Paper Info
Title
Summary of the Sussex-Huawei locomotion-transportation recognition challenge 2019
Abstract
In this paper we summarize the contributions of participants to the Sussex-Huawei Transportation-Locomotion (SHL) Recognition Challenge organized at the HASCA Workshop of Ubi-Comp 2019. The goal of this machine learning/data science challenge is to recognize eight locomotion and transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the inertial sensor data of a smartphone in a placement independent manner. The training data is collected with smartphones placed at three body positions (Torso, Bag and Hips), while the testing data is collected with a smartphone placed at another body position (Hand). We introduce the dataset used in the challenge and the protocol for the competition. We present a meta-analysis of the contributions from 14 submissions, their approaches, the software tools used, computational cost and the achieved results. Overall, three submissions achieved F1 scores between 70% and 80% five with F1 scores between 60% and 70%, five between between 50% and 60%, and one below 50%, with a latency of a maximum of 5 seconds.
Year
DOI
Venue
2019
10.1145/3341162.3344872
Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
Keywords
Field
DocType
activity recognition, deep learning, machine learning, mobile sensing, transportation mode recognition
Computer science,Human–computer interaction,Embedded system
Conference
ISBN
Citations 
PageRank 
978-4503-6869-8
1
0.36
References 
Authors
0
7
Name
Order
Citations
PageRank
Lin Wang117715.50
Hristijan Gjoreski226829.81
Mathias Ciliberto3336.12
Paula Lago434.76
Kazuya Murao513131.38
Tsuyoshi Okita65313.37
Daniel Roggen71851137.05