A Hybrid Approach to Recognising Activities of Daily Living from Object Use in the Home Environment
Accurate recognition of Activities of Daily Living (ADL) plays an important role in providing assistance and support to the elderly and cognitively impaired. Current knowledge-driven and ontology-based techniques model object concepts from assumptions and everyday common knowledge of object use for...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-01-01
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| Series: | Informatics |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2227-9709/5/1/6 |
| _version_ | 1818289154481455104 |
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| author | Isibor Kennedy Ihianle Usman Naeem Syed Islam Abdel-Rahman Tawil |
| author_facet | Isibor Kennedy Ihianle Usman Naeem Syed Islam Abdel-Rahman Tawil |
| author_sort | Isibor Kennedy Ihianle |
| collection | DOAJ |
| description | Accurate recognition of Activities of Daily Living (ADL) plays an important role in providing assistance and support to the elderly and cognitively impaired. Current knowledge-driven and ontology-based techniques model object concepts from assumptions and everyday common knowledge of object use for routine activities. Modelling activities from such information can lead to incorrect recognition of particular routine activities resulting in possible failure to detect abnormal activity trends. In cases where such prior knowledge are not available, such techniques become virtually unemployable. A significant step in the recognition of activities is the accurate discovery of the object usage for specific routine activities. This paper presents a hybrid framework for automatic consumption of sensor data and associating object usage to routine activities using Latent Dirichlet Allocation (LDA) topic modelling. This process enables the recognition of simple activities of daily living from object usage and interactions in the home environment. The evaluation of the proposed framework on the Kasteren and Ordonez datasets show that it yields better results compared to existing techniques. |
| first_indexed | 2024-12-13T02:07:46Z |
| format | Article |
| id | doaj.art-64e71aaf6248471f83e385a54587b51a |
| institution | Directory Open Access Journal |
| issn | 2227-9709 |
| language | English |
| last_indexed | 2024-12-13T02:07:46Z |
| publishDate | 2018-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Informatics |
| spelling | doaj.art-64e71aaf6248471f83e385a54587b51a2022-12-22T00:03:05ZengMDPI AGInformatics2227-97092018-01-0151610.3390/informatics5010006informatics5010006A Hybrid Approach to Recognising Activities of Daily Living from Object Use in the Home EnvironmentIsibor Kennedy Ihianle0Usman Naeem1Syed Islam2Abdel-Rahman Tawil3School of Architecture, Computing and Engineering (ACE), University of East London, London E16 2RD, UKSchool of Architecture, Computing and Engineering (ACE), University of East London, London E16 2RD, UKSchool of Architecture, Computing and Engineering (ACE), University of East London, London E16 2RD, UKSchool of Computing and Digital Technology, Birmingham City University, Birmingham B5 5JU, UKAccurate recognition of Activities of Daily Living (ADL) plays an important role in providing assistance and support to the elderly and cognitively impaired. Current knowledge-driven and ontology-based techniques model object concepts from assumptions and everyday common knowledge of object use for routine activities. Modelling activities from such information can lead to incorrect recognition of particular routine activities resulting in possible failure to detect abnormal activity trends. In cases where such prior knowledge are not available, such techniques become virtually unemployable. A significant step in the recognition of activities is the accurate discovery of the object usage for specific routine activities. This paper presents a hybrid framework for automatic consumption of sensor data and associating object usage to routine activities using Latent Dirichlet Allocation (LDA) topic modelling. This process enables the recognition of simple activities of daily living from object usage and interactions in the home environment. The evaluation of the proposed framework on the Kasteren and Ordonez datasets show that it yields better results compared to existing techniques.http://www.mdpi.com/2227-9709/5/1/6activity recognitiontopic modelontology modelLatent Dirichlet Allocation |
| spellingShingle | Isibor Kennedy Ihianle Usman Naeem Syed Islam Abdel-Rahman Tawil A Hybrid Approach to Recognising Activities of Daily Living from Object Use in the Home Environment Informatics activity recognition topic model ontology model Latent Dirichlet Allocation |
| title | A Hybrid Approach to Recognising Activities of Daily Living from Object Use in the Home Environment |
| title_full | A Hybrid Approach to Recognising Activities of Daily Living from Object Use in the Home Environment |
| title_fullStr | A Hybrid Approach to Recognising Activities of Daily Living from Object Use in the Home Environment |
| title_full_unstemmed | A Hybrid Approach to Recognising Activities of Daily Living from Object Use in the Home Environment |
| title_short | A Hybrid Approach to Recognising Activities of Daily Living from Object Use in the Home Environment |
| title_sort | hybrid approach to recognising activities of daily living from object use in the home environment |
| topic | activity recognition topic model ontology model Latent Dirichlet Allocation |
| url | http://www.mdpi.com/2227-9709/5/1/6 |
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