ARMA-Based Segmentation of Human Limb Motion Sequences
With the development of human motion capture (MoCap) equipment and motion analysis technologies, MoCap systems have been widely applied in many fields, including biomedicine, computer vision, virtual reality, etc. With the rapid increase in MoCap data collection in different scenarios and applicatio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/16/5577 |
| _version_ | 1797522049716977664 |
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| author | Feng Mei Qian Hu Changxuan Yang Lingfeng Liu |
| author_facet | Feng Mei Qian Hu Changxuan Yang Lingfeng Liu |
| author_sort | Feng Mei |
| collection | DOAJ |
| description | With the development of human motion capture (MoCap) equipment and motion analysis technologies, MoCap systems have been widely applied in many fields, including biomedicine, computer vision, virtual reality, etc. With the rapid increase in MoCap data collection in different scenarios and applications, effective segmentation of MoCap data is becoming a crucial issue for further human motion posture and behavior analysis, which requires both robustness and computation efficiency in the algorithm design. In this paper, we propose an unsupervised segmentation algorithm based on limb-bone partition angle body structural representation and autoregressive moving average (ARMA) model fitting. The collected MoCap data were converted into the angle sequence formed by the human limb-bone partition segment and the central spine segment. The limb angle sequences are matched by the ARMA model, and the segmentation points of the limb angle sequences are distinguished by analyzing the good of fitness of the ARMA model. A medial filtering algorithm is proposed to ensemble the segmentation results from individual limb motion sequences. A set of MoCap measurements were also conducted to evaluate the algorithm including typical body motions collected from subjects of different heights, and were labeled by manual segmentation. The proposed algorithm is compared with the principle component analysis (PCA), K-means clustering algorithm (K-means), and back propagation (BP) neural-network-based segmentation algorithms, which shows higher segmentation accuracy due to a more semantic description of human motions by limb-bone partition angles. The results highlight the efficiency and performance of the proposed algorithm, and reveals the potentials of this segmentation model on analyzing inter- and intra-motion sequence distinguishing. |
| first_indexed | 2024-03-10T08:23:05Z |
| format | Article |
| id | doaj.art-0d18ca85c36e4742aad8839934cf9e69 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T08:23:05Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0d18ca85c36e4742aad8839934cf9e692023-11-22T09:42:10ZengMDPI AGSensors1424-82202021-08-012116557710.3390/s21165577ARMA-Based Segmentation of Human Limb Motion SequencesFeng Mei0Qian Hu1Changxuan Yang2Lingfeng Liu3School of Information Engineering, East China Jiao Tong University, Nanchang 330013, ChinaSchool of Information Engineering, East China Jiao Tong University, Nanchang 330013, ChinaSchool of Information Engineering, East China Jiao Tong University, Nanchang 330013, ChinaSchool of Information Engineering, East China Jiao Tong University, Nanchang 330013, ChinaWith the development of human motion capture (MoCap) equipment and motion analysis technologies, MoCap systems have been widely applied in many fields, including biomedicine, computer vision, virtual reality, etc. With the rapid increase in MoCap data collection in different scenarios and applications, effective segmentation of MoCap data is becoming a crucial issue for further human motion posture and behavior analysis, which requires both robustness and computation efficiency in the algorithm design. In this paper, we propose an unsupervised segmentation algorithm based on limb-bone partition angle body structural representation and autoregressive moving average (ARMA) model fitting. The collected MoCap data were converted into the angle sequence formed by the human limb-bone partition segment and the central spine segment. The limb angle sequences are matched by the ARMA model, and the segmentation points of the limb angle sequences are distinguished by analyzing the good of fitness of the ARMA model. A medial filtering algorithm is proposed to ensemble the segmentation results from individual limb motion sequences. A set of MoCap measurements were also conducted to evaluate the algorithm including typical body motions collected from subjects of different heights, and were labeled by manual segmentation. The proposed algorithm is compared with the principle component analysis (PCA), K-means clustering algorithm (K-means), and back propagation (BP) neural-network-based segmentation algorithms, which shows higher segmentation accuracy due to a more semantic description of human motions by limb-bone partition angles. The results highlight the efficiency and performance of the proposed algorithm, and reveals the potentials of this segmentation model on analyzing inter- and intra-motion sequence distinguishing.https://www.mdpi.com/1424-8220/21/16/5577MoCapIMUARMADTWlimb motion sequence segmentationensemble median filtering |
| spellingShingle | Feng Mei Qian Hu Changxuan Yang Lingfeng Liu ARMA-Based Segmentation of Human Limb Motion Sequences Sensors MoCap IMU ARMA DTW limb motion sequence segmentation ensemble median filtering |
| title | ARMA-Based Segmentation of Human Limb Motion Sequences |
| title_full | ARMA-Based Segmentation of Human Limb Motion Sequences |
| title_fullStr | ARMA-Based Segmentation of Human Limb Motion Sequences |
| title_full_unstemmed | ARMA-Based Segmentation of Human Limb Motion Sequences |
| title_short | ARMA-Based Segmentation of Human Limb Motion Sequences |
| title_sort | arma based segmentation of human limb motion sequences |
| topic | MoCap IMU ARMA DTW limb motion sequence segmentation ensemble median filtering |
| url | https://www.mdpi.com/1424-8220/21/16/5577 |
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