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3D Human Pose Recognition for Home Monitoring of Elderly Bart Jansen, Frederik Temmermans and Rudi Deklerck Proceedings of the 29th Annual International Conference of the IEEE EMBS Cité Internationale, Lyon, France August 23-26, 2007. Presenter: Yi-Shing Chen Professor: Dr. Yen-Ting Chen Date: 2009/12/22 1 Outline Introduction Method ● 3D Camera ● Pose recognition algorithm Experiment and Results Discussion and Applications Ethical Issues Conclusion Reference 2 Introduction The old population has increased dramatically. The growing need for beds in nursing homes causes governments to stimulate elderly to live longer in their natural home environment. 3 Introduction Falls are the most common cause. Average 33% of the seniors will fall in a year. Falls also result in a decrease of life quality. 4 Introduction The elderly become so afraid of falling that they limit their activities. Falls will remain an important cause of the loss of independence of elderly. 5 Introduction Many technical aids have been developed in the prevention and detection of falls. A fall detector is a small portable device. Detection results are typically very good, on condition that the device is worn correctly by the elderly. 6 Introduction 3D camera is used for performing visual fall detection, this approach is new. A framework for the monitoring of elderly is introduced. ► Standing ► Sitting ► Lying down 7 Method-3D Camera 3D Camera The camera is an active device, emitting modulated infrared light. Depth It’s information is provided. based on the time-of-flight (TOF) principle. Time-of-Flight :時差測距 或又稱飛時測距 8 Method-3D Camera Time-of-Flight (TOF) 3D laser scanner is an active scanner. The rangefinder finds the distance of a surface by timing the round-trip time. 9 [引用:維基百科http://zh.wikipedia.org/zh-tw/%E4%B8%89%E7%B6%AD%E6%8E%83%E6%8F%8F%E5%84%80] Method-3D Camera The depth information is readily available, without heavy calculations. 3D cameras do not provide inaccurate depth information in regions with poor texture information. The image resolution is rather low. Once 3D cameras will become more widely used, the price is expected to drop significantly. 10 Method-Pose recognition algorithm The pose recognition algorithm consists of three steps. First step The human silhouette is extracted form the gray level image by subtracting the image from the background. The background image is calculated using a running average over many frames. Image process Thresholding Smoothing Erosion Dilation 11 Method-Pose recognition algorithm Second step The center of the silhouette is calculated by fitting an ellipse to the blob . By thresholding on the width/height of the ellipse, blobs not related to human silhouettes can be discarded. 12 Blob identified as person Method-Pose recognition algorithm Third step The 3D position of the silhouette’s center is calculated in a defined coordinate system. Silhouette’s center needs to be transformed into room coordinates. The height above the ground of the silhouette is thresholded. Standing Sitting (z≦75) Lying (z≦ 35) 13 Method-Pose recognition algorithm 近 已知道的高度 利用time of flight算出來的深度 75 cm 35 cm 遠 Experiment and Results The 3D camera was positioned in the corner of a living room. Conditions A male subject of 27 years old Different camera positions Different light conditions Different clothing A week 15 Experiment and Results m Standing 70cm Sitting 35cm Lying frame 16 Experiment and Results The plot shows that lying down, sitting and standing can clearly be distinguished. walking sequences sitting sequences lying sequences 17 Discussion and Applications The monitoring approach proposed here is targeted to the monitoring of elderly. In the future activity analysis framework will be validated in the hospital. The authors will investigate the trajectories defined by the 3D position of the monitored subject’s center. 18 Discussion and Applications This information could let us know a lot way of 3D pose. To overcome the wrong judgment, like bend down. 19 Ethical Issues Right of privacy The important issues are the storage and access of the monitored data. Architecture of two components At the client side, there is the camera, together with a processing unit. At the server side, the monitored data is stored in the electronic patient record. 20 Ethical Issues Solution At the client are running, no images are transmitted from the client to the server. Only the calculated activity information (e.g. the position of the patient) is transmitted. 21 Ethical Issues It’s guaranteed that the captured 3D images are never stored, nor at the client or the server side and that they are never transmitted over the Internet. client server 沒 有 影 像 傳 送 22 Position information data Ethical Issues It is indeed true that patients reveal some aspects of their privacy. It is a choice to reveal information about their behavior for health. They could be able to quit the program at all time. 23 Conclusion This paper presents a simple but reliable 3D pose recognition algorithm applied on images. Author described the reliability of the pose classification into the classes standing, sitting, and lying down. The information used to derive various features which correlate with the well being of the elderly. 24 Reference Bart Jansen, Frederik Temmermans and Rudi Deklerck,3D human pose recognition for home monitoring of elderly, Proceedings of the 29th Annual International, Conference of the IEEE EMBS, Cité Internationale, Lyon, France, August 23-26, 2007. 維基百科-三維掃瞄儀 http://zh.wikipedia.org/zhtw/Wikipedia:%E9%A6%96%E9%A1% B5 25 Thank you for your attention 26