Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture
To obtain accurate position information, herein, a one-assistant method involving the fusion of extreme learning machine (ELM)/finite impulse response (FIR) filters and vision data is proposed for inertial navigation system (INS)-based human motion capture. In the proposed method, when vision is ava...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
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| Series: | Micromachines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-666X/14/11/2088 |
| _version_ | 1797458397007708160 |
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| author | Yuan Xu Rui Gao Ahong Yang Kun Liang Zhongwei Shi Mingxu Sun Tao Shen |
| author_facet | Yuan Xu Rui Gao Ahong Yang Kun Liang Zhongwei Shi Mingxu Sun Tao Shen |
| author_sort | Yuan Xu |
| collection | DOAJ |
| description | To obtain accurate position information, herein, a one-assistant method involving the fusion of extreme learning machine (ELM)/finite impulse response (FIR) filters and vision data is proposed for inertial navigation system (INS)-based human motion capture. In the proposed method, when vision is available, the vision-based human position is considered as input to an FIR filter that accurately outputs the human position. Meanwhile, another FIR filter outputs the human position using INS data. ELM is used to build mapping between the output of the FIR filter and the corresponding error. When vision data are unavailable, FIR is used to provide the human posture and ELM is used to provide its estimation error built in the abovementioned stage. In the right-arm elbow, the proposed method can improve the cumulative distribution functions (CDFs) of the position errors by about 12.71%, which shows the effectiveness of the proposed method. |
| first_indexed | 2024-03-09T16:36:31Z |
| format | Article |
| id | doaj.art-ba5be2d2935b4add9c5916dfd4d54194 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-09T16:36:31Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-ba5be2d2935b4add9c5916dfd4d541942023-11-24T14:56:31ZengMDPI AGMicromachines2072-666X2023-11-011411208810.3390/mi14112088Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion CaptureYuan Xu0Rui Gao1Ahong Yang2Kun Liang3Zhongwei Shi4Mingxu Sun5Tao Shen6School of Electrical Engineering, University of Jinan, Jinan 250022, ChinaSchool of Electrical Engineering, University of Jinan, Jinan 250022, ChinaSchool of Music, University of Jinan, Jinan 250022, ChinaSchool of Electrical Engineering, University of Jinan, Jinan 250022, ChinaSchool of Electrical Engineering, University of Jinan, Jinan 250022, ChinaSchool of Electrical Engineering, University of Jinan, Jinan 250022, ChinaSchool of Electrical Engineering, University of Jinan, Jinan 250022, ChinaTo obtain accurate position information, herein, a one-assistant method involving the fusion of extreme learning machine (ELM)/finite impulse response (FIR) filters and vision data is proposed for inertial navigation system (INS)-based human motion capture. In the proposed method, when vision is available, the vision-based human position is considered as input to an FIR filter that accurately outputs the human position. Meanwhile, another FIR filter outputs the human position using INS data. ELM is used to build mapping between the output of the FIR filter and the corresponding error. When vision data are unavailable, FIR is used to provide the human posture and ELM is used to provide its estimation error built in the abovementioned stage. In the right-arm elbow, the proposed method can improve the cumulative distribution functions (CDFs) of the position errors by about 12.71%, which shows the effectiveness of the proposed method.https://www.mdpi.com/2072-666X/14/11/2088INSvisionELMFIRhuman position |
| spellingShingle | Yuan Xu Rui Gao Ahong Yang Kun Liang Zhongwei Shi Mingxu Sun Tao Shen Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture Micromachines INS vision ELM FIR human position |
| title | Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture |
| title_full | Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture |
| title_fullStr | Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture |
| title_full_unstemmed | Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture |
| title_short | Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture |
| title_sort | extreme learning machine finite impulse response filter and vision data assisted inertial navigation system based human motion capture |
| topic | INS vision ELM FIR human position |
| url | https://www.mdpi.com/2072-666X/14/11/2088 |
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