NCT05443893Unknown不适用
Application Research of Key Points Detection Technology of Artificial Intelligence in Kinematics Analysis
适应症
试验速览
- 阶段
- 不适用
- 入组人数
- 30
- 主要终点
- Gait related parameters
研究概览
简要总结
- 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
详细描述
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
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 75 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Abnormal gait.
- •Can walk 6m or more independently.
- •Older than 18.
排除标准
- •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.
结局指标
主要结局
Gait related parameters
时间窗: 30mins
Step frequency/pace/gait cycle/step length
次要结局
未报告次要终点
研究者
Zhou Mouwang
Professor
Peking University Third Hospital
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