In recent years, research on convolutional neural networks (CNN) and recurrent neural networks (RNN) in deep learning has been actively conducted. In order to provide more personalized and advanced functions in smart home services, studies on deep learning applications are becoming more frequent, and deep learning is acknowledged as an efficient method for recognizing the voices and activities of users. In this context, this study aims to systematically review the smart home studies that apply CNN and RNN/LSTM as their main solution. Of the 632 studies retrieved from the Web of Science, Scopus, IEEE Explore, and PubMed databases, 43 studies were selected and analyzed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. In this paper, we examine which smart home applications CNN and RNN/LSTM are applied to and compare how they were implemented and evaluated. The selected studies dealt with a total of 15 application areas for smart homes, where activity recognition was covered the most. This study provides essential data for all researchers who want to apply deep learning for smart homes, identifies the main trends, and can help to guide design and evaluation decisions for particular smart home services.

Globally, the percentage of older people in the general population is growing. Smart homes have the potential to help older adults to live independently and healthy, improving their quality of life, and relieving the pressure on the healthcare and social care systems. For that, we need to understand how older adults live and their needs. Thus, this study aims to analyze the residentially-based lifestyles (RBL) of older adults and segment them to compare and analyze the real needs of smart home functions for each group. To identify a person’s RBL, a questionnaire was designed to include questions about activities at home, social events, quality of life, etc. This study surveyed 271 older Koreans. As a result of the survey on RBL, five groups with different characteristics were clustered. Finally, each groups’ features and the differences in their needs for smart home functions were compared and analyzed. The priority of needed functions for each group was found to be significantly different. In a total of 26 smart home functions, there were meaningful differences in the needs for 16 functions among the groups. This study presents the results in South Korea, according to older adults’ RBL and their smart home needs.

Background: eHealth and telehealth play a crucial role in assisting older adults who visit hospitals frequently or who live in nursing homes and can benefit from staying at home while being cared for. Adapting to new technologies can be difficult for older people. Thus, to better apply these technologies to older adults’ lives, many studies have analyzed the acceptance factors for this particular population. However, there is not yet a consensual framework that can be used in further development and to search for solutions. Objective: This paper aims to present an integrated acceptance framework (IAF) for older users’ acceptance of eHealth based on 43 studies selected through a systematic review. Methods: We conducted a 4-step study. First, through a systematic review in the field of eHealth from 2010 to 2020, the acceptance factors and basic data for analysis were extracted. Second, we conducted a thematic analysis to group the factors into themes to propose an integrated framework for acceptance. Third, we defined a metric to evaluate the impact of the factors addressed in the studies. Finally, the differences among the important IAF factors were analyzed according to the participants’ health conditions, verification time, and year. Results: Through a systematic review, 731 studies were found in 5 major databases, resulting in 43 (5.9%) selected studies using the PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) methodology. First, the research methods and acceptance factors for eHealth were compared and analyzed, extracting a total of 105 acceptance factors, which were grouped later, resulting in an IAF. A total of 5 dimensions (ie, personal, user–technology relational, technological, service-related, and environmental) emerged, with a total of 23 factors. In addition, we assessed the quality of evidence and then conducted a stratification analysis to reveal the more appropriate factors depending on the health condition and assessment time. Finally, we assessed the factors and dimensions that have recently become more important. Conclusions: The result of this investigation is a framework for conducting research on eHealth acceptance. To elaborately analyze the impact of the factors of the proposed framework, the criteria for evaluating the evidence from the studies that have the extracted factors are presented. Through this process, the impact of each factor in the IAF has been presented, in addition to the framework proposal. Moreover, a meta-analysis of the current status of research is presented, highlighting the areas where specific measures are needed to facilitate eHealth acceptance.

BACKGROUND eHealth and telehealth play a crucial role in assisting older adults who visit hospitals frequently or who live in nursing homes and can benefit from staying at home while being cared for. Adapting to new technologies can be difficult for older people. Thus, to better apply these technologies to older adults’ lives, many studies have analyzed the acceptance factors for this particular population. However, there is not yet a consensual framework that can be used in further development and to search for solutions. OBJECTIVE This paper aims to present an integrated acceptance framework (IAF) for older users’ acceptance of eHealth based on 43 studies selected through a systematic review. METHODS We conducted a 4-step study. First, through a systematic review in the field of eHealth from 2010 to 2020, the acceptance factors and basic data for analysis were extracted. Second, we conducted a thematic analysis to group the factors into themes to propose an integrated framework for acceptance. Third, we defined a metric to evaluate the impact of the factors addressed in the studies. Finally, the differences among the important IAF factors were analyzed according to the participants’ health conditions, verification time, and year. RESULTS Through a systematic review, 731 studies were found in 5 major databases, resulting in 43 (5.9%) selected studies using the PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) methodology. First, the research methods and acceptance factors for eHealth were compared and analyzed, extracting a total of 105 acceptance factors, which were grouped later, resulting in an IAF. A total of 5 dimensions (ie, personal, user–technology relational, technological, service-related, and environmental) emerged, with a total of 23 factors. In addition, we assessed the quality of evidence and then conducted a stratification analysis to reveal the more appropriate factors depending on the health condition and assessment time. Finally, we assessed the factors and dimensions that have recently become more important. CONCLUSIONS The result of this investigation is a framework for conducting research on eHealth acceptance. To elaborately analyze the impact of the factors of the proposed framework, the criteria for evaluating the evidence from the studies that have the extracted factors are presented. Through this process, the impact of each factor in the IAF has been presented, in addition to the framework proposal. Moreover, a meta-analysis of the current status of research is presented, highlighting the areas where specific measures are needed to facilitate eHealth acceptance.

Background: Globally, the older population is increasing rapidly, becoming one of the most significant demographic trends of the 21st century. This growth poses important social, health, and technological challenges for societies that must adapt their environments and services to promote independent and healthy aging. In Spain, the population aged 65 years and older reached 18% of the total population in 2020, and projections indicate that this proportion will continue to rise in the coming decades. Within this context, smart homes have emerged as one of the most promising avenues to support aging in place and improve the quality of life. Smart homes encompass a wide variety of functions, including environmental control, safety monitoring, communication, and other assistive technologies, that may help older people stay healthy, safe, and independent in their own homes. However, older people are not a homogeneous group. Their lifestyles, health conditions, and technological experiences differ substantially, which means that, as with any assistive technology, smart home functions must match the real and perceived needs of the target users to ensure acceptance, adoption, and long-term use. Objective: In this study, as a step forward toward the adaptability of smart home technology, we present a method to analyze the practical needs of smart home functions for older people. Specifically, we aim to understand the Spanish older population’s readiness and needs for smart homes and to provide insights that can guide the design of more adaptive and user-centered solutions. Methods: We conducted an online survey focusing on residentially based lifestyles, health conditions, and preferences for smart home functions, targeting older adults living in Spain. The survey collected information about participants’ demographic profiles, daily activities, health self-assessment, and attitudes toward technology. A total of 102 valid responses were analyzed. We then classified the older adults according to their residentially based lifestyles using clustering techniques and analyzed the preferences and needs for smart home functions in each identified group. Results: Four clusters emerged based on the information provided by the participants: (1) high quality of life and independent life, (2) poor quality of life, (3) social-centered life, and (4) creative and personal-centered hobbies at home. On the basis of this classification, we explored each group’s specific needs for smart homes and estimated their readiness to embrace different aspects of technology. As a result, the top-priority smart home functions for each group were identified and compared. Conclusions: This research contributes to understanding the practical user needs of smart homes as assistive technologies for older people. It provides a methodological approach to anticipate and prioritize functions according to user characteristics, supporting the development of personalized, adaptive, and more acceptable smart home solutions for aging populations.

BACKGROUND Globally, the elderly population is increasing rapidly. In Spain, the population aged 65 years and older reached 18% of the total population in 2020. Smart homes encompass a wide variety of functions, including assistive technologies that may help older people stay healthy and independent. Older people are not a homogeneous group and, as with any assistive technology, the functions must match the real needs of the target users to ensure adoption. OBJECTIVE In this study, as a step forward towards the adaptability of smart home technology, we present a method to analyze the practical needs of smart home functions for older people. An online survey was conducted on residentially-based lifestyles, health conditions, and smart home function preferences, targeting older adults in Spain. METHODS A total of 102 valid responses were analyzed. Then, we classified the elderly group according to their residentially-based lifestyles, and analyzed the preferences and needs of smart home functions for each group. RESULTS Four clusters emerged according to the information provided by the older adults who participated in the survey: 1) High quality of life and independent life; 2) Poor quality of life; 3) Social centered life; 4) Creative and personal-centered hobbies at home. Based on this, each particular group’s needs for smart homes were discovered and we could estimate how prepared each group is to embrace different aspects of technology. As a result of the analysis, the top priority smart home functions for each group were identified. CONCLUSIONS This research helps to understand the practical user needs of smart homes as an assistive technology for older people and provides a method to anticipate and prioritize the required functions for a specific user according to the characterization of the cluster it belongs.