Loading...
Please wait, while we are loading the content...
Similar Documents
Discrete HMM for Visualizing Domiciliary Human Activity Perception and Comprehension
| Content Provider | MDPI |
|---|---|
| Author | Kuan, Ta-Wen Tseng, Shih-Pang Chen, Che-Wen Wang, Jhing-Fa Sun, Chieh-An |
| Copyright Year | 2022 |
| Description | Advances in artificial intelligence-based autonomous applications have led to the advent of domestic robots for smart elderly care; the preliminary critical step for such robots involves increasing the comprehension of robotic visualizing of human activity recognition. In this paper, discrete hidden Markov models (D-HMMs) are used to investigate human activity recognition. Eleven daily home activities are recorded using a video camera with an RGB-D sensor to collect a dataset composed of 25 skeleton joints in a frame, wherein only 10 skeleton joints are utilized to efficiently perform human activity recognition. Features of the chosen ten skeleton joints are sequentially extracted in terms of pose sequences for a specific human activity, and then, processed through coordination transformation and vectorization into a codebook prior to the D-HMM for estimating the maximal posterior probability to predict the target. In the experiments, the confusion matrix is evaluated based on eleven human activities; furthermore, the extension criterion of the confusion matrix is also examined to verify the robustness of the proposed work. The novelty indicated D-HMM theory is not only promising in terms of speech signal processing but also is applicable to visual signal processing and applications. |
| Starting Page | 3070 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app12063070 |
| Journal | Applied Sciences |
| Issue Number | 6 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-03-17 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Industrial Engineering Discrete Hmm Human Activity Comprehension Pose Recognition Confusion Matrix Autonomous Ai |
| Content Type | Text |
| Resource Type | Article |
