Abstract
The human walking behavior could express the physiological information of human body. Consequently, gait analysis methods can be used to access the human body condition. However, many gait analysis methods need to wear sensors to record the walking behavior. We propose a vision sensor based gait analysis method without wearing any sensors. Our method would segment the silhouette in order to extract legs parts. From legs part, we could extract the gait features. The subjects can get the respiratory parameters by using pulmonary spirometer. These parameters become the standard of accessing the body condition of the subjects. If the classification results of our experiment are similar to the classification results by respiratory parameters or the predicting values are close to the respiratory parameters, we can prove there is a correlation between pulmonary spirometer and our method and understand the body condition of the subjects. We film the subjects when they are running the six-minutes brisk walking test at Shuang-Ho hospital in New Taipei, Taiwan and get the respiratory parameter by using pulmonary spirometer. These subjects would be divided into level 1, level 2, and level 3 depending on the respiratory parameters. We would run classification and predicting experiment according to our extracted features. In the classification experiment, the subjects of level 1 and level 2 belong to Bad group and the subjects of level 3 belong to Good group. In the classification experiment, the accuracy result is 75%. In predicting experiment, the correlations of predicting FEV1 and FVC are 0.69 and 0.67. However, the correlation of predicting FEV1/FVC is 0.25. The results have higher correlation on predicting the parameters. As a result, there is a correlation between the pulmonary spirometer and our method. Combining the features of radar system with our features, the classification result would improve to 81% form 75 %. In predicting FEV1/FVC, the correlation also improves to 42% from 25%. Therefore, cooperating with radar system would improve the correlation of pulmonary spirometer and our method.