Framework for A Personalized Intelligent Assistant to Elderly People for Activities of Daily Living

• URL: http://arxiv.org/abs/2107.07344v1
• Date: Tue, 29 Jun 2021 17:36:07 GMT
• Title: Framework for A Personalized Intelligent Assistant to Elderly People for Activities of Daily Living
• Authors: Nirmalya Thakur and Chia Y. Han
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing population of elderly people is associated with the need to meet their increasing requirements and to provide solutions that can improve their quality of life in a smart home. In addition to fear and anxiety towards interfacing with systems; cognitive disabilities, weakened memory, disorganized behavior and even physical limitations are some of the problems that elderly people tend to face with increasing age. The essence of providing technology-based solutions to address these needs of elderly people and to create smart and assisted living spaces for the elderly; lies in developing systems that can adapt by addressing their diversity and can augment their performances in the context of their day to day goals. Therefore, this work proposes a framework for development of a Personalized Intelligent Assistant to help elderly people perform Activities of Daily Living (ADLs) in a smart and connected Internet of Things (IoT) based environment. This Personalized Intelligent Assistant can analyze different tasks performed by the user and recommend activities by considering their daily routine, current affective state and the underlining user experience. To uphold the efficacy of this proposed framework, it has been tested on a couple of datasets for modelling an average user and a specific user respectively. The results presented show that the model achieves a performance accuracy of 73.12% when modelling a specific user, which is considerably higher than its performance while modelling an average user, this upholds the relevance for development and implementation of this proposed framework.

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