IoT supported smart home for the elderly

Sokullu, Radosveta; Akkaş, Mustafa Alper; Demir, Eren

doi:10.1016/j.iot.2020.100239

Cited by 56 publications

(21 citation statements)

References 21 publications

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“…According to the results of research related to elderly users' characteristics, the younger the age, the higher the education level, the higher the income, the more use of internet and smart phone [78]. In addition, the greater the desire to live in the same place, the greater the affinity with the technology, the greater the acceptance of smart home technology [79]. Shin et al ( 2018) compared the differences between the elderly and baby boomers, emphasizing that opinions of each generation should be considered in order to grasp the differences between users' preference and acceptance of technology [80].…”

Section: Methodsmentioning

confidence: 99%

Analysis of Smart Home Technology Acceptance and Preference for Elderly in Dubai, UAE

Arar

Jung

Awad

et al. 2021

Designs

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The elderly are the most predicted users for smart home technology in the United Arab Emirates and the population over 65 is expected to increase to 24.3% by 2030. Despite the rapid development of smart home technology, research has been mainly focused on technology development. To encourage conservative elderly users, however, smart home technology should be implemented for UAE elderly users to accept and integrate it into their daily lives. The objective of this paper is to analyze the preferences and needs of smart home technologies to understand the behaviors of UAE elderly users, and the factors affecting the acceptance of technology. As a methodology, a survey and interview were conducted for 110 people in their 40s and 60s and a total of 105 valid survey responses were collected and used as data for frequency, mean, cross-analysis, independent sample t-test, one-way variance analysis, and multiple regression analysis with IBM SPSS statistics 27. The results showed that 67.0% of UAE elderly users have chronic diseases such as high blood pressure (16.2%), heart disease (3.8%), diabetes (32.4%), or arthritis (10.5%). Therefore, smart home technology for health management is inevitable to improve overall lifestyles. It was statistically proven that UAE elderly users want automatic fall detection in the living room (39.0%) and bedroom (25.7%). Lifestyle monitoring in living room (44.7%) and bedroom (18.1%); the elderly preferred living room most for daily life assistance (36.2%), environmental control (50.5%), health and biometric monitoring (49.5%), and video conferencing (82.9%). In the case of sensors, elderly preferred the switch at the entrance (36.2%), and motion detecting sensors (42.9%), video cameras (56.2%), and voice recognition (50.5%) sensors in the living room. However, UAE elderly users do not think smart home technology can protect their privacy. It is found that age group and computer technology affinity are the most influential variables and UAE elderly users have an anxiety about technology, which influenced the acceptance of smart home technology.

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Section: Methodsmentioning

confidence: 99%

Analysis of Smart Home Technology Acceptance and Preference for Elderly in Dubai, UAE

Arar

Jung

Awad

et al. 2021

Designs

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“…Sokullu et al. [ 47 ] present an IoT-based smart home system for elderly people and people with partial memory loss. In [ 48 ], a health-smart home system that uses a renewable source of energy for monitoring was proposed.…”

Section: Related Workmentioning

confidence: 99%

Nonintrusive Fine-Grained Home Care Monitoring: Characterizing Quality of In-Home Postural Changes Using Bone-Based Human Sensing

Chen

Saiki

Nakamura

2020

Sensors

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In contrast to the physical activities of able-bodied people at home, most people who require long-term specific care (e.g., bedridden patients and patients who have difficulty walking) usually show more low-intensity slow physical activities with postural changes. Although the existing devices can detect data such as heart rate and the number of steps, they have been increasing the physical burden relying on long-term wearing. The purpose of this paper is to realize a noninvasive fine-grained home care monitoring system that is sustainable for people requiring special care. In the proposed method, we present a novel technique that integrates inexpensive camera devices and bone-based human sensing technologies to characterize the quality of in-home postural changes. We realize a local process in feature data acquisition once per second, which extends from a computer browser to Raspberry Pi. Our key idea is to regard the changes of the bounding box output by standalone pose estimation models in the shape and distance as the quality of the pose conversion, body movement, and positional changes. Furthermore, we use multiple servers to realize distributed processing that uploads data to implement home monitoring as a web service. Based on the experimental results, we conveyed our findings and advice to the subject that include where the daily living habits and the irregularity of home care timings needed improvement.

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“…IoT holds promise because of its importance in many commerce, industry, and education applications [ 5 ]. IoT devices can be used, for example, to automate activities in smart homes [ 6 ], traffic from vehicular networks [ 7 ], and health monitoring of elderly in their homes [ 8 ]. Therefore, an IoT system grants services to smart scenarios in different contexts, efficiently managing hardware, software, and communication resources to reduce costs in specific domains.…”

Section: Introductionmentioning

confidence: 99%

IoT Sensor Networks in Smart Buildings: A Performance Assessment Using Queuing Models

Santos

Soares

Nguyen

et al. 2021

Sensors

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Smart buildings in big cities are now equipped with an internet of things (IoT) infrastructure to constantly monitor different aspects of people’s daily lives via IoT devices and sensor networks. The malfunction and low quality of service (QoS) of such devices and networks can severely cause property damage and perhaps loss of life. Therefore, it is important to quantify different metrics related to the operational performance of the systems that make up such computational architecture even in advance of the building construction. Previous studies used analytical models considering different aspects to assess the performance of building monitoring systems. However, some critical points are still missing in the literature, such as (i) analyzing the capacity of computational resources adequate to the data demand, (ii) representing the number of cores per machine, and (iii) the clustering of sensors by location. This work proposes a queuing network based message exchange architecture to evaluate the performance of an intelligent building infrastructure associated with multiple processing layers: edge and fog. We consider an architecture of a building that has several floors and several rooms in each of them, where all rooms are equipped with sensors and an edge device. A comprehensive sensitivity analysis of the model was performed using the Design of Experiments (DoE) method to identify bottlenecks in the proposal. A series of case studies were conducted based on the DoE results. The DoE results allowed us to conclude, for example, that the number of cores can have more impact on the response time than the number of nodes. Simulations of scenarios defined through DoE allow observing the behavior of the following metrics: average response time, resource utilization rate, flow rate, discard rate, and the number of messages in the system. Three scenarios were explored: (i) scenario A (varying the number of cores), (ii) scenario B (varying the number of fog nodes), and (iii) scenario C (varying the nodes and cores simultaneously). Depending on the number of resources (nodes or cores), the system can become so overloaded that no new requests are supported. The queuing network based message exchange architecture and the analyses carried out can help system designers optimize their computational architectures before building construction.

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IoT supported smart home for the elderly

Cited by 56 publications

References 21 publications

Analysis of Smart Home Technology Acceptance and Preference for Elderly in Dubai, UAE

Analysis of Smart Home Technology Acceptance and Preference for Elderly in Dubai, UAE

Nonintrusive Fine-Grained Home Care Monitoring: Characterizing Quality of In-Home Postural Changes Using Bone-Based Human Sensing

IoT Sensor Networks in Smart Buildings: A Performance Assessment Using Queuing Models

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