Artificial Intelligence in Kinematics Analysis
Application Research of Key Points Detection Technology of Artificial Intelligence in Kinematics Analysis
- Establish data sets. The private data set includes relevant parameters including video of the subject's gait and standard methods for kinematic analysis;
- Develop new models. Based on public and private data sets, the kinematic analysis model of human key point detection is further developed.
- 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
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Anticipated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Mouwang Zhou
- Phone Number: 13910092892
- Email: zhoumouwang@outlook.com
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- 1. Abnormal gait.
- Can walk 6m or more independently.
- Older than 18.
Exclusion Criteria:
- Fracture may be aggravated by walking in the acute stage or early postoperative stage. Have heart, lung, liver and kidney And other serious diseases, heart function grading greater than GRADE I (NYHA), respiratory failure and other symptoms and signs or Check the results.
- 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.
Study Plan
How is the study designed?
Design Details
- Observational Models: Case-Control
- Time Perspectives: Cross-Sectional
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Normal subjects
Gait analysis with artificial intelligence and traditional methods
|
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
|
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
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Gait related parameters
Time Frame: 30mins
|
Step frequency/pace/gait cycle/step length
|
30mins
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Anticipated)
Study Start
Primary Completion (Anticipated)
Primary Completion
Study Completion (Anticipated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
Other Study ID Numbers
- M2021231
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