draft_007_HASCA_submission.pdf (586.27 kB)
Smartphone location identification and transport mode recognition using an ensemble of generative adversarial networks
conference contribution
posted on 2023-06-09, 21:53 authored by Lukas Kornelius GunthermannLukas Kornelius Gunthermann, Ivor SimpsonIvor Simpson, Daniel RoggenDaniel RoggenWe present a generative adversarial network (GAN) approach to recognising modes of transportation from smartphone motion sensor data, as part of our contribution to the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge 2020 as team noname. Our approach identifies the location where the smartphone of the test dataset is carried on the body through heuristics, after which a location-specific model is trained based on the available published data at this location. Performance on the validation data is 0.95, which we expect to be very similar on the test set, if our estimation of the location of the phone on the test set is correct. We are highly confident in this location estimation. If however it were wrong, an accuracy as low as 30% could be expected.
History
Publication status
- Published
File Version
- Accepted version
Journal
Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable ComputersPublisher
ACMExternal DOI
Page range
311-316Event name
UbiComp/ISWC '20Event location
Virtual Event MexicoEvent type
conferenceEvent date
September, 2020Place of publication
NY, United StatesISBN
9781450380768Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2020-10-14First Open Access (FOA) Date
2021-02-19First Compliant Deposit (FCD) Date
2020-10-14Usage metrics
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