Gait Recognition and Assistance Parameter Prediction Determination Based on Kinematic Information Measured by Inertial Measurement Units
The gait recognition of exoskeletons includes motion recognition and gait phase recognition under various road conditions. The recognition of gait phase is a prerequisite for predicting exoskeleton assistance time. The estimation of real-time assistance time is crucial for the safety and accurate control of lower-limb exoskeletons. To solve the problem of predicting exoskeleton assistance time, this paper proposes a gait recognition model based on inertial measurement units that combines the real-time motion state recognition of support vector machines and phase recognition of long short-term memory networks. A recognition validation experiment was conducted on 30 subjects to determine the reliability of the gait recognition model. The results showed that the accuracy of motion state and gait phase were 99.98% and 98.26%, respectively. Based on the proposed SVM-LSTM gait model, exoskeleton assistance time was predicted. A test was conducted on 10 subjects, and the results showed that using assistive therapy based on exercise status and gait stage can significantly improve gait movement and reduce metabolic costs by an average of more than 10%.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
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| Enthalten in: |
Bioengineering (Basel, Switzerland) - 11(2024), 3 vom: 13. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xiang, Qian [VerfasserIn] |
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| Links: |
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| Themen: |
Assistance parameter planning |
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| Anmerkungen: |
Date Revised 29.03.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3390/bioengineering11030275 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370241045 |
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| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a The gait recognition of exoskeletons includes motion recognition and gait phase recognition under various road conditions. The recognition of gait phase is a prerequisite for predicting exoskeleton assistance time. The estimation of real-time assistance time is crucial for the safety and accurate control of lower-limb exoskeletons. To solve the problem of predicting exoskeleton assistance time, this paper proposes a gait recognition model based on inertial measurement units that combines the real-time motion state recognition of support vector machines and phase recognition of long short-term memory networks. A recognition validation experiment was conducted on 30 subjects to determine the reliability of the gait recognition model. The results showed that the accuracy of motion state and gait phase were 99.98% and 98.26%, respectively. Based on the proposed SVM-LSTM gait model, exoskeleton assistance time was predicted. A test was conducted on 10 subjects, and the results showed that using assistive therapy based on exercise status and gait stage can significantly improve gait movement and reduce metabolic costs by an average of more than 10% | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a assistance parameter planning | |
| 650 | 4 | |a gait recognition | |
| 650 | 4 | |a long short-term memory (LSTM) | |
| 650 | 4 | |a soft lower-limb exoskeleton | |
| 650 | 4 | |a support vector machine (SVM) | |
| 700 | 1 | |a Wang, Jiaxin |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yong |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Shijie |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Lei |e verfasserin |4 aut | |
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