Using machine learning to detect pedestrian locomotion from sensor-based data
The integration of low cost micro-electro-mechanical (MEM) sensors into smart phones have made inertial navigation systems possible for ubiquitous use. Many research studies developed algorithms to detect a user's steps, and to calculate a user's stride to know the position displacement of...
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| Format: | text |
| Language: | English |
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Animo Repository
2014
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| Online Access: | https://animorepository.dlsu.edu.ph/etd_masteral/4606 |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | The integration of low cost micro-electro-mechanical (MEM) sensors into smart phones have made inertial navigation systems possible for ubiquitous use. Many research studies developed algorithms to detect a user's steps, and to calculate a user's stride to know the position displacement of the user. Subsequent research have already integrated the phone's heading to map out the user's movement across a physical area. These research, however, have not taken into account negative pedestrian locomotion, wherein the user is moving but is not exhibiting any position displacement. This research aims to solve this problem by collecting positive and negative pedestrian locomotion with data from phone-embedded sensors positioned in the research subject's front pocket. Using these data, a model will be built to classify negative pedestrian locomotion from positive ones. |
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