Human activity classification for smart home: A multiagent approach

Alam, Muhammad Raisul; Reaz, Mamun Bin Ibne; Ali, Mohd; Samad, Salina Abdul; Hashim, Fazida Hanim; Hamzah, Mustafar Kamal

doi:10.1109/isiea.2010.5679411

Cited by 8 publications

(7 citation statements)

References 9 publications

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“…In a Smart House context, the user's daily activities generate patterns that play an important role in predicting future events in the Smart House [17,18]. The goal of a Smart House environment is to assist the user with their daily life activities; thus, the Smart House should find repetitive patterns in the user's activities and predict the behaviour of the user for additional assistance [19,20].…”

Section: User Activity and Behaviourmentioning

confidence: 99%

“…These algorithms and techniques include, but are not limited to Bayesian Method, Markov Chain, statistical inferential algorithms, neural networks, Fuzzy logic and Multiagent System (MAS) among others [17,19].…”

Section: User Activity and Behaviourmentioning

confidence: 99%

“…Alam et al [17] developed a multi-agent system to track the user for task isolation. Their result showed successful identification of the inhabitant activities of various lengths.…”

Section: Intelligent Agentsmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Smart House Analysis Methods for Assisting Older People Living Alone

Sánchez

Pfeiffer

Skeie

2017

JSAN

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Smart Houses are a prominent field of research referring to environments adapted to assist people in their everyday life. Older people and people with disabilities would benefit the most from the use of Smart Houses because they provide the opportunity for them to stay in their home for as long as possible. In this review, the developments achieved in the field of Smart Houses for the last 16 years are described. The concept of Smart Houses, the most used analysis methods, and current challenges in Smart Houses are presented. A brief introduction of the analysis methods is given, and their implementation is also reported.

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Section: User Activity and Behaviourmentioning

confidence: 99%

Section: User Activity and Behaviourmentioning

confidence: 99%

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A Review of Smart House Analysis Methods for Assisting Older People Living Alone

Sánchez

Pfeiffer

Skeie

2017

JSAN

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“…People tend to follow a pattern in their daily live (Alam et al, 2010;Bourobou and Yoo, 2015). Therefore, it is possible to recognize the activities of daily life (ADL) a user performs, such as eating, toileting, bathing, dressing, etc.…”

Section: Introductionmentioning

confidence: 99%

Decision Trees for Human Activity Recognition in Smart House Environments

Sánchez

Skeie

2018

Linköping Electronic Conference Proceedings

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Human activity recognition in smart house environments is the task of automatic recognition of physical activities of a person to build a safe environment for older adults or any person in their daily life. The aim of this work is to develop a model that can recognize abnormal activities for assisting people living alone in a smart house environment. The idea is based on the assumption that people tend to follow a specific pattern of activities in their daily life. An open source database is used to train the decision trees classifier algorithm. Training and testing of the algorithm is performed using MATLAB. The results show an accuracy rate of 88.02% in the activity detection task.

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“…People tend to follow a pattern in their daily live [4,5]. Therefore, it is possible to recognize the activities of daily life (ADL) a user performs, such as eating, toileting, bathing, dressing, etc.…”

Section: Introductionmentioning

confidence: 99%