| Summary: | In recent years, the increase in the amount of elderly people has gained importance and significance and has become one of the major social challenges for most developed countries. More than one third of elderly fall at least once a year and often are not able to get up again unsupported, especially if they live alone. Smart homes can provide efficient and cost effective solutions, using technologies in order to sense the environment and helping to understand the occurrence of a possible dangerous situation. Robotic assistance is one of the most promising technologies for recognizing a fallen person and helping him/her in case of danger. This dissertation presents two methods, to detect first and then to recognize the presence or non-presence of a human being on the ground. The first method is based on Kinect depth image, thresholding and blob analysis for detecting human presence. While, the second is a GLCM feature-based method, evaluated from two different classifiers, namely Support Vector Machine (SVM) and Artificial Neural Network (ANN) for recognizing human from non-human. Results show that SVM and ANN can classify the presence of a person with 76.5 and 85.6 of accuracy, respectively. This shows that these methods can potentially be used to recognize the presence or absence of fallen human lying on the floor.
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