Artificial Intelligence in Kinematics Analysis

NCT05443893 | Unknown

• 1 other identifier: M2021231
• Study Type: observational
• Target: 30
• Locations: 0 countries
• Sites: N/A

Brief Summary

1. 1.Establish data sets. The private data set includes relevant parameters including video of the subject's gait and standard methods for kinematic analysis;
2. 2.Develop new models. Based on public and private data sets, the kinematic analysis model of human key point detection is further developed.
3. 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

Conditions

Gait

Outcome Measures

Primary Outcomes (1)

• Gait related parameters

  Step frequency/pace/gait cycle/step length

  30mins

Study Arms (2)

Normal subjects

Gait analysis with artificial intelligence and traditional methods

Device: Application Research of key points detection technology

Subjects with abnormal gait

Gait analysis with artificial intelligence and traditional methods

Device: Application Research of key points detection technology

Interventions

Application Research of key points detection technology DEVICE

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

Normal subjects; Subjects with abnormal gait

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Normal gait subjects and abnormal gait subjects

You may qualify if:

• \. Abnormal gait.
• Can walk 6m or more independently.
• Older than 18.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Peking University Third Hospital: lead

Central Study Contacts

Mouwang Zhou

CONTACT

13910092892; zhoumouwang@outlook.com

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: June 3, 2022
• First Posted: July 5, 2022
• Study Start: July 10, 2022
• Primary Completion: July 29, 2022
• Study Completion: August 30, 2022
• Last Updated: July 5, 2022

Record last verified: 2022-07