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A Post-processing Machine Learning for Activity Recognition Challenge with OpenStreetMap Data

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Xiaoling LuAuthors Info & Claims

UbiComp/ISWC '23 Adjunct: Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing

Pages 557 - 562

https://doi.org/10.1145/3594739.3610755

Published: 08 October 2023 Publication History

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Abstract

This paper aims to address the Sussex-Huawei Locomotion - Transportation (SHL) recognition challenge organized at the HASCA Workshop of UbiComp 2023. The challenge focuses on achieving user-independent recognition of eight different modes of activities using motion and GPS sensor data[3, 9]. Our team, named DataScience SHL Team, proposes a pipeline, which involves extracting features including time domain, motion position, road map, and differential features, utilizing the OpenStreetMap platform as a additional resource. We carefully select the Random Forest model as our classification model. Additionally, a post-processing approach is introduced to modify labels. Since the test data partition lacks identification, we have aggregated models trained at four sites, enhancing the overall performance and robustness. The proposed pipeline has achieved an accuracy of 84.38% and an f1-score of 57.49% for hand data during the validation phase, demonstrating a significant improvement in motion state recognition.

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Wang LGjoreski HCiliberto MLago PMurao KOkita TRoggen D(2023)Summary of SHL Challenge 2023: Recognizing Locomotion and Transportation Mode from GPS and Motion SensorsAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610758(575-585)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610758

Index Terms

A Post-processing Machine Learning for Activity Recognition Challenge with OpenStreetMap Data
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods
        Bagging
    2. Machine learning approaches
      1. Rule learning
2. Human-centered computing
  1. Ubiquitous and mobile computing

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Published In

UbiComp/ISWC '23 Adjunct: Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing

October 2023

822 pages

ISBN:9798400702006

DOI:10.1145/3594739

Editors:
Monica Tentori
(CICESE, Mexico)
,
Nadir Weibel
(UC San Diego, USA)
,
Kristof Van Laerhoven
(University of Siegen, Germany)
,
Zhongyi Zhou
(University of Tokyo, Japan)

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

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Published: 08 October 2023

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National Natural Science Foundation of China

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UbiComp/ISWC '23

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UbiComp/ISWC '23: The 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing

October 8 - 12, 2023

Cancun, Quintana Roo, Mexico

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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UBICOMP '24

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The 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing

October 5 - 9, 2024

Melbourne , VIC , Australia

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Wang LGjoreski HCiliberto MLago PMurao KOkita TRoggen D(2023)Summary of SHL Challenge 2023: Recognizing Locomotion and Transportation Mode from GPS and Motion SensorsAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610758(575-585)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610758

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Short Segment Random Forest with Post Processing Using Label Constraint for SHL Recognition Challenge

Smartphone-sensors Based Activity Recognition Using IndRNN

Activity Recognition by Classification with Time Stabilization for the SHL Recognition Challenge

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