Artificial Intelligence in Kinematics Analysis

Application Research of Key Points Detection Technology of Artificial Intelligence in Kinematics Analysis

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. 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

Study Overview

• Status: Not yet recruiting
• Conditions: Gait
• Intervention / Treatment: Device: Application Research of key points detection technology

Detailed Description

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

• Study Type: Observational
• Enrollment (Anticipated): 30

Contacts and Locations

• Study Contact: Name: Mouwang Zhou; Phone Number: 13910092892; Email: zhoumouwang@outlook.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 75 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Normal gait subjects and abnormal gait subjects

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

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Normal subjects; Gait analysis with artificial intelligence and traditional methods	Device: 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	Device: 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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Gait related parameters	Step frequency/pace/gait cycle/step length	30mins

Collaborators and Investigators

• Sponsor: Peking University Third Hospital

Study record dates

Study Major Dates

• Study Start (Anticipated): July 10, 2022
• Primary Completion (Anticipated): July 29, 2022
• Study Completion (Anticipated): August 30, 2022

Study Registration Dates

• First Submitted: June 3, 2022
• First Submitted That Met QC Criteria: July 1, 2022
• First Posted (Actual): July 5, 2022

Study Record Updates

• Last Update Posted (Actual): July 5, 2022
• Last Update Submitted That Met QC Criteria: July 1, 2022
• Last Verified: July 1, 2022

More Information

Terms related to this study

• Other Study ID Numbers: M2021231