Multi-resident Activity Recognition Using Incremental Decision Trees

Prossegger, Markus; Bouchachia, Abdelhamid

doi:10.1007/978-3-319-11298-5_19

Cited by 39 publications

(26 citation statements)

References 20 publications

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“…There was a proposal of coupled HMM and factorial HMM in computer vision domain [3], but only coupled HMM was employed for sensor data [5]. Besides HMMs, CRFs [9,13] and incremental decision trees (IDT) [18] also have been used for multi-resident activity recognition .…”

Section: Related Workmentioning

confidence: 99%

On multi-resident activity recognition in ambient smart-homes

et al. 2019

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Increasing attention to the research on activity monitoring in smart homes has motivated the employment of ambient intelligence to reduce the deployment cost and solve the privacy issue. Several approaches have been proposed for multi-resident activity recognition, however, there still lacks a comprehensive benchmark for future research and practical selection of models. In this paper we study different methods for multi-resident activity recognition and evaluate them on same sets of data. The experimental results show that recurrent neural network with gated recurrent units is better than other models and also considerably efficient, and that using combined activities as single labels is more effective than represent them as separate labels.

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Section: Related Workmentioning

confidence: 99%

On multi-resident activity recognition in ambient smart-homes

et al. 2019

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“…In [87], the authors applied decision trees to model activities of daily living in a multi-resident context. An extension of ID5R, called E-ID5R, was proposed where the leaf nodes are multi-labeled.…”

Section: Decision Treesmentioning

confidence: 99%

“…The modeling and recognition of such activities require a different approach from the ones used for single-occupancy activities. A few number of studies have recently focused on multi-occupancy [45,87], but the area is still to grow as it comes with is own scientific challenges and application potential. -Online activity learning: In comparison to offline activity recognition, online activity recognition has not been much investigated by the researchers.…”

Section: Activity Recognitionmentioning

confidence: 99%

A review of smart homes in healthcare

Amiribesheli

Benmansour

Bouchachia

2015

J Ambient Intell Human Comput

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156

123

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The technology of Smart Homes (SH), as an instance of ambient assisted living technologies, is designed to assist the homes' residents accomplishing their daily-living activities and thus having a better quality of life while preserving their privacy. A SH system is usually equipped with a collection of inter-related software and hardware components to monitor the living space by capturing the behaviour of the resident and understanding his activities. By doing so the system can inform about risky situations and take actions on behalf of the resident to his satisfaction. The present survey will address technologies and analysis methods and bring examples of the state of the art research studies in order to provide background for the research community. In particular, the survey will expose infrastructure technologies such as sensors and communication platforms along with artificial intelligence techniques used for modeling and recognizing activities. A brief overview of approaches used to develop Human-Computer (HC) interfaces for SH systems is given. The survey also highlights the challenges and research trends in this area.

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“…Unlike traditional classification decision trees (DT) usually applied in single resident activity recognition, (Prossegger and Bouchachia, 2014) suggested the use of an incremental DT algorithm called E-ID5R which can represent single or multiple activities at time to recognize both parallel and cooperative activities. The experimental evaluation on real-world datasets showed that E-ID5R performs differently.…”

Section: Related Workmentioning

confidence: 99%

Modeling interaction in multi-resident activities

Benmansour

Bouchachia²,

Feham

2017

Neurocomputing

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In this paper we investigate the problem of modeling multi-resident activities. Specifically, we explore different approaches based on Hidden Markov Models (HMMs) to deal with parallel activities and cooperative activities. We propose an HMM-based method, called CL-HMM, where activity labels as well as observation labels of different residents are combined to generate the corresponding sequence of activities as well as the corresponding sequence of observations on which a conventional HMM is applied. We also propose a Linked HMM (LHMM) in which activities of all residents are linked at each time step. We compare these two models to baseline models which are Coupled HMM (CHMM) and Parallel HMM (PHMM). The experimental results show that the proposed models outperform CHMM and PHMM when tested on parallel and cooperative activities.

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Multi-resident Activity Recognition Using Incremental Decision Trees

Cited by 39 publications

References 20 publications

On multi-resident activity recognition in ambient smart-homes

On multi-resident activity recognition in ambient smart-homes

A review of smart homes in healthcare

Modeling interaction in multi-resident activities

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