Automated activity-aware prompting for activity initiation
Open accessAbstract
Performing daily activities without assistance is important to maintaining an independent functional lifestyle. As a result, automated activity prompting systems can potentially extend the period of time that adults can age in place. In this paper we introduce AP, an algorithm to automate activity prompting based on smart home technology. AP learns prompt rules based on the time when activities are typically performed as well as the relationship between activities that normally occur in a sequence. We evaluate the AP algorithm based on smart home datasets and demonstrate its ability to operate within a physical smart environment.
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Rule-based activity forecasting relies on the particular rule representation and discovery method and is not sensitive to more complex relationships between activities.
“Rule-based approaches rely on the particular rule representation and discovery method that is used and will not be sensitive to more complex relationships between activities.”
From Forecasting occurrences of activities (2017) · cites this paper as [2]
The Discussion (page unavailable) says this paper’s learned rules use a relative activity and a time offset, while other factors could influence activity timing. That supports a limitation of this particular rule representation, but not the claim that rule-based approaches generally cannot capture more complex relationships; the Methods also describe rules involving multiple relative activities. (LLM, conf=0.83)
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Cites
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Cited by
Sorted by citations
Forecasting occurrences of activities
Minor, B., & Cook, D. J. (2017) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2016.09.010 · Cited by 45 · see citing works
“A smart environment is a physical environment that is equipped to sense the state of its inhabitants and their surroundings in order to act to improve the wellbeing of the inhabitants and the environment [1,2].”
BackgroundMethodSelf-citation
- Authors
- Holder, L. B., & Cook, D. J.
- Affiliations
- Washington State University (US)
- Venue
- Gerontechnology
- Published
- 2013-03-21
- Volume
- 11
- Issue
- 4
- Pages
- 534-544
- ISSN
- 1569-1101 (ISSN-L)
- 1569-111X
- PMC
- PMC3783261
- OpenAlex
- W2154884892
- Cited by
- 24
- References
- 21
- Citations by year
- 2023: 2 · 2021: 2 · 2020: 3 · 2019: 5 · 2018: 2 · 2017: 1 · 2016: 3 · 2015: 2 · 2014: 2 · 2013: 2
- FWCI
- 3.37
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Gerontechnology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (R01EB009675, R01EB015853)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Demography › Technology Use by Older Adults
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Keywords
- home automation, aging in place, independent living, activity sequences, daily activities, older adults
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