The population of elderly people has been rapidly increasing in recent decades and is expected to continue growing in the future. This increase in population is accompanied by a rise in their needs, including physical disabilities, cognitive issues, weakened memory, and disorganized behavior that come with age. In order to alleviate the financial burden on the global economy and improve their quality of life, it is crucial to develop technology-based solutions that are adaptive, assistive, and intelligent. One potential long-term solution for enhancing the user experience of elderly individuals in smart homes is the development of Intelligent Affect Aware Systems. These systems have the capability to not only analyze but also predict the behavior of elderly people within an Internet of Things (IoT)-based environment. By understanding their day-to-day interactions with technology, these systems can effectively reduce their financial burden and enhance their quality of life. To achieve this goal, a framework for an Intelligent Affect Aware environment for elderly people has been proposed. This framework aims to analyze the affective components of their interactions and predict their likely user experience before they engage in any activity within a smart home environment. By forecasting user experience, there is an opportunity to enhance it and further improve the assistive and adaptive nature of these intelligent systems. To validate the efficacy of this proposed framework, it has been tested on three datasets. The results obtained from these tests are presented and discussed in detail. Moving forward with implementation, several steps need to be taken. First, a database of user interactions during day-to-day activities and Activities of Daily Living (ADLs) must be developed within a smart IoT-based environment. Next, specific characteristics of complex activities should be identified by analyzing atomic activities and context attributes associated with them. The CARALGO algorithm can then be used to infer when a given complex activity starts or ends based on atomic activities performed on associated context attributes. During completion of each atomic activity, facial images can be captured to analyze the conveyed emotion. This information is then recorded for further analysis. By using CARALGO to infer the end or completion of a complex activity, the overall emotion conveyed by the user can be inferred by analyzing the recorded information. This emotion can be related to the mood using a specific set of rules obtained through a learning approach. Finally, a learning approach should be developed to predict user experience based on the analyzed emotion and mood which will further enhance adaptability and assistiveness for intelligent systems in smart homes for elderly individuals . In conclusion, this proposed framework for an Intelligent Affect Aware environment shows great potential in improving quality of life for elderly people living in smart homes by accurately predicting user experiences and enhancing them so as to reduce financial burdens while providing better assistance and support .
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Enhancing Quality of Life for Elderly People in Smart Homes with Intelligent Affect Aware Systems
The population of elderly people has been rapidly increasing in recent decades and is expected to continue growing in the future. This increase in population is accompanied by a rise in their needs, including physical disabilities, cognitive issues, weakened memory, and disorganized behavior that come with age. To alleviate the financial burden on the global economy and improve their quality of life, it is crucial to develop technology-based solutions that are adaptive, assistive, and intelligent. One potential long-term solution for enhancing the user experience of elderly individuals living within smart homes is the development of Intelligent Affect Aware Systems (IAAS).
What Are IAAS?
IAAS have the capability to not only analyze but also predict the behavior of elderly people within an Internet of Things (IoT)-based environment. By understanding their day-to-day interactions with technology, these systems can effectively reduce their financial burden and enhance their quality of life. To achieve this goal, a framework for an Intelligent Affect Aware environment for elderly people has been proposed. This framework aims to analyze the affective components of their interactions and predict their likely user experience before they engage in any activity within a smart home environment. By forecasting user experience there is an opportunity to enhance it further improving adaptability and assistiveness for intelligent systems in smart homes for elderly individuals .
How Does It Work?
To validate its efficacy this proposed framework has been tested on three datasets which have yielded promising results as discussed below:
1) A database must be developed containing user interactions during day-to-day activities as well as Activities Of Daily Living (ADLs) within a smart IoT based environment; 2) Specific characteristics associated with complex activities should be identified by analyzing atomic activities along with context attributes; 3) The CARALGO algorithm can then be used to infer when a given complex activity starts or ends based on atomic activities performed on associated context attributes; 4) During completion of each atomic activity facial images can be captured to analyze conveyed emotion which will then be recorded for further analysis; 5) Using CARALGO again we can infer overall emotion conveyed by user by analyzing recorded information which can then be related to mood using specific set rules obtained through learning approach; 6) Finally a learning approach should be developed so as to predict user experience based on analyzed emotion & mood which will further enhance adaptability & assistiveness for intelligent systems present in smart homes meant specifically designed keeping needs & requirements of elderly individuals into consideration .
Conclusion
In conclusion this proposed framework shows great potential when it comes down improving quality life led by elderly people living inside smart homes via accurately predicting users experiences & enhancing them so as reduce financial burdens while providing better assistance & support .
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.
