Position-aware Human Activity Recognition with Smartphone Sensors based on Deep Learning Approaches-Reference-Cited by-同舟云学术

Position-aware Human Activity Recognition with Smartphone Sensors based on Deep Learning Approaches

Published:2023-07-12 Issue: Volume: Page:
ISSN:
Container-title:2023 46th International Conference on Telecommunications and Signal Processing (TSP)
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Author:

Mekruksavanich Sakorn¹,Jitpattanakul Anuchit²

Affiliation:

1. Technology University of Phayao,School of Information and Communication,Department of Computer Engineering,Phayao,Thailand

2. King Mongkut’s University of Technology North Bangkok,Intelligent and Nonlinear Dynamic Innovations Research Center,Department of Mathematics Faculty of Applied Science,Bangkok,Thailand

Funder

University of Phayao

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10197649/10197641/10197773.pdf?arnumber=10197773

Reference19 articles.

1. On the Properties of Neural Machine Translation: Encoder–Decoder Approaches

2. Deep Residual Network for Smartwatch-Based User Identification through Complex Hand Movements

4. Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and Long Short Term Memory Neural Networks

5. A Multichannel CNN-LSTM Network for Daily Activity Recognition using Smartwatch Sensor Data

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1. An Extensive Analysis of Deep Learning-Based Human Activity Detection Techniques;2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT);2024-01-04

2. Optimizing On-Body Sensor Placements for Deep Learning-Driven Human Activity Recognition;Lecture Notes in Computer Science;2024