On multi-resident activity recognition in ambient smart-homes

Tran, Son N.; Nguyễn, Duc Tung; Son, Ngo Tung; Vu, Xuan-Son; Hoang, Long Phi; Zhang, Qing; Karunanithi, Mohanraj

doi:10.1007/s10462-019-09783-8

Cited by 21 publications

(11 citation statements)

References 23 publications

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“…In House B, again, figures show that Resident1 spend more time on sleeping activity than other activities while Resi-dent2 spends less time on sleeping activity than other activities. This implies that resident2s from both House A and B spend less time for sleeping activity, and this may affect their health in future due to lack of enough time for sleep and of which can lead to health problems such as heart attack, blood pressure, diabetes, obesity, stress and even stroke, hence shorten their life span [17,17]…”

Section: Experimental Discussionmentioning

confidence: 99%

Predicting of Sleep Behaviour in Smart Homes Based on Multi-residents Using Machine Learning Techniques

Huchaiah

Kasubi

2021

SN COMPUT. SCI.

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Sleep is a vital part of our daily lives; it plays a significant role in the overall health and welfare of a person. Smart Homes plays a vital role in recognizing human activities performed at home and making it a better place to live. This study focused on assessing sleep behaviour from the ARAS dataset generated from multi-residents in Smart Homes to predict potential sleep behaviour of residents in relation to other activities using different Machine Learning Techniques. In this regard, we used Logistic regression (RL), Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), Naive Bayes (NB), Classification and Regression Trees (CRT) and Support Vector Machine (SVM) to learn and analyze sleep behaviour in relation to other activities of which impacts to the health issues of the residents. The experimental results show that Support Vector Machine (SVM) outperformed in both House A and B in predicting potential sleep activity compared to other algorithms. In House A, Resident1 obtained an accuracy of 90%, and Resident2 obtained an accuracy of 80%, while in House B, Resi-dent1 achieved an accuracy of 100%, and Resident2 obtained an accuracy of 90%. Hence, these results show that resident1 for both House A and B spend enough time for sleeping activity compared to other activities; in contrast, resident2 in both House A and B spend less time in sleeping activity in relation to other activities of which this may affect their health due to sleeping disorder.

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Section: Experimental Discussionmentioning

confidence: 99%

Predicting of Sleep Behaviour in Smart Homes Based on Multi-residents Using Machine Learning Techniques

Huchaiah

Kasubi

2021

SN COMPUT. SCI.

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“…Recent advances in identifying activities in multi-occupant environments are presented in [ 17 ]. There are many published papers related to pattern recognition that conducted their research to detect HAR in a home environment using a range of different machine learning techniques, including HMM [ 34 , 35 ].…”

Section: Related Workmentioning

confidence: 99%

“…The current research acknowledges the challenges of multi-occupancy in HAR [ 5 , 7 , 17 ]. Such challenges are to find suitable models to represent the data association problem (i.e., the identification of the resident) and to find an activity recognition system that captures different interactions among residents [ 5 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring Entropy Measurements to Identify Multi-Occupancy in Activities of Daily Living

Howedi

Lotfi

Pourabdollah

2019

Entropy

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Human Activity Recognition (HAR) is the process of automatically detecting human actions from the data collected from different types of sensors. Research related to HAR has devoted particular attention to monitoring and recognizing the human activities of a single occupant in a home environment, in which it is assumed that only one person is present at any given time. Recognition of the activities is then used to identify any abnormalities within the routine activities of daily living. Despite the assumption in the published literature, living environments are commonly occupied by more than one person and/or accompanied by pet animals. In this paper, a novel method based on different entropy measures, including Approximate Entropy (ApEn), Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn), is explored to detect and identify a visitor in a home environment. The research has mainly focused on when another individual visits the main occupier, and it is, therefore, not possible to distinguish between their movement activities. The goal of this research is to assess whether entropy measures can be used to detect and identify the visitor in a home environment. Once the presence of the main occupier is distinguished from others, the existing activity recognition and abnormality detection processes could be applied for the main occupier. The proposed method is tested and validated using two different datasets. The results obtained from the experiments show that the proposed method could be used to detect and identify a visitor in a home environment with a high degree of accuracy based on the data collected from the occupancy sensors.

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“…The model collects motion, audio signals by using invasive wearable sensors and providing contextual information. Tran et al [30] proposed a model, which is based on activity monitoring in a smart home environment for multiple residents. Different methods for multiple resident activity detection are evaluated on the same data.…”

Section: Related Workmentioning

confidence: 99%

A Unified Framework for Behaviour Monitoring and Abnormality Detection for Smart Home

Grewal

Kaur

Park

2019

Wireless Communications and Mobile Computing

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With the revolution in smart infrastructure in the recent past, the smart healthcare system has been paid more considerable attention. The continuous upgradation of electricity meters to smart electricity devices has probed into a new market of intelligent data analysis services, providing aid to the health care systems. This paper presents a unified framework for extracting user behaviour patterns from home-based smart electricity meter data. The structure allows exploration and integration of frequent pattern growth algorithm for pattern mining and application of a variety of machine learning algorithms for categorizing the activities into manually labelled classes along with the implementation of Local Outlier Factor method for detection of an abnormal pattern of the inhabitant of smart homes. To evaluate the proposed framework, the work is implemented on the smart electricity dataset from the United Kingdom by separating the data into four distinct data files meant for the morning, afternoon, evening, and night energy utilization records. The results show a remarkable performance of Support Vector Machine (SVM) and Multilayer Perceptron (MLP) classifiers with kappa statics greater than 0.95 for all time slots data. The resultant frequent device utilization patterns with anomaly score more than the threshold value, reflecting abnormal activity patterns, are found more in evening time data in comparison to other time slots, requiring the immediate attention of concerned healthcare authorities.

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On multi-resident activity recognition in ambient smart-homes

Cited by 21 publications

References 23 publications

Predicting of Sleep Behaviour in Smart Homes Based on Multi-residents Using Machine Learning Techniques

Predicting of Sleep Behaviour in Smart Homes Based on Multi-residents Using Machine Learning Techniques

Exploring Entropy Measurements to Identify Multi-Occupancy in Activities of Daily Living

A Unified Framework for Behaviour Monitoring and Abnormality Detection for Smart Home

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