Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The 2021
DOI: 10.1145/3460418.3479383
|View full text |Cite
|
Sign up to set email alerts
|

An Ensemble of ConvTransformer Networks for the Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge

Abstract: The task of the SHL recognition challenge 2021 is to recognize eight modes of locomotion-transportation based on radio sequential data collected by smartphones. These data includes GPS reception, GPS location, WiFi reception and GSM cell tower scans. In this challenge, our team (Transformers) presents a recognition scheme. First, a deep model (ConvTransformer) composed of convolutional and Transformer subnets is proposed. The convolutional subnet captures local features, and the Transformer subnet constructs l… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
2
2

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(1 citation statement)
references
References 12 publications
0
1
0
Order By: Relevance
“…Ref. [38] distinguished between users standing still, walking, running, cycling, driving a car, or riding a bus, subway, or train to yield a best F1-score of 0.8779. The use of GPS sensors in [36][37][38] provides less specific displacement sensitivity than the gyroscope and accelerometer used in our study, a difference that can at least partly explain our higher accuracy of 98%.…”
Section: Vehicle Recognition Via Smart Sensorsmentioning
confidence: 99%
“…Ref. [38] distinguished between users standing still, walking, running, cycling, driving a car, or riding a bus, subway, or train to yield a best F1-score of 0.8779. The use of GPS sensors in [36][37][38] provides less specific displacement sensitivity than the gyroscope and accelerometer used in our study, a difference that can at least partly explain our higher accuracy of 98%.…”
Section: Vehicle Recognition Via Smart Sensorsmentioning
confidence: 99%