Paper Info
Title
A New Frontier for Activity Recognition: The Sussex-Huawei Locomotion Challenge.
Abstract
The Sussex-Huawei Locomotion-Transportation recognition challenge presents a unique opportunity to the activity-recognition community - providing a large, real-life dataset with activities different from those typically being recognized. This paper describes our submission (team JSI Classic) to the competition that was organized by the dataset authors. We used a carefully executed machine learning approach, achieving 90% accuracy classifying eight different activities (Still, Walk, Run, Bike, Car, Bus, Train, Subway). The first step was data preprocessing, including a normalization of the phone orientation. Then, a wide set of hand-crafted domain features in both frequency and time domain were computed and their quality was evaluated. Finally, the appropriate machine learning model was chosen (XGBoost) and its hyper-parameters were optimized. The recognition result for the testing dataset will be presented in the summary paper of the challenge [13].
Year
DOI
Venue
2018
10.1145/3267305.3267518
UbiComp '18: The 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing Singapore Singapore October, 2018
Keywords
Field
DocType
Activity recognition, machine learning, feature extraction, XGBoost, competition
Time domain,Activity recognition,Normalization (statistics),Computer science,Data pre-processing,Feature extraction,Human–computer interaction,Phone,Artificial intelligence,Frontier,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-5966-5
1
0.39
References 
Authors
5
10
Name
Order
Citations
PageRank
Vito Janko1158.34
Nina Resçiç232.82
Miha Mlakar3203.30
Vid Drobnic431.13
Matjaz Gams553680.90
Gasper Slapnicar6113.46
Martin Gjoreski7328.08
Jani Bizjak873.44
Matej Marinko941.54
Mitja Luštrek1041054.52