Framework for an Intelligent Affect Aware Smart Home Environment for
Elderly People
- URL: http://arxiv.org/abs/2106.15599v1
- Date: Tue, 29 Jun 2021 17:34:16 GMT
- Title: Framework for an Intelligent Affect Aware Smart Home Environment for
Elderly People
- Authors: Nirmalya Thakur and Chia Y. Han
- Abstract summary: The population of elderly people has been increasing at a rapid rate over the last few decades and their population is expected to further increase in the upcoming future.
Their increasing population is associated with their increasing needs due to problems like physical disabilities, cognitive issues, weakened memory and disorganized behavior.
This work proposes the framework for an Intelligent Affect Aware environment for elderly people that can not only analyze the affective components of their interactions but also predict their likely user experience.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The population of elderly people has been increasing at a rapid rate over the
last few decades and their population is expected to further increase in the
upcoming future. Their increasing population is associated with their
increasing needs due to problems like physical disabilities, cognitive issues,
weakened memory and disorganized behavior, that elderly people face with
increasing age. To reduce their financial burden on the world economy and to
enhance their quality of life, it is essential to develop technology-based
solutions that are adaptive, assistive and intelligent in nature. Intelligent
Affect Aware Systems that can not only analyze but also predict the behavior of
elderly people in the context of their day to day interactions with technology
in an IoT-based environment, holds immense potential for serving as a long-term
solution for improving the user experience of elderly in smart homes. This work
therefore proposes the framework for an Intelligent Affect Aware environment
for elderly people that can not only analyze the affective components of their
interactions but also predict their likely user experience even before they
start engaging in any activity in the given smart home environment. This
forecasting of user experience would provide scope for enhancing the same,
thereby increasing the assistive and adaptive nature of such intelligent
systems. To uphold the efficacy of this proposed framework for improving the
quality of life of elderly people in smart homes, it has been tested on three
datasets and the results are presented and discussed.
Related papers
- Towards Privacy-Aware and Personalised Assistive Robots: A User-Centred Approach [55.5769013369398]
This research pioneers user-centric, privacy-aware technologies such as Federated Learning (FL)
FL enables collaborative learning without sharing sensitive data, addressing privacy and scalability issues.
This work includes developing solutions for smart wheelchair assistance, enhancing user independence and well-being.
arXiv Detail & Related papers (2024-05-23T13:14:08Z) - Smart Home Goal Feature Model -- A guide to support Smart Homes for
Ageing in Place [0.4864105587622174]
This paper provides an overview of the smart home technologies commercially available to support ageing in place.
We create a structured Smart Home Goal Feature Model (SHGFM) to resolve approaches used by Subject Matter Experts (SMEs) at aged care facilities and healthcare researchers in adapting smart homes.
arXiv Detail & Related papers (2023-11-14T05:42:13Z) - Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic
Systems [67.01132165581667]
We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components.
We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
arXiv Detail & Related papers (2023-11-13T21:20:17Z) - Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
Predictive coding (PC) has shown promising performance in machine intelligence tasks.
PC can model information processing in different brain areas, can be used in cognitive control and robotics.
arXiv Detail & Related papers (2023-08-15T16:37:16Z) - Large Language Models Empowered Autonomous Edge AI for Connected
Intelligence [51.269276328087855]
Edge artificial intelligence (Edge AI) is a promising solution to achieve connected intelligence.
This article presents a vision of autonomous edge AI systems that automatically organize, adapt, and optimize themselves to meet users' diverse requirements.
arXiv Detail & Related papers (2023-07-06T05:16:55Z) - Adaptive cognitive fit: Artificial intelligence augmented management of
information facets and representations [62.997667081978825]
Explosive growth in big data technologies and artificial intelligence [AI] applications have led to increasing pervasiveness of information facets.
Information facets, such as equivocality and veracity, can dominate and significantly influence human perceptions of information.
We suggest that artificially intelligent technologies that can adapt information representations to overcome cognitive limitations are necessary.
arXiv Detail & Related papers (2022-04-25T02:47:25Z) - A Survey of Human Activity Recognition in Smart Homes Based on IoT
Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep
Learning [0.0]
Smart homes can offer home assistance services to improve the quality of life, autonomy and health of their residents.
To provide such services, a smart home must be able to understand the daily activities of its residents.
Recent algorithms, works, challenges and taxonomy of the field of human activity recognition in a smart home through ambient sensors are presented.
arXiv Detail & Related papers (2021-10-18T08:44:50Z) - Framework for A Personalized Intelligent Assistant to Elderly People for
Activities of Daily Living [0.0]
This work proposes a framework for development of a Personalized Intelligent Assistant to help elderly people perform Activities of Daily Living.
This framework can analyze different tasks performed by the user and recommend activities by considering their daily routine, current affective state and the underlining user experience.
The results presented show that the model achieves a performance accuracy of 73.12% when modelling a specific user.
arXiv Detail & Related papers (2021-06-29T17:36:07Z) - A Review of Assistive Technologies for Activities of Daily Living of
Elderly [0.0]
Elderly people have several needs and requirements due to physical disabilities, cognitive issues, weakened memory and disorganized behavior.
Various challenges exist in the context of implementation of assisted living services for elderly care in Smart Homes and Smart Cities.
arXiv Detail & Related papers (2021-06-23T06:17:49Z) - Taking Stock of the Present and Future of Smart Technologies for Older
Adults and Caregivers [18.258026962194872]
Technology has the opportunity to assist older adults as they age in place, coordinate caregiving resources, and meet unmet needs through access to resources.
Industry has attempted to create smart home technologies with older adults as a target user group, however these solutions are often more focused on the technical aspects and are short lived.
We advocate for older adults being involved in the design process - from initial ideation to product development to deployment.
arXiv Detail & Related papers (2021-03-31T20:28:38Z) - Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of
AI/AGI Using Multiple Intelligences and Learning Styles [95.58955174499371]
We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains.
Future AI systems will be able not only to communicate with human users and each other, but also to efficiently exchange knowledge and wisdom.
arXiv Detail & Related papers (2020-08-07T21:00:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.