scholarly journals Utilizing deep learning models in CSI-based human activity recognition

2022 ◽  
Author(s):  
Eman Shalaby ◽  
Nada ElShennawy ◽  
Amany Sarhan
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Learning Models

scholarly journals Study on Human Activity Recognition Using Semi-Supervised Active Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2760
Author(s):  
Seungmin Oh ◽  
Akm Ashiquzzaman ◽  
Dongsu Lee ◽  
Yeonggwang Kim ◽  
Jinsul Kim
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Random Sampling ◽  
Data Gathering ◽  
Human Activity Recognition ◽  
Learning Models ◽  
Active Transfer ◽  
Existing Data

In recent years, various studies have begun to use deep learning models to conduct research in the field of human activity recognition (HAR). However, there has been a severe lag in the absolute development of such models since training deep learning models require a lot of labeled data. In fields such as HAR, it is difficult to collect data and there are high costs and efforts involved in manual labeling. The existing methods rely heavily on manual data collection and proper labeling of the data, which is done by human administrators. This often results in the data gathering process often being slow and prone to human-biased labeling. To address these problems, we proposed a new solution for the existing data gathering methods by reducing the labeling tasks conducted on new data based by using the data learned through the semi-supervised active transfer learning method. This method achieved 95.9% performance while also reducing labeling compared to the random sampling or active transfer learning methods.

Deep Learning Models for Real-time Human Activity Recognition with Smartphones

2019 ◽  
Vol 25 (2) ◽  
pp. 743-755 ◽  
Author(s):  
Shaohua Wan ◽  
Lianyong Qi ◽  
Xiaolong Xu ◽  
Chao Tong ◽  
Zonghua Gu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Learning Models

Assessment of Deep Learning Models for Human Activity Recognition on Multi-variate Time Series Data and Non-targeted Adversarial Attack

2021 ◽  
pp. 129-159
Author(s):  
Mahbuba Tasmin ◽  
Sharif Uddin Ruman ◽  
Taoseef Ishtiak ◽  
Arif-ur-Rahman Chowdhury Suhan ◽  
Redwan Hasif ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Time Series Data ◽  
Human Activity Recognition ◽  
Series Data ◽  
Learning Models ◽  
Adversarial Attack

scholarly journals Biometric User Identification Based on Human Activity Recognition Using Wearable Sensors: An Experiment Using Deep Learning Models

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 308
Author(s):  
Sakorn Mekruksavanich ◽  
Anuchit Jitpattanakul
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Wearable Sensors ◽  
The Elderly ◽  
Human Activity Recognition ◽  
Learning Models ◽  
User Identification ◽  
Sensory Data

Currently, a significant amount of interest is focused on research in the field of Human Activity Recognition (HAR) as a result of the wide variety of its practical uses in real-world applications, such as biometric user identification, health monitoring of the elderly, and surveillance by authorities. The widespread use of wearable sensor devices and the Internet of Things (IoT) has led the topic of HAR to become a significant subject in areas of mobile and ubiquitous computing. In recent years, the most widely-used inference and problem-solving approach in the HAR system has been deep learning. Nevertheless, major challenges exist with regard to the application of HAR for problems in biometric user identification in which various human behaviors can be regarded as types of biometric qualities and used for identifying people. In this research study, a novel framework for multi-class wearable user identification, with a basis in the recognition of human behavior through the use of deep learning models, is presented. In order to obtain advanced information regarding users during the performance of various activities, sensory data from tri-axial gyroscopes and tri-axial accelerometers of the wearable devices are applied. Additionally, a set of experiments were shown to validate this work, and the proposed framework’s effectiveness was demonstrated. The results for the two basic models, namely, the Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM) deep learning, showed that the highest accuracy for all users was 91.77% and 92.43%, respectively. With regard to the biometric user identification, these are both acceptable levels.

Sign in / Sign up

Export Citation Format

Share Document