New machine learning approaches for real-life human activity recognition using smartphone sensor-based data-Reference-Cited by-同舟云学术

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New machine learning approaches for real-life human activity recognition using smartphone sensor-based data

Published:2023-02 Issue: Volume:262 Page:110260
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Garcia-Gonzalez Daniel^ORCID,Rivero Daniel,Fernandez-Blanco Enrique,Luaces Miguel R.^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference59 articles.

1. Human activity recognition and pattern discovery;Kim;IEEE Pervasive Comput.,2009

2. Human activity recognition from 3d data: A review;Aggarwal;Pattern Recognit. Lett.,2014

3. Cross-subject transfer learning in human activity recognition systems using generative adversarial networks;Soleimani;Neurocomputing,2021

4. Accelerometer-based human activity recognition in smartphones for healthcare services;Torres-Huitzil,2015

5. Sensor-based human activity recognition for smart healthcare: A semi-supervised machine learning;Zahin,2019

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1. A novel hybrid deep learning approach with GWO–WOA optimization technique for human activity recognition;Biomedical Signal Processing and Control;2025-01

2. An Efficient and Optimized CNN-LSTM Framework for Complex Human Activity Recognition System Using Surface EMG Physiological Sensors and Feature Engineering;2024 IEEE Students Conference on Engineering and Systems (SCES);2024-06-21

3. Human-based gait authentication using multi-modal sensors at the tactical edge device: smartphone;Big Data VI: Learning, Analytics, and Applications;2024-06-10

4. Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization;Sensors;2024-05-10

5. A method for human behavior identification based on integrated sensor data using XGBoost classifier with PCA techniques;Physica Scripta;2024-04-26

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