Interpretable Human Activity Recognition With Temporal Convolutional Networks and Model-Agnostic Explanations-Reference-Cited by-同舟云学术

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Interpretable Human Activity Recognition With Temporal Convolutional Networks and Model-Agnostic Explanations

Published:2024-09-01 Issue:17 Volume:24 Page:27607-27617
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Bijalwan Vishwanath¹^ORCID,Manan Khan Abdul²,Baek Hangyeol³^ORCID,Jeon Sangmin³^ORCID,Kim Youngshik⁴^ORCID

Affiliation:

1. ICRS Laboratory, Hanbat National University, Daejeon, South Korea

2. System and Dyson School of Design Engineering at Imperial College London, London, U.K.

3. Department of Mechanical Engineering, ICRS Laboratory, Hanbat National University, Daejeon, South Korea

4. Department of Mechanical Engineering, Hanbat National University, Daejeon, South Korea

Funder

National Research Foundation of Korea

Korean Government

“Regional Innovation Strategy (RIS)” through NRF

Ministry of Education

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/7361/10660640/10579767.pdf?arnumber=10579767

Reference26 articles.

1. IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment

2. Wearable sensor-based pattern mining for human activity recognition: deep learning approach

3. HDL-PSR: Modelling Spatio-Temporal Features Using Hybrid Deep Learning Approach for Post-Stroke Rehabilitation

4. Activity recognition in beach volleyball using a Deep Convolutional Neural Network

5. Estimation of gait kinematics and kinetics from inertial sensor data using optimal control of musculoskeletal models

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