Decision-making framework for using ambient assisted living

Alsulami, Majid H.; Alsaqer, Mohammed; Atkins, Anthony

doi:10.1108/ijpcc-09-2019-0066

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…It is, thus an essential feature of pervasive systems. Nowadays, the concept of context-awareness is gaining importance in the area of distributed systems (Alsulami et al , 2020; Chen and Rashidi, 2012; Sashima et al , 2006). It has proved to be a potential solution for many problems where dynamic environments with numerous sensors are applied (Han and Suh, 2012; Luo et al , 2002; Veloso et al , 2009).…”

Section: Related Workmentioning

confidence: 99%

Context–aware assistive driving: an overview of techniques for mitigating the risks of driver in real-time driving environment

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Kotecha

Ghinea

2021

IJPCC

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Purpose This study aims to analyze driver risks in the driving environment. A complete analysis of context aware assistive driving techniques. Context awareness in assistive driving by probabilistic modeling techniques. Advanced techniques using Spatio-temporal techniques, computer vision and deep learning techniques. Design/methodology/approach Autonomous vehicles have been aimed to increase driver safety by introducing vehicle control from the driver to Advanced Driver Assistance Systems (ADAS). The core objective of these systems is to cut down on road accidents by helping the user in various ways. Early anticipation of a particular action would give a prior benefit to the driver to successfully handle the dangers on the road. In this paper, the advancements that have taken place in the use of multi-modal machine learning for assistive driving systems are surveyed. The aim is to help elucidate the recent progress and techniques in the field while also identifying the scope for further research and improvement. The authors take an overview of context-aware driver assistance systems that alert drivers in case of maneuvers by taking advantage of multi-modal human processing to better safety and drivability. Findings There has been a huge improvement and investment in ADAS being a key concept for road safety. In such applications, data is processed and information is extracted from multiple data sources, thus requiring training of machine learning algorithms in a multi-modal style. The domain is fast gaining traction owing to its applications across multiple disciplines with crucial gains. Research limitations/implications The research is focused on deep learning and computer vision-based techniques to generate a context for assistive driving and it would definitely adopt by the ADAS manufacturers. Social implications As context-aware assistive driving would work in real-time and it would save the lives of many drivers, pedestrians. Originality/value This paper provides an understanding of context-aware deep learning frameworks for assistive driving. The research is mainly focused on deep learning and computer vision-based techniques to generate a context for assistive driving. It incorporates the latest state-of-the-art techniques using suitable driving context and the driver is alerted. Many automobile manufacturing companies and researchers would refer to this study for their enhancements.

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Section: Related Workmentioning

confidence: 99%

Context–aware assistive driving: an overview of techniques for mitigating the risks of driver in real-time driving environment

Gite

Kotecha

Ghinea

2021

IJPCC

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“…It provides valuable insights and guidelines for making informed decisions in implementing such systems. The framework's practicality and effectiveness make it a valuable resource for researchers and practitioners [15]. To leverage technologies for elderly care, a comprehensive approach was offered in [16].…”

Section: Introductionmentioning

confidence: 99%

Gated recurrent unit decision model for device argumentation in ambient assisted living

Kumar,

Prakash,

Krinkin

2024

IJECE

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The increasing elderly population worldwide is facing a variety of social, physical, and cognitive issues, such as walking problems, falls, and difficulties in performing daily activities. To support elderly people, continuous monitoring and supervision are needed. Due to the busy modern lifestyle of caretakers, taking care of elderly people is difficult. As a result, many elderly people prefer to live independently at home without any assistance. To help such people, an ambient assisted living (AAL) environment is provided that monitors and evaluates the daily activities of elderly individuals. An AAL environment has heterogeneous devices that interact, and exchange information of the activities performed by the users. The devices can be involve in an argumentation about the occurrence of an activity thus leading to generate conflicts. To address this issue, the paper proposes a gated recurrent unit (GRU) learning techniques to facilitate decision-making for device argumentation during activity occurrences. The proposed model is used to initially classify user activities and each sensor value status. Then a novel method is used to identify argumentation among devices for activity occurrences in the classified user activities. Later, the GRU decision making model is used to resolve the argumentation and to identify the target activity that occurred. The result of the proposed model is compared with other existing techniques. The proposed model outperformed the other existing methods with an accuracy of 85.45%, precision of 72.32%, recall of 65.83%, and F1-Score of 60.22%.

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