Using machine learning to detect pedestrian locomotion from sensor-based data
College of Computer Studies
Proceedings of the 14th Philippine Computing Science Congress (PCSC 2014)
The integration of low cost microelectromagnetic (MEM) sensors into smart phones have made inertial navigation systems (INS) 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. Current INSs are not suited to handle negative pedestrian locomotion movements, and this leads them to consider false steps as real steps. As the INS's modules depend heavily on the outputs of the other modules, a cascading error would most likely occur. 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, and to eventually improve the INS's accuracy overall.
Ngo, C. M., See, S., & Legaspi, R. (2014). Using machine learning to detect pedestrian locomotion from sensor-based data. Proceedings of the 14th Philippine Computing Science Congress (PCSC 2014), 219-226. Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/8418
Databases and Information Systems
Inertial navigation systems; Motion; Spatial data mining