Back to Search
Start Over
A Survey of Vision-Based Human Action Evaluation Methods
- Source :
- Sensors, Vol 19, Iss 19, p 4129 (2019), Sensors (Basel, Switzerland)
- Publication Year :
- 2019
- Publisher :
- MDPI AG, 2019.
-
Abstract
- The fields of human activity analysis have recently begun to diversify. Many researchers have taken much interest in developing action recognition or action prediction methods. The research on human action evaluation differs by aiming to design computation models and evaluation approaches for automatically assessing the quality of human actions. This line of study has become popular because of its explosively emerging real-world applications, such as physical rehabilitation, assistive living for elderly people, skill training on self-learning platforms, and sports activity scoring. This paper presents a comprehensive survey of approaches and techniques in action evaluation research, including motion detection and preprocessing using skeleton data, handcrafted feature representation methods, and deep learning-based feature representation methods. The benchmark datasets from this research field and some evaluation criteria employed to validate the algorithms’ performance are introduced. Finally, the authors present several promising future directions for further studies.
- Subjects :
- feature learning
Computer science
02 engineering and technology
Review
deep learning features
Machine learning
computer.software_genre
lcsh:Chemical technology
Biochemistry
Field (computer science)
Analytical Chemistry
Machine Learning
Deep Learning
human action evaluation
0202 electrical engineering, electronic engineering, information engineering
Feature (machine learning)
Humans
hand-crafted features
lcsh:TP1-1185
action evaluation dataset
Electrical and Electronic Engineering
Instrumentation
business.industry
Deep learning
020207 software engineering
Motion detection
Atomic and Molecular Physics, and Optics
Action (philosophy)
Benchmark (computing)
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Feature learning
action quality assessment
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 19
- Issue :
- 19
- Database :
- OpenAIRE
- Journal :
- Sensors
- Accession number :
- edsair.doi.dedup.....8291bb289d70c0ccf13293699cf67e86