Modeling activity recognition of multi resident using label combination of multi label classification in smart home

Mohamed, Raihani; Perumal, Thinagaran; Sulaiman, Nasir; Mustapha, Norwati; Zainudin, Muhammad Noorazlan Shah

doi:10.1063/1.5005427

Cited by 11 publications

(8 citation statements)

References 25 publications

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“…Sleep plays a significant role in the overall health and welfare of a person; less sleep short of your lifespan, so it is important to perform prediction for potential sleep behaviour for Smart Homes residents. This study contributes to the early detection of diseases such as heart attack, blood pressure, diabetes, obesity, stress and even stroke that are likely to face residents in Smart Homes due to disorder sleeping behaviour as per health issues pre-described by the health unit [19]. 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.…”

Section: Resultsmentioning

confidence: 99%

“…The predictions of activities take place on the smart bed usage; data were collected using smart sensors connected to the smart bed and analyzed to extract hidden patterns from data by using different Machine Learning Techniques [2]. The HAR plays an essential role in recognizing human activities due to its applications in various fields such as health, security, electricity and water usage [3].…”

Section: Introductionmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

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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“…In terms of future work, dynamic activities involving transition such as walking to running, standing to sitting and standing to jumping may be included for classification. With the ability to classify transition activities, it is possible to recognize different orders of complex actions such as sports, laundry and so on [15,16]. Fall detection can also be recognized when the classification model is effectively performed in the classification of the transition between activities.…”

Section: Discussionmentioning

confidence: 99%

Activity Recognition on Subject Independent Using Machine Learning

Chaudhari

Aparna

Rane³

2020

Cybernetics and Information Technologies

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Recent Activity Daily Living (ADL) not only tackles simple activities, but also caters to a wide range of complex activities. Although the same activity has been carried out under the same environmental conditions, the acceleration signal obtained from each subject considerably differs. This happens due to the pattern of action generated for each subject is diverse based on several aspects such as subject age, gender, emotion and personality. This project therefore compares the accuracy of various machine learning models for ADL classification. On top of that, this research work also scrutinizes the effectiveness of various feature selection methods to identify the most relevant attribute for ADL classification. As a result, Random Forest was able to achieve the highest accuracy of 83.3% on subject independent matter in ADL classification. Meanwhile, CFS Subset Evaluator is considered to be a good feature selector as it successfully selected the 8 most relevant features compared with Correlation and Information Gain Evaluator.

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“…For the ARAS dataset, we first performed multi-label merging [ 20 ] for the 27 activities identified in the dataset, combining similar activity types, such as preparing breakfast, preparing lunch, and preparing dinner, into “cooking” and use “eating” to represent having breakfast, having lunch, and having dinner to enhance the nature of the activity. The combined results are shown in Table 6 and Table 7 .…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Therefore, this paper tries to address such activity recognition problem in the multi-resident environment and proposes to explore a suit of machine learning models to tackle this problem. Specifically, we investigate and implement five traditional machine learning methods (including Support Vector Machine , K-Nearest Neighbor , Multi-Layer Perceptron , J48 Decision Tree , Random Forest ) and two deep learning techniques (including Recurrent Neural Networks and Long Short-Term Memory ) for modeling and testing, and then apply six special historical activity features with multi-label methods [ 20 ] to evaluate their benefits based on the confusion matrix and 10-Fold Cross-Validation. Note that the deep learning models have the nature to handle time series features, which are fitting with the characteristics of sequential home activities.…”

Section: Introductionmentioning

confidence: 99%

Applying Machine Learning Technologies Based on Historical Activity Features for Multi-Resident Activity Recognition

Liang

Chung

et al. 2021

Sensors

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Due to the aging population, home care for the elderly has become very important. Currently, there are many studies focusing on the deployment of various sensors in the house to recognize the home activities of the elderly, especially for the elderly living alone. Through these, we can detect the home situation of the single person and ensure his/her living safety. However, the living environment of the elderly includes, not only the person living alone, but also multiple people living together. By applying the traditional methods for a multi-resident environment, the “individual” activities of each person could not be accurately identified. This resulted in an inability to distinguish which person was involved in what activities, and thus, failed to provide personal care. Therefore, this research tries to investigate how to recognize home activities in multi-resident living environments, in order to accurately distinguish the association between residents and home activities. Specifically, we propose to use the special characteristics of historical activity of residents in a multi-person environment, including activity interaction, activity frequency, activity period length, and residential behaviors, and then apply a suite of machine learning methods to train and test. Five traditional models of supervised learning and two deep learning methods are explored to tackle this problem. Through the experiments with real datasets, the proposed methods were found to achieve higher precision, recall and accuracy with less training time. The best accuracy can reach up to 91% and 95%, by J48DT, and LSTM, respectively, in different living environments.

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Modeling activity recognition of multi resident using label combination of multi label classification in smart home

Cited by 11 publications

References 25 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

Activity Recognition on Subject Independent Using Machine Learning

Applying Machine Learning Technologies Based on Historical Activity Features for Multi-Resident Activity Recognition

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