Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor-Reference-Cited by-同舟云学术

Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor

Published:2022 Issue: Volume:10 Page:1-16
ISSN:2168-2372
Container-title:IEEE Journal of Translational Engineering in Health and Medicine
language:
Short-container-title:IEEE J. Transl. Eng. Health Med.

Author:

Khatun Mst. Alema¹,Yousuf Mohammad Abu¹^ORCID,Ahmed Sabbir¹^ORCID,Uddin Md. Zia²^ORCID,Alyami Salem A.³^ORCID,Al-Ashhab Samer³,Akhdar Hanan F.⁴^ORCID,Khan Asaduzzaman⁵,Azad Akm⁶,Moni Mohammad Ali⁵^ORCID

Affiliation:

1. Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh

2. SINTEF Digital, Oslo, Norway

3. Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

4. Department of Physics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

5. School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, Saint Lucia, QLD, Australia

6. Faculty of Science, Engineering & Technology, Swinburne University of Technology Sydney, Parramatta, NSW, Australia

Funder

Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University through Research Group

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Medicine

Link

http://xplorestaging.ieee.org/ielx7/6221039/9652399/09781408.pdf?arnumber=9781408

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1. An optimized feature selection using bio-geography optimization technique for human walking activities recognition

2. Clinical Human Gait Classification: Extreme Learning Machine Approach