research-article

A Hierarchical Classifier for Detecting Metro-Journey Activities in Data Sampled at Low Frequency

Authors:

Venkata M. V. Gunturi,

Kartik Vishwakarma,

Shrehal BohraAuthors Info & Claims

MoMM2019: Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia

Pages 46 - 55

https://doi.org/10.1145/3365921.3365937

Published: 22 February 2020 Publication History

Abstract

This paper aims to build a novel classification model that can distinguish the typical motion activities that a traveler would perform in a metro-journey. Following motion activities are focused in this work: waiting in a queue, traveling in a metro train, climbing-up, climbing-down, walking and stationary. We aim to build a classifier which can work on data sampled from smartphone sensors at a low frequency (4Hz). However, it is non-trivial to do so as the mentioned activities are not easily separable in data sampled at low frequency. Current works focus on data sampled at high frequency (40Hz). Also, they don't consider metro-journey specific activities such as queue. Our proposed model focuses on all the metro-journey specific activities while using data sampled at low frequency (4Hz). Experimental evaluation (datasets collected in Delhi Metro-rail network) indicate superior performance of our classifier (mean accuracy 92%) over the related work (mean accuracy 70%).

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Cited By

Dewan AGunturi VNaik V(2023)Deep Learning Models for NEAT Activity Detection on Smartwatch2023 IEEE Smart World Congress (SWC)10.1109/SWC57546.2023.10449263(1-6)Online publication date: 28-Aug-2023
https://doi.org/10.1109/SWC57546.2023.10449263
Dewan AGunturi VNaik VDutta K(2021)NEAT Activity Detection using Smartwatch at Low Sampling Frequency2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI)10.1109/SWC50871.2021.00014(25-32)Online publication date: Oct-2021
https://doi.org/10.1109/SWC50871.2021.00014

Index Terms

A Hierarchical Classifier for Detecting Metro-Journey Activities in Data Sampled at Low Frequency
1. Computer systems organization
  1. Embedded and cyber-physical systems
2. Human-centered computing
  1. Ubiquitous and mobile computing

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MoMM2019: Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia

December 2019

266 pages

ISBN:9781450371780

DOI:10.1145/3365921

Copyright © 2019 ACM.

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 ACM 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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Johannes Kepler University, Linz, Austria
@WAS: International Organization of Information Integration and Web-based Applications and Services

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Association for Computing Machinery

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Publication History

Published: 22 February 2020

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MoMM2019

MoMM2019: The 17th International Conference on Advances in Mobile Computing & Multimedia

December 2 - 4, 2019

Munich, Germany

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Cited By

Dewan AGunturi VNaik V(2023)Deep Learning Models for NEAT Activity Detection on Smartwatch2023 IEEE Smart World Congress (SWC)10.1109/SWC57546.2023.10449263(1-6)Online publication date: 28-Aug-2023
https://doi.org/10.1109/SWC57546.2023.10449263
Dewan AGunturi VNaik VDutta K(2021)NEAT Activity Detection using Smartwatch at Low Sampling Frequency2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI)10.1109/SWC50871.2021.00014(25-32)Online publication date: Oct-2021
https://doi.org/10.1109/SWC50871.2021.00014

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