Literature DB >> 35903759

Real-Time Change Point Detection with application to Smart Home Time Series Data.

Samaneh Aminikhanghahi1, Tinghui Wang1, Diane J Cook1.   

Abstract

Change Point Detection (CPD) is the problem of discovering time points at which the behavior of a time series changes abruptly. In this paper, we present a novel real-time nonparametric change point detection algorithm called SEP, which uses Separation distance as a divergence measure to detect change points in high-dimensional time series. Through experiments on artificial and real-world datasets, we demonstrate the usefulness of the proposed method in comparison with existing methods.

Entities:  

Keywords:  Activity transition detection; Separation distance; change detection algorithms; smart homes; time series data

Year:  2018        PMID: 35903759      PMCID: PMC9328027          DOI: 10.1109/tkde.2018.2850347

Source DB:  PubMed          Journal:  IEEE Trans Knowl Data Eng        ISSN: 1041-4347            Impact factor:   9.235


  7 in total

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Authors:  Richard J Radke; Srinivas Andra; Omar Al-Kofahi; Badrinath Roysam
Journal:  IEEE Trans Image Process       Date:  2005-03       Impact factor: 10.856

Review 2.  The Elderly's Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development.

Authors:  Qin Ni; Ana Belén García Hernando; Iván Pau de la Cruz
Journal:  Sensors (Basel)       Date:  2015-05-14       Impact factor: 3.576

3.  Toward personalized and context-aware prompting for smartphone-based intervention.

Authors:  Ramin Fallahzadeh; Samaneh Aminikhanghahi; Ashley Nichole Gibson; Diane J Cook
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2016-08

4.  Change-point detection in time-series data by relative density-ratio estimation.

Authors:  Song Liu; Makoto Yamada; Nigel Collier; Masashi Sugiyama
Journal:  Neural Netw       Date:  2013-02-04

5.  CASAS: A Smart Home in a Box.

Authors:  Diane J Cook; Aaron S Crandall; Brian L Thomas; Narayanan C Krishnan
Journal:  Computer (Long Beach Calif)       Date:  2013-07       Impact factor: 2.683

6.  Automated Detection of Activity Transitions for Prompting.

Authors:  Kyle D Feuz; Diane J Cook; Cody Rosasco; Kayela Robertson; Maureen Schmitter-Edgecombe
Journal:  IEEE Trans Hum Mach Syst       Date:  2014-11-06       Impact factor: 2.968

7.  Evaluation of prompted annotation of activity data recorded from a smart phone.

Authors:  Ian Cleland; Manhyung Han; Chris Nugent; Hosung Lee; Sally McClean; Shuai Zhang; Sungyoung Lee
Journal:  Sensors (Basel)       Date:  2014-08-27       Impact factor: 3.576

  7 in total

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