No abstract

In this study, a system is developed to measure human chest wall motion for respiratory volume estimation without any physical contact. Based on depth image sensing technique, respiratory volume is estimated by measuring morphological changes of the chest wall. We evaluated the system and compared with a standard reference device, and the results show strong agreement in respiratory volume measurement [correlation coefficient: r=0.966]. The isovolume test presents small variations of the total respiratory volume during the isovolume maneuver (standard deviation<107 ml). Then, a regional pulmonary measurement test is evaluated by a patient, and the results show visibly difference of pulmonary functional between the diseased and the contralateral sides of the thorax after the thoracotomy. This study has big potential for personal health care and preventive medicine as it provides a novel, low-cost, and convenient way to measure user's respiration volume.

Sleep monitoring is increasingly seen as a common and important issue. In this paper, a depth analysis technique was developed to monitor user's sleep conditions without any physical contact. In this research, a cross-section method was proposed to detect user's head and torso from the depth images. Then, the system can monitor user's breathing rate, sleep position, and sleep cycle. In order to evaluate the measurement accuracy of this system, two experiments were conducted. In the first experiment, eight participants with various body shapes were asked to join the experiment. They were asked to change the sleep positions (supine and side-lying) every fifteen breathing cycles in two circumstances (sleep with and without a thin quilt) on the bed. The experimental results showed that the system is promising to detect the head and torso with various sleeping postures. In the second experiment, a realistic overnight sleep monitoring experiment was conducted. The experimental results demonstrated that this system is promising to monitor the sleep conditions in realistic sleep conditions. To conclude, this study is important for providing a non-contact technology to detect multiple sleep conditions and assist users in better understanding of their sleep quality.

No abstract

No abstract