The aim of this thesis was to make a design proposal of a pattern recognition system. Pattern recognition is the scientific discipline which is concerned about the description and classification (recognition) of patterns (objects). Pattern recognition is an important part of intelligent systems and are used for many applications domains like decision making. Pattern recognition is studied in several fields, like psychology, cognitive science, psychiatry, ethology, traffic flow and computer science. Pattern recognition is typically classified based on the type of learning procedure employed to generate the output value.
Pattern recognition is the study of the way machines can observe the environment, figure out how to differentiate patterns of interest, make sound and sensible decisions concerning the categories of the patterns. Pattern recognition algorithms typically make an attempt to give a sensible answer for all possible inputs and to perform matching of the inputs, considering their statistical variation. The objective in this dissertation (project report) was to create a design proposal of a pattern recognition system. The system needs to be competent to discover falls, but in addition steps of elderly persons with possible walking limitations. Having said that, to be able to assess the step detection of elderly, it was actually mandatory to permit the system detect steps of non-elderly persons without any walking problems as well, enabling recognition performance comparisons between the 2 step pattern types.
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Two wireless sensors were created, making it possible for collection of synchronized signal data from test persons undertaking fall and step movements. The author was also a participant in the fall data collection and he was equipped with a sensor at the hip. The step data collection was done in 2 tests where both sensors, placed at the hip and ankle, were utilized. The individuals in the initial test were non-elderly with no walking disorders, and the participants in the next test were elderly persons with different walking capabilities.
Source: Lulea University of Technology
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