skip to main content
10.1145/3341162.3344834acmconferencesArticle/Chapter ViewAbstractPublication PagesubicompConference Proceedingsconference-collections
research-article

Activity prediction for improving well-being of both the elderly and caregivers

Published: 09 September 2019 Publication History

Abstract

The issue of ageing population is gaining significant attention across the world, while the caregivers' psychological burden caused by a variety of geriatric symptoms is often overlooked. Efficient collaboration between the elderly and caregivers has great potential to relieve the caregivers' psychological burden and improve the caregiving quality. For instance, activity prediction can provide a promising approach to cultivate this efficient collaboration. Given the ability to predict the elderly patients' activity and its timing, caregivers can provide timely and appropriate care, which not only can relieve caregiving stress for professional or family caregivers, but also can reduce the unwanted conflicts between both parties. In this paper, we train an activity predictor by integrating the activity temporal information into the Long Short-Term Memory (LSTM) networks. The approach leads to significant improvements in the prediction accuracy both in the next activity and its precise occurrence time.

References

[1]
Aitor Almeida and Gorka Azkune. 2018. Predicting human behaviour with recurrent neural networks. Applied Sciences 8, 2 (2018), 305.
[2]
European Commission. 2015. The 2015 ageing report: Economic and budgetary projections for the 28 EU member states (2013--2060). European Economy 3/2015 Financial Affairs, Brussels. (2015).
[3]
Diane J Cook and Narayanan C Krishnan. 2015. Activity learning: discovering, recognizing, and predicting human behavior from sensor data. John Wiley & Sons.
[4]
Lefteris Koumakis, Charikleia Chatzaki, Eleni Kazantzaki, Evangelia Maniadi, and Manolis Tsiknakis. 2019. Dementia Care Frameworks and Assistive Technologies for Their Implementation: A Review. IEEE reviews in biomedical engineering 12 (2019), 4--18.
[5]
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. arXiv preprint arXiv:1802.05365 (2018).
[6]
Martin James Prince. 2015. World Alzheimer Report 2015: the global impact of dementia: an analysis of prevalence, incidence, cost and trends. Alzheimer's Disease International.
[7]
Emmanuel Munguia Tapia, Natalia Marmasse, Stephen S Intille, and Kent Larson. 2004. MITes: Wireless portable sensors for studying behavior. In Proceedings of Extended Abstracts Ubicomp 2004: Ubiquitous Computing.
[8]
Tim Van Kasteren, Athanasios Noulas, Gwenn Englebienne, and Ben Kröse. 2008. Accurate activity recognition in a home setting. In Proceedings of the 10th international conference on Ubiquitous computing. ACM, 1--9.

Cited By

View all
  • (2023)Predicting Activities of Daily Living for the Coming Time Period in Smart HomesIEEE Transactions on Human-Machine Systems10.1109/THMS.2022.317621353:1(228-238)Online publication date: Feb-2023
  • (2023)(Counter-)stereotypical Gendering of Robots in Care: Impact on Needs Satisfaction and Gender Role Concepts in Men and Women UsersInternational Journal of Social Robotics10.1007/s12369-023-01033-w15:11(1769-1790)Online publication date: 22-Aug-2023

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
UbiComp/ISWC '19 Adjunct: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
September 2019
1234 pages
ISBN:9781450368698
DOI:10.1145/3341162
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 September 2019

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. activity prediction
  2. activity recognition
  3. long-term healthcare
  4. recurrent neural networks
  5. smart home

Qualifiers

  • Research-article

Funding Sources

Conference

UbiComp '19

Acceptance Rates

Overall Acceptance Rate 764 of 2,912 submissions, 26%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)16
  • Downloads (Last 6 weeks)0
Reflects downloads up to 19 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Predicting Activities of Daily Living for the Coming Time Period in Smart HomesIEEE Transactions on Human-Machine Systems10.1109/THMS.2022.317621353:1(228-238)Online publication date: Feb-2023
  • (2023)(Counter-)stereotypical Gendering of Robots in Care: Impact on Needs Satisfaction and Gender Role Concepts in Men and Women UsersInternational Journal of Social Robotics10.1007/s12369-023-01033-w15:11(1769-1790)Online publication date: 22-Aug-2023

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media