Activity prediction using context
Date of Publication
2011
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Computer Science
College
College of Computer Studies
Department/Unit
Computer Science
Thesis Adviser
Rhia S. Trogo
Defense Panel Chair
Ethel Chua Joy Ong
Defense Panel Member
Merlin Teodosia C. Suarez
Gregory G. Cu
Abstract/Summary
As new technologies emerge, it is important to take note of the possible improvements we can make in terms of how humans and computers interact. Construction of an empathic environment that supports and responds to the users state is becoming more of a necessity for intelligent systems. Recognizing and predicting human activities will provide these intelligent systems the means to properly assess the needs of its users. This research make use of previous actions and context containing item used, location, and affect to predict the possible future activities that the user might do. A corpus that includes affect and actions of the user will be used to model the activities of the inhabitant. The model is able to anticipate the succeeding activity of the user. The proponents have tested 4 types of model on 2 different DBN set-ups. The models that were used in testing were Item used only, item used with location, item used with emotion, and item used with location and emotion. With the different combination of attributes tested the proponents have obtain these results. For set-up 1 item used had 32%-56%, item used with location had 33%-56%, item used with emotion had 32%-37%, and item used with location and emotion had 33%-56%. For set-up 2 item used had 33%-61%, item used with location had 34%-56%, item used with emotion had 34%-61%, and item used with location and emotion had 37%-56%. The results show that set-up 2 (refer to Figure 4.5) has a much higher output that set-up 1 (refer to Figure 4.4), this is because of how the set-ups were designed.
Abstract Format
html
Language
English
Format
Accession Number
TU18525
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Physical Description
viii, 102 leaves : illustrations (some colored) ; 28 cm.
Recommended Citation
Co, J., Samson, C., & Uy, J. (2011). Activity prediction using context. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/11823
