Intelligent Decision Support System for Dementia Care Through Smart Home
Open accessAbstract
As per the statistics of WHO (World Health Organization), major percentages of aged society across the globe are affected with memory related disease - dementia. The percentage of dement people would be doubled in future and hence assistive health care systems have become predominant. Smart home, an ubiquitous environment offers ambient assisted living to its occupant through activity recognition and decision making process. This research work proposes an assistive dementia care environment through smart home that aid mentally disabled people with many different types of assistance during emergency. The proposed system models “Intelligent Decision Support System” that identifies deviation of the occupant from their regular activities of daily routines and decides on appropriate alerts to handle these situations. Significant criterion to model dementia care is to handle incomplete event sequences (produced due to memory loss) and to model occupant specific knowledge (provided by the care taker / doctors). Markov Logic Network (MLN), an approach of statistical relational learning models uncertain data and domain knowledge within a single framework. Thus, the proposed approach of decision support system for dementia care effectively utilizes MLN for its modeling. The experimental study made with smart home dataset showcased the competence of MLN approach of decision making has higher F-measure than existing approaches.
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The Markov Logic Network handles both uncertainty modeling and domain knowledge modeling within a single framework, thus modeling factors that influence patient abnormality.
“The Markov Logic Network used handles both uncertainty modeling and domain knowledge modeling within a single framework, thus modeling the factors that influence patient abnormality [55].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [55]
A Markov Logic Network was used for activity recognition design to model both simple and composite activities and decide on appropriate alerts to process patient abnormality.
“In one example, Markov Logic Network was used for activity recognition design to model both simple and composite activities and decide on appropriate alerts to process patient abnormality.”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [55]
This machine learning-based activity recognition framework detected abnormality together with contextual factors such as object, space, time, and duration for decision support on suitable action to keep the patient safe.
“This machine learning-based activity recognition framework detected abnormality together with contextual factors such as object, space, time, and duration for decision support on suitable action to keep the patient safe in the given environment.”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [55]
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The rise of artificial intelligence in healthcare applications
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- Authors
- Gayathri, K. S., & Easwarakumar, K. S.
- Affiliations
- Sri Venkateswara University (IN)
- Anna University, Chennai (IN)
- Corresponding author
- Gayathri, K.S.
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2016
- Volume
- 93
- Pages
- 947-955
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2507138918
- Cited by
- 22
- References
- 22
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 3 · 2022: 2 · 2021: 3 · 2020: 3 · 2019: 2 · 2018: 2 · 2017: 1
- FWCI
- 0.58
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
- Keywords
- dementia care, home automation, ambient assisted living, intelligent decision support system, Markov logic networks, activity recognition, activities of daily living, memory loss, emergency alerting, statistical relational learning
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