Human Activity Recognition Using Sensors and Machine Learning: 2nd Edition
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 84
Special Issue Editors
Interests: deep learning; mobile computing; pervasive computing; Internet of Things; brain–computer interface; health informatics
Special Issues, Collections and Topics in MDPI journals
Interests: data mining, deep learning and sensor-based human activity recognition
Special Issues, Collections and Topics in MDPI journals
Interests: pattern recognition; application of artificial intelligence technology in water transportation systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The recent advances in hardware and acquisition devices have accelerated the deployment of the Internet of Things, thus enabling myriad applications of human activity recognition. Human activity recognition is a time series classification task that involves predicting user behavior based on sensor data. The task is challenging in real-world applications due to many inherent issues and various practical problems in different scenarios. The most major inherent issue is how to filter noisy sensor data and extract high-quality features for better recognition performance. The practical problems include lightweight algorithms for wearable devices, modeling human behaviors with fewer annotated data, learning to recognize complex activities, continually learning patterns of streaming data, etc. Recently, we have witnessed compelling evidence from successful investigations of machine learning for activity recognition. While machine learning is shown to be effective and achieve state-of-the-art performance, the increasing number of related studies indicates that, in both academic and industrial communities, there is a considerable demand for developing more advanced machine learning algorithms in order to tackle the challenges and achieve a better activity recognition performance. Therefore, it is vital and timely to offer an opportunity of reporting the progress in human activity recognition using sensors and machine learning. The research foci of this Special Issue include theoretical studies, model designs, development, and advanced applications of machine learning algorithms on sensor-based activity data.
Dr. Dalin Zhang
Dr. Kaixuan Chen
Prof. Dr. Xu Cheng
Dr. Huan Liu
Guest Editors
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Keywords
- supervised learning
- semi-supervised learning
- unsupervised learning
- active learning
- transfer learning
- online learning
- imbalance learning
- representation learning
- ensemble methods
- auto-machine learning
- data segmentation
- explainable
Related Special Issue
- Human Activity Recognition Using Sensors and Machine Learning in Sensors (13 articles)
