Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models
Motion sequence data comprise a chronologically organized recording of a series of movements or actions carried out by a human being. Motion patterns found in such data holds significance for research and applications across multiple fields. In recent years, various feature representation techniques...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Jezik: | English |
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MDPI AG
2024-01-01
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| Serija: | Mathematics |
| Teme: | |
| Online dostop: | https://www.mdpi.com/2227-7390/12/2/185 |
| _version_ | 1827371585202290688 |
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| author | Xiangzeng Kong Xinyue Liu Shimiao Chen Wenxuan Kang Zhicong Luo Jianjun Chen Tao Wu |
| author_facet | Xiangzeng Kong Xinyue Liu Shimiao Chen Wenxuan Kang Zhicong Luo Jianjun Chen Tao Wu |
| author_sort | Xiangzeng Kong |
| collection | DOAJ |
| description | Motion sequence data comprise a chronologically organized recording of a series of movements or actions carried out by a human being. Motion patterns found in such data holds significance for research and applications across multiple fields. In recent years, various feature representation techniques have been proposed to carry out sequence analysis. However, many of these methods have not fully uncovered the correlations between elements in sequences nor the internal interrelated structures among different dimensions, which are crucial to the recognition of motion patterns. This study proposes a novel Adaptive Sequence Coding (ASC) feature representation with ensemble hidden Markov models for motion sequence analysis. The ASC adopts the dual symbolization integrating first-order differential symbolization and event sequence encoding to effectively represent individual motion sequences. Subsequently, an adaptive boost algorithm based on a hidden Markov model is presented to distinguish the coded sequence data into different motion patterns. The experimental results on several publicly available datasets demonstrate that the proposed methodology outperforms other competing techniques. Meanwhile, ablation studies conducted on ASC and the adaptive boost approach further verify their significant potential in motion sequence analysis. |
| first_indexed | 2024-03-08T10:42:33Z |
| format | Article |
| id | doaj.art-b6d96ecf3ac94b5e95344fa7545e5af0 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-08T10:42:33Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-b6d96ecf3ac94b5e95344fa7545e5af02024-01-26T17:30:59ZengMDPI AGMathematics2227-73902024-01-0112218510.3390/math12020185Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov ModelsXiangzeng Kong0Xinyue Liu1Shimiao Chen2Wenxuan Kang3Zhicong Luo4Jianjun Chen5Tao Wu6College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350100, ChinaSchool of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaSchool of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, ChinaCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350100, ChinaDepartment of Computing, Xi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaSchool of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaMotion sequence data comprise a chronologically organized recording of a series of movements or actions carried out by a human being. Motion patterns found in such data holds significance for research and applications across multiple fields. In recent years, various feature representation techniques have been proposed to carry out sequence analysis. However, many of these methods have not fully uncovered the correlations between elements in sequences nor the internal interrelated structures among different dimensions, which are crucial to the recognition of motion patterns. This study proposes a novel Adaptive Sequence Coding (ASC) feature representation with ensemble hidden Markov models for motion sequence analysis. The ASC adopts the dual symbolization integrating first-order differential symbolization and event sequence encoding to effectively represent individual motion sequences. Subsequently, an adaptive boost algorithm based on a hidden Markov model is presented to distinguish the coded sequence data into different motion patterns. The experimental results on several publicly available datasets demonstrate that the proposed methodology outperforms other competing techniques. Meanwhile, ablation studies conducted on ASC and the adaptive boost approach further verify their significant potential in motion sequence analysis.https://www.mdpi.com/2227-7390/12/2/185motion sequencedual symbolizationevent encodinghidden Markov modeladaptive boost |
| spellingShingle | Xiangzeng Kong Xinyue Liu Shimiao Chen Wenxuan Kang Zhicong Luo Jianjun Chen Tao Wu Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models Mathematics motion sequence dual symbolization event encoding hidden Markov model adaptive boost |
| title | Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models |
| title_full | Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models |
| title_fullStr | Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models |
| title_full_unstemmed | Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models |
| title_short | Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models |
| title_sort | motion sequence analysis using adaptive coding with ensemble hidden markov models |
| topic | motion sequence dual symbolization event encoding hidden Markov model adaptive boost |
| url | https://www.mdpi.com/2227-7390/12/2/185 |
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