Classifying and Predicting Respiratory Function Based on Gait Analysis

The human walking behaviour can express the physiological information of human body, and gait analysis methods can be used to access the human body condition. In addition, the respiratory parameters from pulmonary spirometer are the standard of accessing the body condition of the subjects. Therefore, we want to show the correlation between gait analysis method and the respiratory parameters. We propose a vision sensor-based gait analysis method without wearing any sensors. Our method proposed features such as D′p, V′p and γυ to prove the correlation by classification and prediction experiment. In our experiment, the subjects are divided into three levels depending on the respiratory index. We run classifying and predicting experiment with the extracted features: V′p and γυ. In the classifying experiment, the accuracy result is 75%. In predicting experiment, the correlations of predicting the forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) are 0.69 and 0.67, respectively. Therefore, there is a correlation between the pulmonary spirometer and our method. The radar system is a tool using impulse to record the moving of the subjects’ chest. Combining the features of radar system with our features improves the classification result from 75 to 81%. In predicting FEV1/FVC, the correlation also improves from 25 to 42%. Therefore, cooperating with radar system improves the correlation

Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture

People detection and tracking using a network of low-cost depth cameras

Automaattinen ihmisten havainnointi on jo laajalti käytetty teknologia, jolla on sovelluksia esimerkiksi kaupan ja turvallisuuden aloilla. Tämän diplomityön tarkoituksena on suunnitella yleiskäyttöinen järjestelmä ihmisten havainnointiin sisätiloissa. Tässä työssä ensin esitetään kirjallisuudesta löytyvät ratkaisut ihmisten havainnointiin, seurantaan ja tunnistamiseen. Painopiste on syvyyskuvaa hyödyntävissä havaitsemismenetelmissä. Lisäksi esittellään kehitetty älykkäiden syvyyskameroiden verkko. Havainnointitarkkuutta kokeillaan neljällä kuvasarjalla, jotka sisältävät yli 20 000 syvyyskuvaa. Tulokset ovat lupaavia ja näyttävät, että yksinkertaiset ja laskennallisesti kevyet ratkaisut sopivat hyvin käytännön sovelluksiin.Automatic people detection is a widely adopted technology that has applications in retail stores, crowd management and surveillance. The goal of this work is to create a general purpose people detection framework. First, studies on people detection, tracking and re-identification are reviewed. The emphasis is on people detection from depth images. Furthermore, an approach based on a network of smart depth cameras is presented. The performance is evaluated with four image sequences, totalling over 20 000 depth images. Experimental results show that simple and lightweight algorithms are very useful in practical applications