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- Fall detection systems to be used by elderly peoplePublication . Igrejas, Getúlio; Amaral, Joana S.; Rodrigues, Pedro JoãoStatistics show that, each year, falls affect tens of millions of elderly people throughout the world. Falls are the leading cause of injury deaths and injury-related hospitalization among people over 65 years old. A system able to automatically detect falls could be an important tool from a social point of view, as it would contribute to the prompt assistance of these emergency cases. Currently, many researchers are interested in the development of fall detection systems. This chapter presents several approaches suggested so far, with special attention to the different strategies and technologies applied. Other sen- sor technologies that could be applied to this field are also referred. Additionally, a new fall detection system based on a machine learning paradigm, using neural networks, is suggested. The system was trained using fall and non-fall examples. Although the test set has included some particular examples, problematic for detection of the correspondent motion, the obtained results present good specificity (88.9%) and sensitivity (93.9%) rates.
- A segmentation approach for the identification of hazelnut cultivarsPublication . Rodrigues, Pedro João; Amaral, Joana S.; Santos, Alberto; Igrejas, GetúlioThe development of a methodology for cultivar discrimination and selection during on-line quality selection could be a relevant tool for certain industries that use hazelnuts as ingredients in processed food products. The aim of this work was to obtain a computer vision system that enables an automatic recognition of six hazelnuts cultivars from 3 different origins (US, Italy and Spain). To achieve this objective, an algorithm for tuning the segmentation between the hazelnut shell and crown was developed. Important morphological features that allow the correct identification of the cultivar present in an image were obtained from the segmentation process. Finally, these features were used as input data to a neural network classifier. The obtained results showed a high percentage of correct cultivar classification.