CAJ | 학술논문

对人体体育运动训练过程中的受力行为进行分解建模，可以定量分析人体受力特征，为实现多传感器信息融合的运动受力行为识别奠定基础。传统方法中采用熵聚类泛函的人体运动神经元受力行为分析方法，无法实现对复杂运动状态下的人体受力缺失数据的模拟和分析。提出一种基于RFID缺失数据概率泛函的人体运动受力行为分解，将加速度传感器与RFID结合，采用缺失数据概率泛函算法对感知的运动关节节点进行受力行为分解，提取了不同传感器节点输出信号关联特征，实现对人体运动特征行为分解算法和模型的改进。实验结果表明，该模型能准确采集到人体的上体和下肢的运动受力数据，受力行为分解准确具体，定量刻画人体在各种运动模型下的受力特性，从而指导运动训练，应用价值较高。
대인체체육운동훈련과정중적수역행위진행분해건모，가이정량분석인체수력특정，위실현다전감기신식융합적운동수역행위식별전정기출。전통방법중채용적취류범함적인체운동신경원수역행위분석방법，무법실현대복잡운동상태하적인체수력결실수거적모의화분석。제출일충기우RFID결실수거개솔범함적인체운동수역행위분해，장가속도전감기여RFID결합，채용결실수거개솔범함산법대감지적운동관절절점진행수역행위분해，제취료불동전감기절점수출신호관련특정，실현대인체운동특정행위분해산법화모형적개진。실험결과표명，해모형능준학채집도인체적상체화하지적운동수력수거，수역행위분해준학구체，정량각화인체재각충운동모형하적수력특성，종이지도운동훈련，응용개치교고。
In the process human sports training, the human motion force decomposition modeling is used for analyzing the characteristics of split force in quantitative, and it can provides the base of human force action recognition with multi sensor information fusion. In the traditional method, the entropy clustering functional analysis method is used for human motor neuron stress behavior analysis, but it cannot achieve the simulation and analysis of missing force data in complex motion state. An improved human motion force behavior decomposition method is proposed based on RFID missing data probabili?ty functional. The acceleration sensor is combined with RFID. The missing data probability functional algorithm is used for perception of the movement joint node stress behavior decomposition, and the different sensor output signal correlation characteristics are extracted. The improved decomposition algorithm and the model of human body motion characteristic be?havior is completed. The experimental results show that the model can accurately capture to the exercise stress data of body with upper body and lower limbs. Force behavior decomposition is accurate and concrete. The quantitative description of stress characteristics of human body in motion model is obtained, so as to guide the movement training, so it has good appli?cation value.