Stakeholder needs assessment for developing ageing in place solutions - a qualitative study.

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.

Examining pain, function, behavioral symptoms and associations with engagement in meaningful activity for residents with dementia in assisted living.

OBJECTIVES: The purpose of this study was to examine factors associated with engagement in meaningful activity among residents with dementia in assisted living. We hypothesized that greater functional independence, less pain, and lower behavioral and psychological symptom severity would be associated with higher engagement in meaningful activity after controlling for residents' age, gender, comorbidities, and cognition. Understanding factors associated with engagement in meaningful activity can help to inform strategies for optimizing engagement among residents with dementia in assisted living. METHOD: This descriptive study used baseline data from a randomized controlled trial, Meaningful Activity for Managing Behavioral Symptoms of Distress (MAC-4-BSD). Linear regression was used to examine factors associated with engagement in meaningful activity. RESULTS: A total of 71 residents from 5 assisted living settings were included in the sample. Most participants were female (n = 52, 73%), White (n = 62, 87%), and mean age was 85 years old (SD = 8.2). Controlling for age, gender, comorbidities, and cognition, pain was significantly associated with engagement in meaningful activity (b= -2.09, p < 0.05). There were no associations found between function and behavioral symptoms with engagement in meaningful activity. CONCLUSION: Findings from this study show that pain is a significant factor that is negatively associated with residents' engagement in meaningful activity. Ongoing research is needed to help improve pain management for residents with dementia in assisted living and support their engagement in meaningful activity.

Measuring older adults' wellbeing when transitioning into assisted living facilities: a confirmatory factor analysis of the Mueller assessment of transition (MAT).

OBJECTIVES: Older adults' wellbeing during the transition into an assisted living facility (ALF) is not well understood and may influence their wellbeing. The Mueller Assessment of Transition (MAT) was created to measure the impact of transition on older adults' wellbeing. Early developmental testing of the MAT revealed a hypothesized model with two constructs (adjustment strategies and constraints to wellbeing). Therefore, the purpose of this study was to confirm the factor structure of the MAT with a representative sample of older adults transitioning into ALFs. METHODS: In a nationwide sample, 108 older adult participants completed the MAT to measure wellbeing when relocating into ALFs. Confirmatory factor analysis (CFA) assessed the structural validity of the MAT. Internal consistency was evaluated, and chi-square tests of association for regional differences in MAT scores were also conducted. RESULTS: The CFA produced strong fit indices to confirm the hypothesized 2-factor (constraints to wellbeing and adjustment strategies) model of the MAT. Cronbach's alpha for the internal consistency was 0.784 and chi-square test indicated no significant regional differences. CONCLUSION: The MAT was established as a valid and reliable standardized assessment. Implications for using the MAT as a tool to measure older adults' wellbeing and future research are discussed.

The Impact of Dual-Tasks and Disease Severity on Posture, Gait, and Functional Mobility among People Living with Dementia in Residential Care Facilities: A Pilot Study.

Gait speed and timed-up-and-go (TUG) predict cognitive decline, falls, and mortality. Dual-tasks may be useful in cognitive screening among people living with dementia (PWD), but more evidence is needed. This cross-sectional study aimed to compare single- and dual-task performance and determine the influence of dementia severity on dual-task performance and interference. Thirty PWD in two residential care facilities (Age: 81.3 ± 7.1 years; Montreal Cognitive Assessment: 10.4 ± 6.0 points) completed two trials of single- (feet apart) and dual-task posture (feet apart while counting backward), single- (walk 4 m) and dual-task gait (walk 4m while naming words), and single- (timed-up-and-go (TUG)), and dual-task functional mobility (TUG while completing a category task) with APDM inertial sensors. Dual-tasks resulted in greater sway frequency, jerk, and sway area; slower gait speed; greater double limb support; shorter stride length; reduced mid-swing elevation; longer TUG duration; reduced turn angle; and slower turn velocity than single-tasks (ps < 0.05). Dual-task performance was impacted (reduced double limb support, greater mid-swing elevation), and dual-task interference (greater jerk, faster gait speed) was related to moderate-to-severe compared to mild PWD. Moderate-to-severe PWD had poorer dynamic stability and a reduced ability to appropriately select a cautious gait during dual-tasks than those with mild PWD, indicating the usefulness of dual-tasks for cognitive screening.

Unobtrusive Cognitive Assessment in Smart-Homes: Leveraging Visual Encoding and Synthetic Movement Traces Data Mining.

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.

A Robust Deep Feature Extraction Method for Human Activity Recognition Using a Wavelet Based Spectral Visualisation Technique.

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.

"Qualitative evaluation of a digital software solution for documentation and training in 24-hour home care".

Owing to its cost-efficiency, 24-hour home care is increasing and represents a cornerstone in the care of elder people in Austria. A software solution to support 24-hour caregivers in their daily routine was developed facilitating a user-centered design process. It includes electronic documentation, an e-learning platform, emergency management, and a communication tool. To evaluate the usability and acceptance of the developed software solution, a qualitative survey including focus groups (FG), a group interview (GI), and a usability walkthrough (UW) were conducted. This paper describes the findings of the qualitative survey which indicate that the e-learning platform as well as the e-documentation support 24-hour caregivers in their daily work with their clients. Participants with less technical affinity needed support to use the tool proficiently. 24-hour caregivers appreciate the fact that the solution unites all the needed information in one place.

A Mixed Methods Feasibility Study of Machine-Based Resistance Training With Prefrail Older Adults in Residential Care: The Keeping Active in Residential Elderly Trial II.

Physical activity is an effective, proactive intervention to reduce or reverse frailty and functional decline. However, uncertainty exists about the feasibility and impact of resistance training on multidimensional health in prefrail older adults in residential care. This mixed methods feasibility study assessed practicability with limited efficacy testing on health and functional outcomes. Eleven prefrail older adults participated in a 6-week progressive resistance training protocol three times per week. The intervention and measures were found to be appropriate and acceptable by those who completed the trial, with participants self-reporting improved well-being, mood, and function. Analysis identified several barriers to recruitment, including prior commitments, seasonal impact, and session timing, and offered potential solutions with further recommendations for program refinement prior to a definitive randomized controlled trial. These findings add to our understanding of prefrail older adults' preferences regarding participation in physical activity research and the perceived benefits of resistance training. This trial was registered with ClinicalTrials.gov: NCT03141879.

Healthy and Happy? An Ethical Investigation of Emotion Recognition and Regulation Technologies (ERR) within Ambient Assisted Living (AAL).

Ambient Assisted Living (AAL) refers to technologies that track daily activities of persons in need of care to enhance their autonomy and minimise their need for assistance. New technological developments show an increasing effort to integrate automated emotion recognition and regulation (ERR) into AAL systems. These technologies aim to recognise emotions via different sensors and, eventually, to regulate emotions defined as "negative" via different forms of intervention. Although these technologies are already implemented in other areas, AAL stands out by its tendency to enable an inconspicuous 24-hour surveillance in the private living space of users who rely on the technology to maintain a certain degree of independence in their daily activities. The combination of both technologies represents a new dimension of emotion recognition in a potentially vulnerable group of users. Our paper aims to provide an ethical contextualisation of the novel combination of both technologies. We discuss different concepts of emotions, namely Basic Emotion Theory (BET) and the Circumplex Model of Affect (CMA), that form the basis of ERR and provide an overview over the current technological developments in AAL. We highlight four ethical issues that specifically arise in the context of ERR in AAL systems, namely concerns regarding (1) the reductionist view of emotions, (2) solutionism as an underlying assumption of these technologies, (3) the privacy and autonomy of users and their emotions, (4) the tendency of machine learning techniques to normalise and generalise human behaviour and emotional reactions.

Effects of mindfulness-based stress reduction programs on Nurses' perceived stress levels.

INTRODUCTION: Poor quality of care and patient safety, adverse patient outcomes, high rates of burnout and turnover, and increased job dissatisfaction are all linked to uncontrolled stress among nurses. Unmanaged nurse stress can lead to disorganized thinking, decreased ability to focus and concentrate, and can put patients at risk. The purpose of the integrative review was to determine the effects of a mindfulness-based stress reduction (MBSR) program to reduce the perceived stress levels of nurses. RESEARCH METHODOLOGY: Included was a search of databases using key search terms and inclusion and exclusion criteria. Relevant peer-reviewed articles within the last five years were appraised to identify similarities and differences. This paper discusses the implications of unresolved stress on nurses and patients they care for including quality and safety of patient care, cost of nurse turnover rates related to stress, and poor work efficiency. RESULTS AND DISCUSSION: Literature results suggest that implementation of a mindfulness-based stress reduction (MBSR) program leads to a reduction in perceived stress levels among nurses resulting in improved patient outcomes, increased job satisfaction, and reduction of organizational costs associated with the hiring and onboarding process. CONCLUSIONS AND FURTHER RECOMMENDATIONS: It is the conclusion of this integrative review that implementation of a MBSR program leads to improvements in stress reduction and indirectly positively impacts patient outcomes. It is recommended that organizations implement a formal eight-week mindfulness-based stress reduction program. MBSR IMPLEMENTATION RESOURCES: There are no conflicts of interest or financial gains for the recommendations for MBSR implementation resources. These resources are solely provided for additional learning and implementation purposes. There are both free resources and for purchase resources available that guides implementation and operationalization of the MBSR program.

The Impact of Assisted Living Organizational Structure and Process Characteristics on Staff Absence During COVID-19.

Quality of care in assisted living communities (ALCs) is important to consumers. The coronavirus disease of 2019 (COVID-19) pandemic and associated emergency policies have posed major challenges on staff recruitment and retention. This study aimed to examine the relationship between organizational characteristics of ALCs in Florida and challenges with staff absence during COVID-19 (N = 129 ALCs). Results indicated that structures (provision of memory care services), processes of care (challenges hiring new staff, staff sent home to comply with precautions, and staff anxiety), and local COVID-19 positivity rate were significantly associated with staff absence due to fear of infection, sickness, or family responsibilities. ALC providers and state regulatory agencies should develop policies and procedures that take these issues into consideration to prepare for future emergencies and disasters. Infection control policies should reflect whether an ALC provides memory care services and inform ways to overcome potential challenges with protocols. Efforts to reduce staff anxiety and disaster-related concerns should also be considered to ensure quality of care during emergencies.

ALCs who provided memory care services had significantly greater challenges with staff absence due to fear of infection during the COVID-19 pandemic.The administrative action of sending staff home to comply with COVID-19 precautions, difficulty hiring new staff, and staff anxiety were process characteristics of ALCs associated with greater challenges with staff absence.Policymakers should consider whether an ALC provides memory care services, faces potential challenges with infection control protocols, and addresses the mental health of staff when creating and implementing infection control policies.

Medication Costs and Use of Older Americans in Assisted Living Settings: a Nationally Representative Cross-Sectional Study.

BACKGROUND: Assisted-living (AL) settings are an important residential care option for old and disabled Americans, but there are no national data characterizing medication use in AL. OBJECTIVE: To investigate medication costs and use of older adults living in the AL settings compared to those in the community, independent living, and nursing home settings. DESIGN: 2015 National Health and Aging Trends Study; nationally representative cross-sectional study. PATICIPANTS: Respondents ≥ 65 years with Medicare Part D prescription drug coverage (n = 5980, representing 32.34 million older adults). MEASURES: Total Part D medication costs; number of 30-day prescription fills; binary indicators for overall polypharmacy (≥ 5 and ≥ 10 concurrent medications), prescription fills of opioid and psychotropic medications including antipsychotics, benzodiazepines, gabapentinoids, antidepressants, and central nervous system-active (CNS-active) polypharmacy. RESULTS: Adjusting for demographics, the annual medication costs among AL residents, at $3890, were twice as high as those of their community-dwelling counterparts ($1932; p < .01). All medication outcomes except opioids were higher for older adults in AL compared to community settings. While the adjusted number of 30-day prescription fills among AL residents was slightly lower than that of nursing home residents (89.5 vs. 106.2; p < .05), AL residents experienced equivalent rates of overall polypharmacy ≥ 10 medications (30.2% vs. 23.5%), antipsychotics (30.8% vs. 27.8%), benzodiazepines (30.7% vs. 32.6%), gabapentinoids (21.2% vs. 16.1%), and CNS-active polypharmacy (26.0% vs. 36.9%; p > .05 for all). Patterns of use across settings were consistent when limited to older adults with dementia. CONCLUSIONS: Older Americans in AL experience a prescription medication burden similar to those in nursing homes. AL settings have an important opportunity to ensure their medication-related clinical services and supports match the needs of their residents.

Caregiver Involvement and Concerns with Care of Residents of Assisted Living before and during the COVID-19 Pandemic.

INTRODUCTION: Family/friend caregivers play an essential role in promoting the physical and mental health of older adults in need of care - especially during the COVID-19 pandemic and especially in assisted living (AL) homes, where resident care needs are similarly complex as in long-term care homes but fewer staffing resources and services are available. However, little research is available on caregiver involvement and concerns with care of AL residents prior to and during waves 1 and 2 of the COVID-19 pandemic. This study addressed this important knowledge gap. METHODS: This prospective cohort study used baseline and follow-up online surveys with primary caregivers to AL residents in Western Canada who were 65 years or older and had lived in the AL home for at least 3 months before Mar 1, 2020. Surveys assessed the following outcomes in the 3 months prior to and during waves 1 and 2 of the pandemic: sociodemographics, 5 ways of visiting or communicating with residents, involvement in 16 care tasks, concerns with 9 resident physical/mental health conditions, perceived lack of resident access to 7 care services, and whether caregivers felt well informed and involved with resident care. RESULTS: Based on 386 caregiver responses, in-person visits dropped significantly in wave 1 of the pandemic and so did caregiver involvement in nearly all care tasks. While these rates increased in wave 2, most did not return to pre-pandemic levels. Correspondingly, caregiver concerns (already high before the pandemic) substantially increased in wave 1 and stayed high in wave 2. These were particularly elevated among caregivers who did not feel well informed/involved with resident care. CONCLUSIONS: Restricted in-person visiting disrupted resident care and was associated with worse perceptions of resident health and well-being. Continued caregiver involvement in resident care and communication with caregivers even during lockdowns is key to mitigating these issues.

Older adults' experiences of participation in daily activities in Swedish assisted living.

BACKGROUND: According to Swedish law, older adults in Sweden should be able to live a good, safe, and independent life with social and healthcare provided, based on their individual needs. In assisted living in Swedish eldercare the environment affects the older adults' ability to participate in decision-making and strengthens their ability to feel meaningfulness. The ability of staff working in social and healthcare to invite older adults to participate varies. It is important to examine how older adults perceive their situation, as caregivers in eldercare tend to focus on routine work and experience difficulties in meeting the individual needs of older adults. The aim of this study was to explore how older adults in assisted living experienced participation in daily activities. METHODS: A qualitative interview study was conducted in two municipalities in the western part of Sweden. An exploratory and inductive design was used. Individual interviews were conducted with 11 older adults living in two different assisted living facilities. The data were analysed using thematic analysis. RESULTS: The interviews resulted in three themes: Being involved, Sense of well-being, and Influenced by the context. The older adults' experiences of participation were interpreted as feelings of being involved in daily life activities, and how they felt involved in their own care and nursing. Participation created prerequisites for well-being. Sense of well-being could be related to feelings of security and social community. The organisation and work environment of the healthcare staff had a great impact on their ability to increase the participation of the older adults. The older adults were aware of the everyday work situation of the providers of social and healthcare and were Influenced by the context. DISCUSSION/CONCLUSION: Important conditions for a good quality of life and participation for the older adults are to be treated with respect, receive information, and be able to choose. The older adults expressed several good ideas for improvements and a willingness to be involved in the development of the organisation at different levels. It is essential to invite older adults in assisted living to participate in the design of care and nursing.

Digital support for quality assurance in 24-hour caregiving at home: a randomized controlled trial investigating the effects on quality of life and professional skills of paid 24h-caregivers.

BACKGROUND: Regarding the care of older adults, 24-h home-care represents a cornerstone, with > 32,000 service users in Austria. Our research project 24hQuAALity aimed to develop and evaluate a distributed client-server software solution for the support and quality assurance of this home-care service. In this trial, we investigated the effects of this intervention on the quality of life and professional skills of paid 24h-caregivers in Austria. METHODS: The application used in our study comprises an e-learning platform, an integrated emergency management, networking opportunities, and an electronic care documentation system in the native language of the 24h-caregivers. The trial was conducted using a parallel three-arm study design to evaluate (i) a control group, which performed usual home care, (ii) a partial intervention group, which used the e-learning and networking platforms, and (iii) a full intervention group, which used the entire intervention (e-learning platform, networking platform, and digital care documentation). Primary self-reported outcomes were the standardized ASCOT for Carers score and a score based on responses to project-specific efficacy questions. RESULTS: Among the 110 24h-caregivers who were randomly classified into the three groups, ASCOT for Carers score data were available for 57 and 35 24h-caregivers at 5- and 9-month follow-up examinations, respectively. At 9 months, 24h-caregivers receiving any intervention rated the ASCOT for Carers score (not significantly) better than the controls (p = 0.05, ηp2 = 0.15), mainly in the domain "feeling encouraged and supported". At 9 months, 24h-caregivers receiving any intervention rated the project-specific Efficacy score significantly better than the controls (p = 0.02, ηp2 = 0.20), mainly due to better ratings in the subitems "satisfaction with current docu", "docu supports doing my job", " I'm well prepared for emergencies", "my professional skills are adequate for doing my job", and "communication with contacts". CONCLUSIONS: Providing e-learning and e-documentation devices to 24h-caregivers improved their care-related quality of life, mainly because they felt more encouraged and supported. Moreover, these interventions improved their self-perceived professional skills. As an extrapolation of findings, we found that these interventions could empower 24h-caregivers and improve the quality of home-care services provided by them. TRIAL REGISTRATION: Digital Support for Quality Assurance in 24-h Caregiving at Home was registered and posted on the ClinicalTrials.gov public website (ClinicalTrials.gov Identifier: NCT04581538).

Identifying predictors of cognitive decline in long-term care: a scoping review.

BACKGROUND: Cognitive impairment can cause social, emotional, and financial burdens on individuals, caregivers, and healthcare providers. This is especially important in settings such as long-term care (LTC) homes which largely consist of vulnerable older adults. Thus, the objective of this study is to review and summarize current research examining risk factors of cognitive decline in older adults within LTC. METHODS: This scoping review includes primary observational research studies assessing within-person change in cognition over time in LTC or equivalent settings in high resource countries. A mean participant age of ≥ 65 years was required. Searches were conducted in Medline, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and PyscInfo on June 27th, 2022 and included articles published during or after the year 2000. Title, abstract, and full-text screening was performed by two independent reviewers using Covidence. Specific predictors along with their associated relation with cognitive decline were extracted by a team of reviewers into a spreadsheet. RESULTS: Thirty-eight studies were included in this review. The mean sample size was 14 620. Eighty-seven unique predictors were examined in relation to cognitive decline. Dementia was the most studied predictor (examined by 9 of 38 studies), and the most conclusive, with eight of those studies identifying it as a risk factor for cognitive decline. Other predictors that were identified as risk factors included arterial stiffness (identified by 2 of 2 studies), physical frailty (2 of 2 studies), sub-syndromal delirium (2 of 2 studies), and undergoing the first wave of COVID-19 lockdowns (2 of 2 studies). ADL independence was the most conclusive protective factor (3 of 4 studies), followed by social engagement (2 of 3 studies). Many remaining predictors showed no association and/or conflicting results. CONCLUSIONS: Dementia was the most common risk factor, while ADL independence was the most common protective factor associated with cognitive decline in LTC residents. This information can be used to stratify residents by risk severity and provide better personalized care for older adults through the targeted management of cognitive decline.

Acceptance and Privacy Perceptions Toward Video-based Active and Assisted Living Technologies: Scoping Review.

BACKGROUND: The aging society posits new socioeconomic challenges to which a potential solution is active and assisted living (AAL) technologies. Visual-based sensing systems are technologically among the most advantageous forms of AAL technologies in providing health and social care; however, they come at the risk of violating rights to privacy. With the immersion of video-based technologies, privacy-preserving smart solutions are being developed; however, the user acceptance research about these developments is not yet being systematized. OBJECTIVE: With this scoping review, we aimed to gain an overview of existing studies examining the viewpoints of older adults and/or their caregivers on technology acceptance and privacy perceptions, specifically toward video-based AAL technology. METHODS: A total of 22 studies were identified with a primary focus on user acceptance and privacy attitudes during a literature search of major databases. Methodological quality assessment and thematic analysis of the selected studies were executed and principal findings are summarized. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines were followed at every step of this scoping review. RESULTS: Acceptance attitudes toward video-based AAL technologies are rather conditional, and are summarized into five main themes seen from the two end-user perspectives: caregiver and care receiver. With privacy being a major barrier to video-based AAL technologies, security and medical safety were identified as the major benefits across the studies. CONCLUSIONS: This review reveals a very low methodological quality of the empirical studies assessing user acceptance of video-based AAL technologies. We propose that more specific and more end user- and real life-targeting research is needed to assess the acceptance of proposed solutions.

Using Smart Home Technologies to Promote Physical Activity Among the General and Aging Populations: Scoping Review.

BACKGROUND: Health-monitoring smart homes are becoming popular, with experts arguing that 9-to-5 health care services might soon become a thing of the past. However, no review has explored the landscape of smart home technologies that aim to promote physical activity and independent living among a wide range of age groups. OBJECTIVE: This review aims to map published studies on smart home technologies aimed at promoting physical activity among the general and aging populations to unveil the state of the art, its potential, and the research gaps and opportunities. METHODS: Articles were retrieved from 6 databases (PubMed, CINAHL, Scopus, IEEE Xplore, ACM Library, and Web of Science). The criteria for inclusion were that the articles must be user studies that dealt with smart home or Active Assisted Living technologies and physical activity, were written in English, and were published in peer-reviewed journals. In total, 3 researchers independently and collaboratively assessed the eligibility of the retrieved articles and elicited the relevant data and findings using tables and charts. RESULTS: This review synthesized 20 articles that met the inclusion criteria, 70% (14/20) of which were conducted between 2018 and 2020. Three-quarters of the studies (15/20, 75%) were conducted in Western countries, with the United States accounting for 25% (5/20). Activities of daily living were the most studied (9/20, 45%), followed by physical activity (6/20, 30%), therapeutic exercise (4/20, 20%), and bodyweight exercise (1/20, 5%). K-nearest neighbor and naïve Bayes classifier were the most used machine learning algorithms for activity recognition, with at least 10% (2/20) of the studies using either algorithm. Ambient and wearable technologies were equally studied (8/20, 40% each), followed by robots (3/20, 15%). Activity recognition was the most common goal of the evaluated smart home technologies, with 55% (11/20) of the studies reporting it, followed by activity monitoring (7/20, 35%). Most studies (8/20, 40%) were conducted in a laboratory setting. Moreover, 25% (5/20) and 10% (2/20) were conducted in a home and hospital setting, respectively. Finally, 75% (15/20) had a positive outcome, 15% (3/20) had a mixed outcome, and 10% (2/20) had an indeterminate outcome. CONCLUSIONS: Our results suggest that smart home technologies, especially digital personal assistants, coaches, and robots, are effective in promoting physical activity among the young population. Although only few studies were identified among the older population, smart home technologies hold bright prospects in assisting and aiding older people to age in place and function independently, especially in Western countries, where there are shortages of long-term care workers. Hence, there is a need to do more work (eg, cross-cultural studies and randomized controlled trials) among the growing aging population on the effectiveness and acceptance of smart home technologies that aim to promote physical activity.

Recurrent Network Solutions for Human Posture Recognition Based on Kinect Skeletal Data.

Ambient Assisted Living (AAL) systems are designed to provide unobtrusive and user-friendly support in daily life and can be used for monitoring frail people based on various types of sensors, including wearables and cameras. Although cameras can be perceived as intrusive in terms of privacy, low-cost RGB-D devices (i.e., Kinect V2) that extract skeletal data can partially overcome these limits. In addition, deep learning-based algorithms, such as Recurrent Neural Networks (RNNs), can be trained on skeletal tracking data to automatically identify different human postures in the AAL domain. In this study, we investigate the performance of two RNN models (2BLSTM and 3BGRU) in identifying daily living postures and potentially dangerous situations in a home monitoring system, based on 3D skeletal data acquired with Kinect V2. We tested the RNN models with two different feature sets: one consisting of eight human-crafted kinematic features selected by a genetic algorithm, and another consisting of 52 ego-centric 3D coordinates of each considered skeleton joint, plus the subject's distance from the Kinect V2. To improve the generalization ability of the 3BGRU model, we also applied a data augmentation method to balance the training dataset. With this last solution we reached an accuracy of 88%, the best we achieved so far.

A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults.

Older adults' independent life is compromised due to various problems, such as memory impairments and decision-making difficulties. This work initially proposes an integrated conceptual model for assisted living systems capable of providing helping means for older adults with mild memory impairments and their caregivers. The proposed model has four main components: (1) an indoor location and heading measurement unit in the local fog layer, (2) an augmented reality (AR) application to make interactions with the user, (3) an IoT-based fuzzy decision-making system to handle the direct and environmental interactions with the user, and (4) a user interface for caregivers to monitor the situation in real time and send reminders once required. Then, a preliminary proof-of-concept implementation is performed to evaluate the suggested mode's feasibility. Functional experiments are carried out based on various factual scenarios, which validate the effectiveness of the proposed approach. The accuracy and response time of the proposed proof-of-concept system are further examined. The results suggest that implementing such a system is feasible and has the potential to promote assisted living. The suggested system has the potential to promote scalable and customizable assisted living systems to reduce the challenges of independent living for older adults.