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The possibilities of the Ambient Assisted Living (AAL)/Enhanced Living Environments (ELE) concept in the environment of a smart home were investigated to improve accessibility and improve the quality of life of a person with disabilities. This paper focuses on the concept of predictive information for a person with disabilities in a smart home environment concept where artificial intelligence (AI) and machine learning (ML) systems use data on the user's preferences, habits, and possible incident situations. A conceptual mathematical model is proposed, the purpose of which is to provide predictive user information from defined data sets. This paper defines the taxonomy of communication technologies, devices, and sensors in the environment of the user's smart home and shows the interaction of all elements in the environment of the smart home. Through the integration of assistive technologies, it is possible to adapt the home to users with diverse types of disabilities and needs. The smart home environment with diverse types of sensors whose data are part of sets defined by a mathematical model is also evaluated. The significance of establishing data sets as a foundation for future research, the development of ML models, and the utilization of AI is highlighted in this paper. [ABSTRACT FROM AUTHOR]

AgeTech refers to a growing sector that is advancing the use of technologies, such as information and communication technologies (ICTs), mobile technologies, robotics, wearables and smart home systems to enhance the lives of older adults. Although AgeTech can be seen as an opportunity for empowering older people and enhance their overall quality of life, crucial ethical issues have to be addressed. The articles in this topical collection focus on these and other ethical questions, particularly in respect to key emerging technologies of AI and robotics. The overall aim is to explore the multifaceted ethical landscape of emerging AgeTech and to provide frameworks and strategies for ethically-appropriate technologies that support the health, well-being, and quality of life of older adults. [ABSTRACT FROM AUTHOR]

The idea of introducing a robot into an Ambient Assisted Living (AAL) environment to provide additional services beyond those provided by the environment itself has been explored in numerous projects. Moreover, new opportunities can arise from this symbiosis, which usually requires both systems to share the knowledge (and not just the data) they capture from the context. Thus, by using knowledge extracted from the raw data captured by the sensors deployed in the environment, the robot can know where the person is and whether he/she should perform some physical exercise, as well as whether he/she should move a chair away to allow the robot to successfully complete a task. This paper describes the design of an Ambient Assisted Living system where an IoT scheme and robot coexist as independent but connected elements, forming a cyber-physical system-of-systems architecture. The IoT environment includes cameras to monitor the person's activity and physical position (lying down, sitting...), as well as non-invasive sensors to monitor the person's heart or breathing rate while lying in bed or sitting in the living room. Although this manuscript focuses on how both systems handle and share the knowledge they possess about the context, a couple of example use cases are included. In the first case, the environment provides the robot with information about the positions of objects in the environment, which allows the robot to augment the metric map it uses to navigate, detecting situations that prevent it from moving to a target. If there is a person nearby, the robot will approach them to ask them to move a chair or open a door. In the second case, even more use is made of the robot's ability to interact with the person. When the IoT system detects that the person has fallen to the ground, it passes this information to the robot so that it can go to the person, talk to them, and ask for external help if necessary. [ABSTRACT FROM AUTHOR]

To create an effective Ambient Assisted Living (AAL) system that supports the daily activities of patients or the elderly, it is crucial to accurately detect and differentiate user actions to determine the necessary assistance. Traditional intrusive methods, such as wearable or object-attached devices, can interfere with the natural behavior of patients and may lead to resistance. Furthermore, non-intrusive systems that rely on video or sound data processed by servers or the cloud can generate excessive data traffic and raise concerns about the security of personal information. In this study, we developed an edge-based real-time system for detecting Activities of Daily Living (ADL) using ambient noise. Additionally, we introduced an online post-processing method to enhance classification performance and extract activity events from noisy sound in resource-constrained environments. The system, tested with data collected in a living space, achieved high accuracy in classifying ADL-related behaviors in continuous events and successfully generated user activity logs from time-series sound data, enabling further analyses such as ADL assessments. Future work will focus on enhancing detection accuracy and expanding the range of detectable behaviors by integrating the activity logs generated in this study with additional data sources beyond sound. [ABSTRACT FROM AUTHOR]

As the proportion of elderly individuals in developed countries continues to rise globally, addressing their healthcare needs, particularly in preserving their autonomy, is of paramount concern. A growing body of research focuses on Ambient Assisted Living (AAL) systems, aimed at alleviating concerns related to the independent living of the elderly. This systematic review examines the literature pertaining to fall detection and Human Activity Recognition (HAR) for the elderly, two critical tasks for ensuring their safety when living alone. Specifically, this review emphasizes the utilization of Deep Learning (DL) approaches on computer vision data, reflecting current trends in the field. A comprehensive search yielded 2,616 works from five distinct sources, spanning the years 2019 to 2023 (inclusive). From this pool, 151 relevant works were selected for detailed analysis. The review scrutinizes the employed DL models, datasets, and hardware configurations, with particular emphasis on aspects such as privacy preservation and real-world deployment. The main contribution of this study lies in the synthesis of recent advancements in DL-based fall detection and HAR for the elderly, providing insights into the state-of-the-art techniques and identifying areas for further improvement. Given the increasing importance of AAL systems in enhancing the quality of life for the elderly, this review serves as a valuable resource for researchers, practitioners, and policymakers involved in developing and implementing such technologies. [ABSTRACT FROM AUTHOR]

Indoor air pollution (IAP) is a prevalent issue, and in the absence of any concrete and stringent guidelines, particularly in developing countries the concern becomes graver. Technological strategies like the use of the Internet of Things (IoT) and cloud computing have been explored for real‐time monitoring and these interventions may be investigated to improve indoor air quality (IAQ) and human health. This review article explores the prospects of IoT and smart environments for the improvement of indoor living conditions through automation. Some specific interference like sensors, air pollution simulations and modeling, the concept of smart ventilation, and fuzzy logic controllers (FLC) have been elaborated with pieces of evidence taken from previously published studies in leading indexing databases. Furthermore, some automated health risk assessment tools like the Human exposure model (HEM), Integrated Fuzzy‐stochastic modeling (IFSM), and proximity and interpolation models have also been described. The findings suggested that IoT‐based gadgets require ambient intelligence capabilities for ambient assisted living (AAL). The studies showed that innovations in technology like sensors and modeling techniques may yield crucial information on pollution exposure enabling long‐term and sustainable predictions. However, efficient AAL systems may also face challenges in designing interfaces, usability, and accessibility. Although IoT can aid in mimicking real‐world scenarios, nevertheless its‐micro spatial scale application requires thorough investigation for reliable information extraction. [ABSTRACT FROM AUTHOR]

The article reviews recent research on new technologies for assisting the elderly based on Artificial Intelligence: Utilities for smart houses and Ambient Assisted Living, wearable and monitoring devices, virtual and augmented reality, and assistive robotics.These smart technological tools can reduce the isolation of older adults by assisting them in their daily activities, but without completely replacing direct interventions, and requiring human support to facilitate interaction.In determining the effectiveness of assistive technologies, older people's perceptions of them, acceptability, and willingness to use them are key variables. Also relevant is the perception by caregivers and healthcare professionals, and the socio-cultural dimension of acceptability.Finally, the paper presents the ethical issues related to the use of technologies with the elderly population, to ensure the autonomy, safety, and dignity of the person. Some suggestions are offered for developers of new technologies for older adults.In conclusion, the challenges for the future of assistive technologies are highlighted, so that they can best be used to assist large and differentiated elderly populations.

Human Activity Recognition (HAR), alongside Ambient Assisted Living (AAL), are integral components of smart homes, sports, surveillance, and investigation activities. To recognize daily activities, researchers are focusing on lightweight, cost-effective, wearable sensor-based technologies as traditional vision-based technologies lack elderly privacy, a fundamental right of every human. However, it is challenging to extract potential features from 1D multi-sensor data. Thus, this research focuses on extracting distinguishable patterns and deep features from spectral images by time-frequency-domain analysis of 1D multi-sensor data. Wearable sensor data, particularly accelerator and gyroscope data, act as input signals of different daily activities, and provide potential information using time-frequency analysis. This potential time series information is mapped into spectral images through a process called use of 'scalograms', derived from the continuous wavelet transform. The deep activity features are extracted from the activity image using deep learning models such as CNN, MobileNetV3, ResNet, and GoogleNet and subsequently classified using a conventional classifier. To validate the proposed model, SisFall and PAMAP2 benchmark datasets are used. Based on the experimental results, this proposed model shows the optimal performance for activity recognition obtaining an accuracy of 98.4% for SisFall and 98.1% for PAMAP2, using Morlet as the mother wavelet with ResNet-101 and a softmax classifier, and outperforms state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Falling is a significant threat for old people and disabled persons. Therefore, it is necessary to develop an automated fall detection system to make them more independent and to mitigate the physical and psychological consequences of falls. Such a system can lower the death rate among dependent people who live alone. This paper presents a review of vision-based techniques, algorithms and databases used in detecting falls. Recently proposed systems are analyzed and compared. The literature review underscores the promising potential of camera-based fall detection systems and highlights areas for further research and development. [ABSTRACT FROM AUTHOR]

Information and Communication Technology can turn older adults' homes into smart living environments, decreasing the need for support and improving life quality. However, new technology has high uncertainty in terms of acceptance. Co-creation approaches are proposed to reach high acceptability, usability and satisfaction, but performance evaluations are sparse. This paper aimed at presenting the evaluation of a hybrid approach of Agile co-creation framework, specially designed for large, distributed consortia and cross-national collaborations. The new framework, leveraging the concepts of Design Thinking, Lean start-up, and SCRUM Agile, was tested in the H2020 CAPTAIN project. A network of stakeholders, built across five Living Labs, allowed the participatory design of the CATAIN technology through multiple iterations. A pre-post assessment questionnaire was administered to team components and used to compare partners' perspective and satisfaction about CAPTAIN's and other projects methodologies. A longitudinal assessment throughout the iterations, using actionable metrics, monitored team morale and stakeholders' engagement and satisfaction. The CAPTAIN development process resulted in a stronger involvement of the partners and a in better perception of requirements elicitation, design and development process than previous projects. Team morale was good and steady along the development iterations. Stakeholders increased from 91 to 132 along the iterations and more than 90% were satisfied. The tested framework went beyond usual participatory designs. It required stronger technical coordination, activity planning and communication among technical and pilot partners, but resulted in high satisfaction and engagement of both team members and stakeholders, showing its validity as a development approach for H2020 projects. [ABSTRACT FROM AUTHOR]

Research and development in the area of Ambient Assisted Living has been undertaken for many years. The aim of Ambient assisted Living is to manage the risks due to aging and living alone so that the older person can remain in their familiar home environment longer. Despite the many years of research and development there is apparently only limited uptake in the real world. Some researchers have suggested that insufficient focus on user needs, focus on the technology not the user, little concern for ethical issues in deployment, and lack of user-centered system design are causes for the limited uptake. These and other reasons should be understood else it is likely that when further systems are developed they too will not be accepted. Subsequently, the aim of the present research is to explore the issues that affected the uptake of the Assistive Technology and Ambient Assisted Living systems that are currently in Australian homes. Due to privacy concerns and commercial confidentiality issues, this has proven difficult, so an innovative approach is being used to obtain data. Some preliminary information has emerged from the ongoing data gathering that indicates that specific Assistive Technology is being used but little consideration has been given to Ambient Assisted Living. This appears due to differing perceptions of usefulness. To improve the perception of Ambient Assisted Living systems usefulness we are developing simple devices that target specific needs raised by potential users. Through using such devices, appreciation of the usefulness of comprehensive home Ambient Assisted Living systems may increase. The devices are specifically low-cost to encourage trial and are designed with a user-centered approach that incorporates important earlier researchers' findings regarding older people and technology. [ABSTRACT FROM AUTHOR]

Ambient assisted living (AAL) is a groundbreaking approach that harnesses the power of smart technology to offer all-encompassing care and support for elderly and differently abled individuals in their day-to-day lives. Progressive innovation in AAL solutions can facilitate and support day-to-day routines, expanding the time they can live autonomously and supporting proficiency. This research mainly analyzes AAL's significant role in tending to the exceptional difficulties these populations face. AAL frameworks incorporate an array of sensors, gadgets, and intelligent calculations that help monitor current circumstances and exercises, empowering early recognition of peculiarities, fall counteraction, and customized help. This research introduces a novel attention transfer learning-based crossover chimp (ATL-CC) algorithm for AAL, which combines crossover-based chimp optimization with a transformer-based model for transfer learning, integrating an attention mechanism. The ATL-CC algorithm aims to enhance activity recognition and classification within AAL environments. Precision, accuracy, recall, root mean square error, and F1-score are evaluated, where accuracy attains the value of 98.9%, precision attains the value of 97.4%, recall attains the value of 98%, and F1-score attains the value of 96%. Overall, AAL arises as a promising arrangement that upholds the deprived and advances respect, independence, and inclusivity in maturing and various societies. [ABSTRACT FROM AUTHOR]

The ubiquity of sensors in smart-homes facilitates the support of independent living for older adults and enables cognitive assessment. Notably, there has been a growing interest in utilizing movement traces for identifying signs of cognitive impairment in recent years. In this study, we introduce an innovative approach to identify abnormal indoor movement patterns that may signal cognitive decline. This is achieved through the non-intrusive integration of smart-home sensors, including passive infrared sensors and sensors embedded in everyday objects. The methodology involves visualizing user locomotion traces and discerning interactions with objects on a floor plan representation of the smart-home, and employing different image descriptor features designed for image analysis tasks and synthetic minority oversampling techniques to enhance the methodology. This approach distinguishes itself by its flexibility in effortlessly incorporating additional features through sensor data. A comprehensive analysis, conducted with a substantial dataset obtained from a real smart-home, involving 99 seniors, including those with cognitive diseases, reveals the effectiveness of the proposed functional prototype of the system architecture. The results validate the system's efficacy in accurately discerning the cognitive status of seniors, achieving a macro-averaged F1-score of 72.22% for the two targeted categories: cognitively healthy and people with dementia. Furthermore, through experimental comparison, our system demonstrates superior performance compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The measurement of Functional Reach Test (FRT) is a widely used assessment tool in various fields, including physical therapy, rehabilitation, and geriatrics. This test evaluates a person's balance, mobility, and functional ability to reach forward while maintaining stability. Recently, there has been a growing interest in utilizing sensor-based systems to objectively and accurately measure FRT results. This systematic review was performed in various scientific databases or publishers, including PubMed Central, IEEE Explore, Elsevier, Springer, the Multidisciplinary Digital Publishing Institute (MDPI), and the Association for Computing Machinery (ACM), and considered studies published between January 2017 and October 2022, related to methods for the automation of the measurement of the Functional Reach Test variables and results with sensors. Camera-based devices and motion-based sensors are used for Functional Reach Tests, with statistical models extracting meaningful information. Sensor-based systems offer several advantages over traditional manual measurement techniques, as they can provide objective and precise measurements of the reach distance, quantify postural sway, and capture additional parameters related to the movement. [ABSTRACT FROM AUTHOR]

This paper reports the description of a multi-sensor platform able to automatically assess the level of physical activity and sedentary time of older adults. The platform has a hierarchical network topology, compound by N detector nodes managing several ambient sensor nodes and one detector node that manages a wearable sensor node. The system provides also one coordinator node that receives high-level reports from detector nodes. The idea of using heterogeneous sensors is motivated by the fact that in this way we expands the number of end-users, as they may accept only a type of sensor technology. The objective assessment was conducted through two main algorithmic steps: (1) recognition of well-defined set of human activities, detected by a 3D vision sensor (ambient node) and a smart garment (wearable sensor node), and (2) estimation of a physiological measure, that is (MET)-minutes. Results obtained in terms of activity recognition (and subsequent physical activity/sedentary time assessment) showed that the integrated version of the platform performs better than each single sensor technology with an overall accuracy obtained using simultaneously data provided from both sensory technologies that is about 5% higher of single sub-system, thus confirming the advantage in using a coordinator node. Finally, an added value of this work is the capability of the platform in providing a sensing invariant interface (i.e., abstracted from any specific sensing technology), since the use of the activities enables the integration of a wide set of devices, providing that they are able to reproduce the same set of features. [ABSTRACT FROM AUTHOR]

Abstract Background Ageing in place is a common desire among older adults and people in need of care. Accessible housing and ambient assisted living (AAL) technologies can help to live independently at home. However, they cannot replace the human support network of informal caregivers, healthcare professionals and social workers. The needs of these stakeholders should be considered and analysed in order to develop user-friendly and acceptable (digital) solutions for ageing in place while supporting human support networks in fulfilling their roles. This paper presents the first step for a comprehensive multi-level needs analysis within the framework of an user-centered design thinking approach. Methods Guideline-based interviews were conducted with healthcare professionals, social workers and an informal caregiver to collect data about the needs of older adults as well as people in need of care, and their human support networks. Results The call for more information that is easier to find is a common desire of the three groups. There is agreement on system-based communication and orientation problems, the existence of physical and psychological stress exacerbated by a lack of human resources, the desire for personalised care, the need to feel safe and supported in emergencies, and the need for advice and help with administrative tasks. Overall, the needs of one group are closely linked to those of the other. Conclusion Stakeholder selection and diversity are decisive for findings about ageing in place. The overlaps between the stakeholders’ needs offer chances and challenges at the same time for the development of user-friendly, acceptable (digital) solutions and products that support ageing in place.

The Internet of Things (IoT) has revolutionized numerous sectors, with healthcare being a prominent beneficiary. One key area of advancement is Ambient Assisted Living (AAL), which leverages Information and Communication Technologies (ICT) like IoT to improve the quality of life for vulnerable populations, particularly seniors. However, despite efforts to tailor solutions to the needs of these environments, the prevailing approach often becomes overly focused on intricate implementation details rather than the core characteristics of the problem domain. This narrow focus inhibits generalization and leads to unsustainable solutions due to these environments’ diverse, dynamic, and scalable nature. In response, emerging technology paradigms, such as Fog Computing (FC), have emerged to enhance performance and other quality aspects. FC enables the deployment of distributed, latency-aware applications and services. Therefore, this paper introduces FOGAAL, a Domain-Specific Language (DSL) tool crafted to design AAL architectures, including the FC advantages. To demonstrate FOGAAL’s practical applicability, this contribution uses FOGAAL to build an AAL architecture for a real scenario; in addition, it has an empirical evaluation employing the Technology Acceptance Model (TAM) to gauge user perceptions while utilizing the tool for modeling AAL Architectures. The evaluation, conducted as a quasi-experiment, underscore the tool’s alignment with requirements and its potential for adoption, as evidenced by software engineers who have expressed their intention to use FOGAAL when creating architecture for AAL environments.

The critical importance of technological innovation in home care for older adults is indisputable. Less well understood is the question of how to measure its performance and impact on the delivery of healthcare to older adults who are living with chronic illness and disability. Knowing how well digital technologies, such as smartphones, tablets, wearable devices, and Ambient Assisted Living Technologies (AAL) systems “work” should certainly include assessing their impact on older adults’ health and ability to function in daily living but that will not guarantee that it will necessarily be adopted by the user or implemented by a healthcare facility or the healthcare system. Technology implementation is a process of planned and guided activities to launch, introduce and support technologies in a certain context to innovate or improve healthcare, which delivers the evidence for adoption and upscaling a technology in healthcare practices. Factors in addition to user acceptance and clinical effectiveness require investigation. Failure to appreciate these factors can result in increased likelihood of technology rejection or protracted procurement decision at the “adoption decision” stage or delayed or incomplete implementation or discontinuance (following initial adoption) during implementation. The aim of our research to analyze research studies on the effectiveness of digital health technologies for older adults to answer the question, “How well do these studies address factors that affect the implementation of technology?” We found common problems with the conceptualization, design, and methodology in studies of digital technology that have contributed to the slow pace of implementation in home care and long-term care. We recommend a framework for improving the quality of research in this critical area.Systematic Review Registration:https://archive.org/details/osf-registrations-f56rb-v1, identifier osf-registrations-f56rb-v1.

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%. [ABSTRACT FROM AUTHOR]

Background: Ageing in place is a common desire among older adults and people in need of care. Accessible housing and ambient assisted living (AAL) technologies can help to live independently at home. However, they cannot replace the human support network of informal caregivers, healthcare professionals and social workers. The needs of these stakeholders should be considered and analysed in order to develop user-friendly and acceptable (digital) solutions for ageing in place while supporting human support networks in fulfilling their roles. This paper presents the first step for a comprehensive multi-level needs analysis within the framework of an user-centered design thinking approach. Methods: Guideline-based interviews were conducted with healthcare professionals, social workers and an informal caregiver to collect data about the needs of older adults as well as people in need of care, and their human support networks. Results: The call for more information that is easier to find is a common desire of the three groups. There is agreement on system-based communication and orientation problems, the existence of physical and psychological stress exacerbated by a lack of human resources, the desire for personalised care, the need to feel safe and supported in emergencies, and the need for advice and help with administrative tasks. Overall, the needs of one group are closely linked to those of the other. Conclusion: Stakeholder selection and diversity are decisive for findings about ageing in place. The overlaps between the stakeholders' needs offer chances and challenges at the same time for the development of user-friendly, acceptable (digital) solutions and products that support ageing in place. [ABSTRACT FROM AUTHOR]

In Italy, many people aged over 65 cannot live independently, causing an overall decrease in their quality of life and a need for social and health care. Due to the lack of both formal and informal caregivers, technological solutions become of paramount importance in this scenario. This article describes the user-centered development of the GUARDIAN ecosystem, consisting of a social robot integrated with two mobile applications which aim to monitor, coach, and keep the older user company in order to prolong his/her independence at home. In particular, the advancements from the alpha to the beta prototype of the ecosystem are described, achieved through the feedback collected from 41 end users—older people and their carers—that have tested the system for 6 weeks. By enhancing human–robot interaction, a positive improvement in terms of usability and acceptability of the system was retrieved. However, to increase the perceived usefulness and the impact on older users' lives, it is necessary to make the entire system more customizable, and more capable in providing support for daily activities. [ABSTRACT FROM AUTHOR]

Background: AgeTech (technology for older people) offers digital solutions for older adults supporting aging in place, including digital health, assistive technology, Internet of Things, medical devices, robotics, wearables, and sensors. This study underscores the critical role of standards and guidelines in ensuring the safety and effectiveness of these technologies for the health of older adults. As the aging demographic expands, the focus on robust standards becomes vital, reflecting a collective commitment to improving the overall quality of life for older individuals through thoughtful and secure technology integration. Objective: This scoping review aims to investigate the current state of standards and guidelines applied in AgeTech design and development as reported in academic literature. We explore the existing knowledge of these standards and guidelines and identify key gaps in the design and development of AgeTech guidelines and standards in scholarly publications. Methods: The literature review adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Searches were carried out across multiple databases, including Scopus, IEEE, PubMed, Web of Science, EBSCO, CINAHL, Cochrane, and Google Scholar, using a search string incorporating concepts such as "older people," "technology," and "standards or guidelines." Alternative terms, Boolean operators, and truncation were used for comprehensive coverage in each database. The synthesis of results and data analysis involved both quantitative and qualitative methods. Results: Initially, 736 documents were identified across various databases. After applying specific inclusion and exclusion criteria and a screening process, 58 documents were selected for full-text review. The findings highlight that the most frequently addressed aspect of AgeTech standards or guidelines is related to "design and development," constituting 36% (21/58) of the literature; "usability and user experience" was the second most prevalent aspect, accounting for 19% (11/58) of the documents. In contrast, "privacy and security" (1/58, 2%) and "data quality" (1/58, 2%) were the least addressed aspects. Similarly, "ethics," "integration and interoperability," "accessibility," and "acceptance or adoption" each accounted for 3% (2/58) of the documents. In addition, a thematic analysis identified qualitative themes that warrant further exploration of variables. Conclusions: This study investigated the available knowledge regarding standards and guidelines in AgeTech design and development to evaluate their current status in academic literature. The substantial focus on assistive technologies and ambient assisted living technologies confirmed their vital role in AgeTech. The findings provide valuable insights for interested parties and point to prioritized areas for further development and research in the AgeTech domain. [ABSTRACT FROM AUTHOR]