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

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.

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 A Robust Deep Learning Approach for Position-Independent Smartphone-Based Human Activity Recognition

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3726 ◽  
Author(s):  
Bandar Almaslukh ◽  
Abdel Artoli ◽  
Jalal Al-Muhtadi
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Activity Recognition ◽  
Human Activity ◽  
Cost Effective ◽  
Human Activity Recognition ◽  
Position Detection ◽  
Proposed Model ◽  
Deep Convolution Neural Network ◽  
Real Time Applications

Recently, modern smartphones equipped with a variety of embedded-sensors, such as accelerometers and gyroscopes, have been used as an alternative platform for human activity recognition (HAR), since they are cost-effective, unobtrusive and they facilitate real-time applications. However, the majority of the related works have proposed a position-dependent HAR, i.e., the target subject has to fix the smartphone in a pre-defined position. Few studies have tackled the problem of position-independent HAR. They have tackled the problem either using handcrafted features that are less influenced by the position of the smartphone or by building a position-aware HAR. The performance of these studies still needs more improvement to produce a reliable smartphone-based HAR. Thus, in this paper, we propose a deep convolution neural network model that provides a robust position-independent HAR system. We build and evaluate the performance of the proposed model using the RealWorld HAR public dataset. We find that our deep learning proposed model increases the overall performance compared to the state-of-the-art traditional machine learning method from 84% to 88% for position-independent HAR. In addition, the position detection performance of our model improves superiorly from 89% to 98%. Finally, the recognition time of the proposed model is evaluated in order to validate the applicability of the model for real-time applications.

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