research-article

Accurate activity recognition in a home setting

Authors:

Tim van Kasteren,

Athanasios Noulas,

Gwenn Englebienne,

Ben KröseAuthors Info & Claims

UbiComp '08: Proceedings of the 10th international conference on Ubiquitous computing

Pages 1 - 9

https://doi.org/10.1145/1409635.1409637

Published: 21 September 2008 Publication History

Abstract

A sensor system capable of automatically recognizing activities would allow many potential ubiquitous applications. In this paper, we present an easy to install sensor network and an accurate but inexpensive annotation method. A recorded dataset consisting of 28 days of sensor data and its annotation is described and made available to the community. Through a number of experiments we show how the hidden Markov model and conditional random fields perform in recognizing activities. We achieve a timeslice accuracy of 95.6% and a class accuracy of 79.4%.

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Cited By

Takeda NLegaspi RNishimura YIkeda KMinamikawa APlötz TChernova S(2024)Sensor event sequence prediction for proactive smart homeJournal of Ambient Intelligence and Smart Environments10.3233/AIS-23042916:3(275-308)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.3233/AIS-230429
Dilekh TBenharzallah SMokeddem AKerdoudi S(2024)Dynamic Context-Aware Recommender System for Home Automation Through Synergistic Unsupervised and Supervised Learning AlgorithmsActa Informatica Pragensia10.18267/j.aip.22813:1(38-61)Online publication date: 15-Apr-2024
https://doi.org/10.18267/j.aip.228
Song XSun HDong YPei YWang S(2024)Deep Learning-Based Wearable Human Activity Recognition: Model and Performance AnalysisProceedings of the 2024 8th International Conference on Control Engineering and Artificial Intelligence10.1145/3640824.3640830(30-36)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3640824.3640830
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Published In

cover image ACM Other conferences

UbiComp '08: Proceedings of the 10th international conference on Ubiquitous computing

September 2008

404 pages

ISBN:9781605581361

DOI:10.1145/1409635

General Chairs:
Hee Yong Youn
Sungkyunkwan University, Korea
,
We-Duke Cho
Ajou University, Korea
,
Program Chairs:
Joe McCarthy,
James Scott,
Woontack Woo

Copyright © 2008 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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Published: 21 September 2008

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UbiComp08

UbiComp08: The 10th International Conference on Ubiquitous Computing

September 21 - 24, 2008

Seoul, Korea

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

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Cited By

Takeda NLegaspi RNishimura YIkeda KMinamikawa APlötz TChernova S(2024)Sensor event sequence prediction for proactive smart homeJournal of Ambient Intelligence and Smart Environments10.3233/AIS-23042916:3(275-308)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.3233/AIS-230429
Dilekh TBenharzallah SMokeddem AKerdoudi S(2024)Dynamic Context-Aware Recommender System for Home Automation Through Synergistic Unsupervised and Supervised Learning AlgorithmsActa Informatica Pragensia10.18267/j.aip.22813:1(38-61)Online publication date: 15-Apr-2024
https://doi.org/10.18267/j.aip.228
Song XSun HDong YPei YWang S(2024)Deep Learning-Based Wearable Human Activity Recognition: Model and Performance AnalysisProceedings of the 2024 8th International Conference on Control Engineering and Artificial Intelligence10.1145/3640824.3640830(30-36)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3640824.3640830
Song YBian YWang XCai Z(2024)Learning from User-driven Events to Generate Automation SequencesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314277:4(1-22)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3631427
Tello ADegeler VLazovik A(2024)Too Good To Be True: accuracy overestimation in (re)current practices for Human Activity Recognition2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops59983.2024.10503465(511-517)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerComWorkshops59983.2024.10503465
Hao HZhao SChen GZhao SGe Y(2024)Segmentation of multi-residential activities based on spatial correlation and FWC-CPD2024 International Conference on Networking and Network Applications (NaNA)10.1109/NaNA63151.2024.00080(444-452)Online publication date: 9-Aug-2024
https://doi.org/10.1109/NaNA63151.2024.00080
Tanaka KKudo MKimura K(2024)Sensor Data Simulation for Anomaly Detection of the Elderly Living AloneIEEE Internet of Things Journal10.1109/JIOT.2024.342154811:19(31675-31686)Online publication date: 1-Oct-2024
https://doi.org/10.1109/JIOT.2024.3421548
Graule MIsler V(2024)GG-LLM: Geometrically Grounding Large Language Models for Zero-shot Human Activity Forecasting in Human-Aware Task Planning2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10611090(568-574)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10611090
C D MRavi VM PA YM MA S(2024)Human behavioural identification in different aspects using neural network2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT)10.1109/ICCPCT61902.2024.10672662(570-575)Online publication date: 8-Aug-2024
https://doi.org/10.1109/ICCPCT61902.2024.10672662
Fährmann DMartín LSánchez LDamer N(2024)Anomaly Detection in Smart Environments: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2024.339505112(64006-64049)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3395051
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