Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods
This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/19/9096 |
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| author | Idongesit Ekerete Matias Garcia-Constantino Alexandros Konios Mustafa A. Mustafa Yohanca Diaz-Skeete Christopher Nugent James McLaughlin |
| author_facet | Idongesit Ekerete Matias Garcia-Constantino Alexandros Konios Mustafa A. Mustafa Yohanca Diaz-Skeete Christopher Nugent James McLaughlin |
| author_sort | Idongesit Ekerete |
| collection | DOAJ |
| description | This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and classify activities performed in home environments can help monitor health parameters in vulnerable individuals. This study addresses five principal concerns in ARC: (i) users’ privacy, (ii) wearability, (iii) data acquisition in a home environment, (iv) actual recognition of activities, and (v) classification of activities from single to multiple users. Timestamp information from contact sensors mounted at strategic locations in a kitchen environment helped obtain the time, location, and activity of 10 participants during the experiments. A total of 11,980 thermal blobs gleaned from privacy-friendly USSs such as ceiling and lateral thermal sensors were fused using data mining models and methods. Experimental results demonstrated cluster-based activity recognition, classification, and fusion of the datasets with an average regression coefficient of 0.95 for tested features and clusters. In addition, a pooled Mean accuracy of 96.5% was obtained using classification-by-clustering and statistical methods for models such as Neural Network, Support Vector Machine, K-Nearest Neighbour, and Stochastic Gradient Descent on Evaluation Test. |
| first_indexed | 2024-03-10T07:06:20Z |
| format | Article |
| id | doaj.art-04ea4a3ce60e48a4ad46b361163f7c64 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T07:06:20Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-04ea4a3ce60e48a4ad46b361163f7c642023-11-22T15:47:43ZengMDPI AGApplied Sciences2076-34172021-09-011119909610.3390/app11199096Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and MethodsIdongesit Ekerete0Matias Garcia-Constantino1Alexandros Konios2Mustafa A. Mustafa3Yohanca Diaz-Skeete4Christopher Nugent5James McLaughlin6School of Computing, Ulster University, Jordanstown Campus, Newtownabbey BT37 0QB, UKSchool of Computing, Ulster University, Jordanstown Campus, Newtownabbey BT37 0QB, UKSchool of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent ST4 2DE, UKDepartment of Computer Science, The University of Manchester, Manchester M13 9PL, UKNetwellCASALA Advanced Research Centre, Dundalk Institute of Technology, A91 K584 Dundalk, IrelandSchool of Computing, Ulster University, Jordanstown Campus, Newtownabbey BT37 0QB, UKSchool of Engineering (NIBEC), Ulster University, Jordanstown Campus, Newtownabbey BT37 0QB, UKThis paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and classify activities performed in home environments can help monitor health parameters in vulnerable individuals. This study addresses five principal concerns in ARC: (i) users’ privacy, (ii) wearability, (iii) data acquisition in a home environment, (iv) actual recognition of activities, and (v) classification of activities from single to multiple users. Timestamp information from contact sensors mounted at strategic locations in a kitchen environment helped obtain the time, location, and activity of 10 participants during the experiments. A total of 11,980 thermal blobs gleaned from privacy-friendly USSs such as ceiling and lateral thermal sensors were fused using data mining models and methods. Experimental results demonstrated cluster-based activity recognition, classification, and fusion of the datasets with an average regression coefficient of 0.95 for tested features and clusters. In addition, a pooled Mean accuracy of 96.5% was obtained using classification-by-clustering and statistical methods for models such as Neural Network, Support Vector Machine, K-Nearest Neighbour, and Stochastic Gradient Descent on Evaluation Test.https://www.mdpi.com/2076-3417/11/19/9096K-Means analysishome environmentsensor fusionactivity recognitionunobtrusive sensingdata mining |
| spellingShingle | Idongesit Ekerete Matias Garcia-Constantino Alexandros Konios Mustafa A. Mustafa Yohanca Diaz-Skeete Christopher Nugent James McLaughlin Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods Applied Sciences K-Means analysis home environment sensor fusion activity recognition unobtrusive sensing data mining |
| title | Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods |
| title_full | Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods |
| title_fullStr | Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods |
| title_full_unstemmed | Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods |
| title_short | Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods |
| title_sort | fusion of unobtrusive sensing solutions for home based activity recognition and classification using data mining models and methods |
| topic | K-Means analysis home environment sensor fusion activity recognition unobtrusive sensing data mining |
| url | https://www.mdpi.com/2076-3417/11/19/9096 |
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