Fall Down Detection Under Smart Home System

Medical technology makes an inevitable trend for the elderly population, therefore the intelligent home care is an important direction for science and technology development, in particular, elderly in-home safety management issues become more and more important. In this research, a low of operation algorithm and using the triangular pattern rule are proposed, then can quickly detect fall-down movements of humanoid by the installation of a robot with camera vision at home that will be able to judge the fall-down movements of in-home elderly people in real time. In this paper, it will present a preliminary design and experimental results of fall-down movements from body posture that utilizes image pre-processing and three triangular-mass-central points to extract the characteristics. The result shows that the proposed method would adopt some characteristic value and the accuracy can reach up to 90 % for a single character posture. Furthermore the accuracy can be up to 100 % when a continuous-time sampling criterion and support vector machine (SVM) classifier are used.

[1]  Amit Jain,et al.  Integrating independent components and linear discriminant analysis for gender classification , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[2]  W. Eric L. Grimson,et al.  Gait analysis for recognition and classification , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[3]  Yunhong Wang,et al.  Gender Classification Based on Fusion of Multi-view Gait Sequences , 2007, ACCV.

[4]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[5]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

[6]  W. Xiong,et al.  A Moving Object Detection Algorithm Based on Color Information , 2006 .

[7]  Nikos Paragios,et al.  Motion-based background subtraction using adaptive kernel density estimation , 2004, CVPR 2004.

[8]  Aaron F. Bobick,et al.  Gait recognition using static, activity-specific parameters , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[9]  Hui Gao,et al.  Gender Recognition from Walking Movements using Adaptive Three-Mode PCA , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[10]  James Nga-Kwok Liu,et al.  Gait flow image: A silhouette-based gait representation for human identification , 2011, Pattern Recognit..

[11]  Dong Ming,et al.  Infrared gait recognition based on wavelet transform and support vector machine , 2010, Pattern Recognit..

[12]  Yangsheng Xu,et al.  Abnormal Behavior Detection by Multi-SVM-Based Bayesian Network , 2007, 2007 International Conference on Information Acquisition.

[13]  Haihong Hu,et al.  Frame difference energy image for gait recognition with incomplete silhouettes , 2009, Pattern Recognit. Lett..

[14]  M. Nixon,et al.  Human gait recognition in canonical space using temporal templates , 1999 .

[15]  Robert Bergevin,et al.  Body tracking in human walk from monocular video sequences , 2005, The 2nd Canadian Conference on Computer and Robot Vision (CRV'05).

[16]  J. Cutting,et al.  Recognizing the sex of a walker from a dynamic point-light display , 1977 .

[17]  N. Troje.  Decomposing biological motion: a framework for analysis and synthesis of human gait patterns. , 2002, Journal of vision.

[18]  Jun Zhang,et al.  Detecting abnormal motion of pedestrian in video , 2008, 2008 International Conference on Information and Automation.

[19]  Chia-Feng Juang,et al.  Reinforcement Ant Optimized Fuzzy Controller for Mobile-Robot Wall-Following Control , 2009, IEEE Transactions on Industrial Electronics.

[20]  John K. Tsotsos,et al.  Automatic detection of abnormal gait , 2009, Image Vis. Comput..

[21]  G. Mather,et al.  Gender discrimination in biological motion displays based on dynamic cues , 1994, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[22]  Sudeep Sarkar,et al.  The humanID gait challenge problem: data sets, performance, and analysis , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Pietro Siciliano,et al.  An active vision system for fall detection and posture recognition in elderly healthcare , 2010, 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010).

[24]  Tieniu Tan,et al.  A Study on Gait-Based Gender Classification , 2009, IEEE Transactions on Image Processing.

[25]  Tieniu Tan,et al.  Silhouette Analysis-Based Gait Recognition for Human Identification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Xuelong Li,et al.  Gait Components and Their Application to Gender Recognition , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[27]  Nikos Paragios,et al.  Motion-based background subtraction using adaptive kernel density estimation , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[28]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Terrence J. Sejnowski,et al.  SEXNET: A Neural Network Identifies Sex From Human Faces , 1990, NIPS.

[30]  Peter H. N. de With,et al.  Video-Based Fall Detection in the Home Using Principal Component Analysis , 2008, ACIVS.

[31]  Robert Bergevin,et al.  Towards view-invariant gait modeling: Computing view-normalized body part trajectories , 2009, Pattern Recognit..