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Improvement of Data Mining Methods on Falling Detection and Daily Activities Recognition
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems. (Data Mining)
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

With the growing phenomenon of an aging population, an increasing numberof older people are living alone for domestic and social reasons. Based on thisfact, falling accidents become one of the most important factors in threateningthe lives of the elderly. Therefore, it is necessary to set up an application to de-tect the daily activities of the elderly. However, falling detection is difficult to recognize because the "falling" motion is an instantaneous motion and easy to confuse with others.In this thesis, three data mining methods were employed on wearable sensors' value; first which contains the continuous data set concerning eleven activities of daily living, and then an analysis of the different results was performed. Not only could the fall be detected, but other activities could also be classified. In detail, three methods including Back Propagation Neural Network, Support Vector Machine and Hidden Markov Model are applied separately to train the data set.What highlights the project is that a new  idea is put forward, the aim of which is to design a methodology of accurate classification in the time-series data set. The proposed approach, which includes obtaining of classifier parts and the application parts allows the generalization of classification. The preliminary results indicate that the new method achieves the high accuracy of classification,and significantly performs better than other data mining methods in this experiment.

Place, publisher, year, edition, pages
2015.
Keywords [en]
a ctivities of daily living, wearable sensor, neural network, Support Vector Machine, Hidden Markov Model
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:miun:diva-25521OAI: oai:DiVA.org:miun-25521DiVA, id: diva2:841584
Subject / course
Computer Engineering DT1
Presentation
2015-06-08, M312, M Building, Sundsvall, 13:00 (English)
Examiners
Available from: 2015-07-14 Created: 2015-07-14 Last updated: 2018-01-11Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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