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Human Activity Recognition and Behavioral Prediction using Wearable Sensors and Deep Learning
Linköping University, Department of Mathematics. Linköping University, Faculty of Science & Engineering.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

When moving into a more connected world together with machines, a mutual understanding will be very important. With the increased availability in wear- able sensors, a better understanding of human needs is suggested. The Dart- mouth Research study at the Psychiatric Research Center has examined the viability of detecting and further on predicting human behaviour and complex tasks. The field of smoking detection was challenged by using the Q-sensor by Affectiva as a prototype. Further more, this study implemented a framework for future research on the basis for developing a low cost, connected, device with Thayer Engineering School at Dartmouth College. With 3 days of data from 10 subjects smoking sessions was detected with just under 90% accuracy using the Conditional Random Field algorithm. However, predicting smoking with Electrodermal Momentary Assessment (EMA) remains an unanswered ques- tion. Hopefully a tool has been provided as a platform for better understanding of habits and behaviour. 

Place, publisher, year, edition, pages
2017. , p. 52
Keywords [en]
Wearable sensors, Machine Learning, Deep Learning, Long Short Term Memory, Conditional Random Fields, Computational Psychiatry
National Category
Medical Engineering Mathematical Analysis
Identifiers
URN: urn:nbn:se:liu:diva-138064ISRN: LiTH-MAT-EX--2017/04--SEOAI: oai:DiVA.org:liu-138064DiVA, id: diva2:1177287
External cooperation
Dartmouth Psychiatric Research Center, Geisel School of Medicine
Subject / course
Applied Mathematics
Supervisors
Examiners
Available from: 2018-02-01 Created: 2018-01-24 Last updated: 2018-02-01Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • 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
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