research-article

Automatic Sensor Data Stream Segmentation for Real-time Activity Prediction in Smart Spaces

Authors:

Younghee LeeAuthors Info & Claims

IoT-Sys '15: Proceedings of the 2015 Workshop on IoT challenges in Mobile and Industrial Systems

Pages 13 - 18

https://doi.org/10.1145/2753476.2753484

Published: 18 May 2015 Publication History

Abstract

Recently, human activity recognition and prediction have become important functionalities in ambient-assisted living. Activity inference algorithms detect what task a human undertakes, by analyzing the data stream pattern generated from various Internet of Things (IoT) devices. However, determining how the data stream should be segmented in real-time, referred to as data segmentation, remains as one of the most difficult challenges. In this paper, we propose an automatic data segmentation approach for real-time activity prediction by employing the Jaro-Winkler Distance measurement. Our approach selects a breakpoint of a stream by comparing the Jaro-Winkler distance between the training dataset and the data stream and finding a peak among the variations. The resultant segment also becomes new training data after being tagged; this removes the need to segment the stream data manually for humans. From the experiment based on MIT's smart home dataset collected from a real living environment, our approach shows reasonable performance of 76% accuracy even though the dataset size is relatively diminutive.

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Cited By

Ermshaus ASchäfer PLeser U(2024)Raising the ClaSS of Streaming Time Series SegmentationProceedings of the VLDB Endowment10.14778/3659437.365945017:8(1953-1966)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.14778/3659437.3659450
Cui SZhu TZhang XChen LMao LNing H(2023)Generating Markov Logic Networks Rulebase Based on Probabilistic Latent Semantics AnalysisTsinghua Science and Technology10.26599/TST.2022.901007228:5(952-964)Online publication date: Oct-2023
https://doi.org/10.26599/TST.2022.9010072
Dębski RDreżewski R(2022)Real-time surrogate-assisted preprocessing of streaming sensor dataComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2022.109422219:COnline publication date: 24-Dec-2022
https://dl.acm.org/doi/10.1016/j.comnet.2022.109422
Show More Cited By

Index Terms

Automatic Sensor Data Stream Segmentation for Real-time Activity Prediction in Smart Spaces
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
  2. Machine learning
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

IoT-Sys '15: Proceedings of the 2015 Workshop on IoT challenges in Mobile and Industrial Systems

May 2015

64 pages

ISBN:9781450335027

DOI:10.1145/2753476

Program Chairs:
Simone Cirani
Università degli Studi di Parma, Italy
,
Mischa Dohler
King's College London, United Kingdom
,
Gianluigi Ferrari
Università degli Studi di Parma, Italy
,
Luigi Alfredo Grieco
Politecnico di Bari, Italy
,
Marco Picone
Università degli Studi di Parma, Italy
,
Thomas Watteyne
Inria, France

Copyright © 2015 ACM.

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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SIGOPS: ACM Special Interest Group on Operating Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2015

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ICT R&D program of MSIP/IITP

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MobiSys'15

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SIGMOBILE

MobiSys'15: The 13th Annual International Conference on Mobile Systems, Applications, and Services

May 18, 2015

Florence, Italy

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IoT-Sys '15 Paper Acceptance Rate 9 of 18 submissions, 50%;

Overall Acceptance Rate 9 of 18 submissions, 50%

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Cited By

Ermshaus ASchäfer PLeser U(2024)Raising the ClaSS of Streaming Time Series SegmentationProceedings of the VLDB Endowment10.14778/3659437.365945017:8(1953-1966)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.14778/3659437.3659450
Cui SZhu TZhang XChen LMao LNing H(2023)Generating Markov Logic Networks Rulebase Based on Probabilistic Latent Semantics AnalysisTsinghua Science and Technology10.26599/TST.2022.901007228:5(952-964)Online publication date: Oct-2023
https://doi.org/10.26599/TST.2022.9010072
Dębski RDreżewski R(2022)Real-time surrogate-assisted preprocessing of streaming sensor dataComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2022.109422219:COnline publication date: 24-Dec-2022
https://dl.acm.org/doi/10.1016/j.comnet.2022.109422
Dębski RDreżewski R(2021)Adaptive Segmentation of Streaming Sensor Data on Edge DevicesSensors10.3390/s2120688421:20(6884)Online publication date: 17-Oct-2021
https://doi.org/10.3390/s21206884
Liono JAbdallah ZQin ASalim FSchulzrinne HLi P(2018)Inferring Transportation Mode and Human Activity from Mobile Sensing in Daily LifeProceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3286978.3287006(342-351)Online publication date: 5-Nov-2018
https://dl.acm.org/doi/10.1145/3286978.3287006
Varol CAbdulhadi H(2018)Comparision of String Matching Algorithms on Spam Email Detection2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT)10.1109/IBIGDELFT.2018.8625317(6-11)Online publication date: Dec-2018
https://doi.org/10.1109/IBIGDELFT.2018.8625317
Alobaid ORamaswamy LRasheed K(2018)A Machine Learning Approach for Identifying Soccer Moves Using an Accelerometer Sensor2018 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI46756.2018.00225(1168-1174)Online publication date: Dec-2018
https://doi.org/10.1109/CSCI46756.2018.00225
Cho MKim YLee Y(2016)Contextual Relationship-based Activity Segmentation on an Event Stream in the IoT Environment with Multi-user ActivitiesProceedings of the 3rd Workshop on Middleware for Context-Aware Applications in the IoT10.1145/3008631.3008633(7-12)Online publication date: 12-Dec-2016
https://dl.acm.org/doi/10.1145/3008631.3008633
Ghasemi VPouyan A(2016)Human activity recognition in ambient assisted living environments using a convex optimization problem2016 2nd International Conference of Signal Processing and Intelligent Systems (ICSPIS)10.1109/ICSPIS.2016.7869899(1-6)Online publication date: Dec-2016
https://doi.org/10.1109/ICSPIS.2016.7869899

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