Towards Environment-Independent Human Activity Recognition using Deep Learning and Enhanced CSI

2020 ◽  
Author(s):  
Zhenguo Shi ◽  
J. Andrew Zhang ◽  
Richard Xu ◽  
Qingqing Cheng ◽  
Andre Pearce
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition

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.

scholarly journals Iss2Image: A Novel Signal-Encoding Technique for CNN-Based Human Activity Recognition

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3910 ◽  
Author(s):  
Taeho Hur ◽  
Jaehun Bang ◽  
Thien Huynh-The ◽  
Jongwon Lee ◽  
Jee-In Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Expert Knowledge ◽  
Inertial Sensor ◽  
Human Activity Recognition ◽  
Learning Technology ◽  
Sensor Signal ◽  
Signal Encoding ◽  
The Right

The most significant barrier to success in human activity recognition is extracting and selecting the right features. In traditional methods, the features are chosen by humans, which requires the user to have expert knowledge or to do a large amount of empirical study. Newly developed deep learning technology can automatically extract and select features. Among the various deep learning methods, convolutional neural networks (CNNs) have the advantages of local dependency and scale invariance and are suitable for temporal data such as accelerometer (ACC) signals. In this paper, we propose an efficient human activity recognition method, namely Iss2Image (Inertial sensor signal to Image), a novel encoding technique for transforming an inertial sensor signal into an image with minimum distortion and a CNN model for image-based activity classification. Iss2Image converts real number values from the X, Y, and Z axes into three color channels to precisely infer correlations among successive sensor signal values in three different dimensions. We experimentally evaluated our method using several well-known datasets and our own dataset collected from a smartphone and smartwatch. The proposed method shows higher accuracy than other state-of-the-art approaches on the tested datasets.

scholarly journals Design of Optimal Deep Learning based Human Activity Recognition on Sensor Enabled Internet of Things Environment

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Fahd N. Al-Wesabi ◽  
Amani Abdulrahman Albraikan ◽  
Anwer Mustafa Hilal ◽  
Asma Abdulghani Al-Shargabi ◽  
Saleh Alhazbi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Internet Of Things Environment

scholarly journals Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

2021 ◽  
Author(s):  
Pranjal Kumar
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Well Being ◽  
Human Activity Recognition ◽  
Research Field ◽  
Daily Lives ◽  
Video Cameras ◽  
Advantages And Disadvantages

Human Activity Recognition (HAR) has become a vibrant research field over the last decade, especially because of the spread of electronic devices like mobile phones, smart cell phones, and video cameras in our daily lives. In addition, the progress of deep learning and other algorithms has made it possible for researchers to use HAR in many fields including sports, health, and well-being. HAR is, for example, one of the most promising resources for helping older people with the support of their cognitive and physical function through day-to-day activities. This study focuses on the key role machine learning plays in the development of HAR applications. While numerous HAR surveys and review articles have previously been carried out, the main/overall HAR issue was not taken into account, and these studies concentrate only on specific HAR topics. A detailed review paper covering major HAR topics is therefore essential. This study analyses the most up-to-date studies on HAR in recent years and provides a classification of HAR methodology and demonstrates advantages and disadvantages for each group of methods. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.

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