Study Results
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Basic Information
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UNKNOWN
30 participants
OBSERVATIONAL
2022-07-10
2022-08-30
Brief Summary
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2. Develop new models. Based on public and private data sets, the kinematic analysis model of human key point detection is further developed.
3. Test the new model. By comparing the parameters with the standard method, the accuracy of the model was verified, and the kinematics analysis model of artificial intelligence with accuracy above 98% was obtained
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Detailed Description
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Conditions
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Study Design
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CASE_CONTROL
CROSS_SECTIONAL
Study Groups
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Normal subjects
Gait analysis with artificial intelligence and traditional methods
Application Research of key points detection technology
Artificial intelligence human key point detection model mainly has traditional algorithm, "top-down" algorithm and "bottom-up" algorithm three methods, three methods have advantages. This project will comprehensively use the above three methods to conduct algorithm and parameter debugging in the public data set and test in the private data set, so as to obtain the most suitable human key point recognition method for gait analysis
Subjects with abnormal gait
Gait analysis with artificial intelligence and traditional methods
Application Research of key points detection technology
Artificial intelligence human key point detection model mainly has traditional algorithm, "top-down" algorithm and "bottom-up" algorithm three methods, three methods have advantages. This project will comprehensively use the above three methods to conduct algorithm and parameter debugging in the public data set and test in the private data set, so as to obtain the most suitable human key point recognition method for gait analysis
Interventions
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Application Research of key points detection technology
Artificial intelligence human key point detection model mainly has traditional algorithm, "top-down" algorithm and "bottom-up" algorithm three methods, three methods have advantages. This project will comprehensively use the above three methods to conduct algorithm and parameter debugging in the public data set and test in the private data set, so as to obtain the most suitable human key point recognition method for gait analysis
Eligibility Criteria
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Inclusion Criteria
* Can walk 6m or more independently.
* Older than 18.
Exclusion Criteria
* The mental and psychological state cannot cooperate with the completion of the experiment.
* High risk of falls (Berg score ≤20）
* Gait kinematics analysis equipment cannot be used together.
18 Years
75 Years
ALL
Yes
Sponsors
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Peking University Third Hospital
OTHER
Responsible Party
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Zhou Mouwang
Professor
Central Contacts
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Other Identifiers
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M2021231
Identifier Type: -
Identifier Source: org_study_id
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