1, 2018.

[7]

Z. He and L. Jin. Activity recognition from acceleration data based on discrete consine transform and svm. In 2009 IEEE International Conference on Systems, Man and Cybernetics, pages 5041--5044, Oct 2009.

[8]

Jian Bo Yang, Minh Nhut Nguyen, Phyo Phyo San, Xiao Li Li, and Shonali Krishnaswamy. Deep Convolutional Neural Networks On Multichannel Time Series For Human Activity Recognition. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. IJCAI, 2015.

Digital Library

[9]

H. Matsuyama, K. Urano, K. Hiroi, K. Kaji, and N. Kawaguchi. Short Segment Random Forest with Post Processing using Label Constraint for SHL Recognition Challenge. In International Workshop on Human Activity Sensing Corpus and Its Application (HASCA2018). ACM, 2018.

Digital Library

[10]

M. Zeng, L. T. Nguyen, B. Yu, O. J. Mengshoel, J. Zhu, P. Wu, and J. Zhang. Convolutional neural networks for human activity recognition using mobile sensors. In 6th International Conference on Mobile Computing, Applications and Services, pages 197--205, Nov 2014.

[11]

M. Edel and E. KÃűppe. Binarized-blstm-rnn based human activity recognition. In 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pages 1--7, Oct 2016.

[12]

F. J. Ordóñez Morales and D. Roggen. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition. Sensors, 16(1), 2016.

[13]

Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa. Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification. ACM Transactions on Graphics (Proc. of SIGGRAPH 2016), 35(4):110:1--110:11, 2016.

Digital Library

[14]

Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller. Striving for Simplicity: The All Convolutional Net. arXiv:1412.6806, 2014.

[15]

Lin Wang, Hristijan Gjoreski, Kazuya Murao, Tsuyoshi Okita, and Daniel Roggen. Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge. In Proceedings of the 6th International Workshop on Human Activity Sensing Corpus and Applications (HASCA2018), Singapore, Oct 2018.

Digital Library

Cited By

Ahmad NLeung H(2024)HyperHARProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435118:1(1-29)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643511
Qu YTang YYang XWen YZhang W(2023)Context-aware mutual learning for semi-supervised human activity recognition using wearable sensorsExpert Systems with Applications10.1016/j.eswa.2023.119679219(119679)Online publication date: Jun-2023
https://doi.org/10.1016/j.eswa.2023.119679
Khater SHadhoud MFayek M(2022)A novel human activity recognition architecture: using residual inception ConvLSTM layerJournal of Engineering and Applied Science10.1186/s44147-022-00098-069:1Online publication date: 21-May-2022
https://doi.org/10.1186/s44147-022-00098-0
Show More Cited By

Index Terms

Activity Recognition Using Dual-ConvLSTM Extracting Local and Global Features for SHL Recognition Challenge
1. Computing methodologies
  1. Machine learning

Recommendations

Evaluation of hidden Markov models using deep CNN features in isolated sign recognition
Abstract
Isolated sign recognition from video streams is a challenging problem due to the multi-modal nature of the signs, where both local and global hand features and face gestures needs to be attended simultaneously. This problem has recently been ...
Facial expression recognition using bidirectional LSTM - CNN
Abstract
Nowadays, there has been much attention on computer vision regarding human-computer interaction, especially facial expression recognition (FER). Many researchers have explored and suggested systems for this field. In this paper, we propose the ...
Face recognition using dual-tree complex wavelet features

We propose a novel facial representation based on the dual-tree complex wavelet transform for face recognition. It is effective and efficient to represent the geometrical structures in facial image with low redundancy. Moreover, we experimentally verify ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers

October 2018

1881 pages

ISBN:9781450359665

DOI:10.1145/3267305

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
University of Florida: University of Florida
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

In-Cooperation

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 October 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

UbiComp '18

Sponsor:

SIGMOBILE
University of Florida
SIGCHI

UbiComp '18: The 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing

October 8 - 12, 2018

Singapore, Singapore

Acceptance Rates

Overall Acceptance Rate 764 of 2,912 submissions, 26%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
274
Total Downloads

Downloads (Last 12 months)24
Downloads (Last 6 weeks)3

Reflects downloads up to 23 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ahmad NLeung H(2024)HyperHARProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435118:1(1-29)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643511
Qu YTang YYang XWen YZhang W(2023)Context-aware mutual learning for semi-supervised human activity recognition using wearable sensorsExpert Systems with Applications10.1016/j.eswa.2023.119679219(119679)Online publication date: Jun-2023
https://doi.org/10.1016/j.eswa.2023.119679
Khater SHadhoud MFayek M(2022)A novel human activity recognition architecture: using residual inception ConvLSTM layerJournal of Engineering and Applied Science10.1186/s44147-022-00098-069:1Online publication date: 21-May-2022
https://doi.org/10.1186/s44147-022-00098-0
Zhang YD’Haeseleer ICoelho JVanden Abeele VVanrumste B(2021)Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and RecommendationsSensors10.3390/s2106217621:6(2176)Online publication date: 20-Mar-2021
https://doi.org/10.3390/s21062176
Chen KZhang DYao LGuo BYu ZLiu Y(2021)Deep Learning for Sensor-based Human Activity RecognitionACM Computing Surveys10.1145/344774454:4(1-40)Online publication date: 24-May-2021
https://dl.acm.org/doi/10.1145/3447744
Matsuyama HUrano KHiroi KKaji KKawaguchi N(2018)Short Segment Random Forest with Post Processing Using Label Constraint for SHL Recognition ChallengeProceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers10.1145/3267305.3267532(1636-1642)Online publication date: 8-Oct-2018
https://dl.acm.org/doi/10.1145/3267305.3267532
Wang LGjoreskia HMurao KOkita TRoggen D(2018)Summary of the Sussex-Huawei Locomotion-Transportation Recognition ChallengeProceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers10.1145/3267305.3267519(1521-1530)Online publication date: 8-Oct-2018
https://dl.acm.org/doi/10.1145/3267305.3267519

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents