Home Assistance Research Articles

The Adversarial Attacks towards Human Action Recognition Models: A Comparison between unimodal and multimodal Models

Human-action recognition models are neural networks that analyse visual inputs and provide classification or text outputs. This technology has and will significantly impact society in security, education, healthcare, etc. However, Human-Action Recognition models, like other neural networks, are still susceptible to malicious adversarial attacks. Therefore, this paper proposes an experimental adversarial attack towards ResNet-18 using FGSM. First, ResNet-18 is finetuned using the UCF-101 dataset, and keyframes are selected from sample videos. The keyframes will be given to ResNet-18 for classification while FGSM will be implemented, and ResNet will do another classification of the attached sample. The classification results (Original and Attacked) are given to the language model (GPT-4o) through a prompt that provides the language model with a specific role (e.g. a smart home assistant), and this section is regarded as unimodal. The original and attacked frames will be sent directly instead of the labels in the multimodal section. Lastly, this paper proposes to observe the effects on textual responses generated based on a given prompt and the classification result and evaluate the impact of the attack through Cosine Similarities and Human Evaluation.

Burden of vaccine-preventable diseases in adults (50+) in the United States: a retrospective claims analysis

BackgroundIn adults aged 50 + years, vaccine-preventable diseases (VPDs) pose a significant health burden and can lead to additional ‘downstream effects’ of infection beyond the acute phase e.g., increasing the risk for non-communicable disease and exacerbating chronic conditions. The aim was to understand and quantify the burden of VPD downstream effects in hospitalised adults in the United States.MethodsThis retrospective observational study analysed hospitalisation claims data (2016–2019) with 1-year follow-up, in adults with a VPD diagnosis versus matched controls (using Optum’s de-identified Clinformatics Data Mart Database). Outcomes included mortality; increase in Charlson Comorbidity Index (CCI) score; new diagnosis of comorbidities; and loss of independence (defined by need for home health/home care and/or move to long-term facility).ResultsMortality was significantly increased in VPD cases versus controls at 30-day (risk ratio [RR] of 4.08 [95% CI 3.98–4.18]) and 1-year follow-up (RR 2.76 [2.73–2.80]). Over a 1-year follow-up period, morbidity increased following VPD hospitalisation: 65–86% of VPD cases had new comorbidities diagnosed (versus 13–41% of controls); with a significantly higher mean increase in CCI score versus baseline (3.23 in VPD cases versus 0.89 in controls, p < 0.001). Adults were observed to experience a worsening of their health status and were less likely to return to their original health state. In addition, 41% of VPD cases had a loss of independence following hospitalisation versus 12% of controls; as seen by an increased need for home assistance (in 25% versus 9% of controls) and/or a move to a long-term care facility (in 29% versus 6% of controls).ConclusionsThis analysis suggests that VPD hospitalised cases suffer significantly worse clinical outcomes than controls, with downstream effects that include increased mortality and morbidity, and greater loss of independence. Evidence on potential downstream effects of infection is relatively new, and this additional burden is generally not considered in vaccine decision-making. More research is needed to disentangle the effect of VPDs on new comorbidities versus the natural course of the condition. Increasing awareness among adults, healthcare providers and decision makers could help to increase adult vaccination coverage, and reduce the clinical burden of VPDs.

Wireless sensor network-based health monitoring and smart home assistance for the older adults in sports and wellness towns.

Integrating technology with rural development is essential for addressing the unique challenges faced by aging populations in rural areas. China's national rural revitalization strategy emphasizes the importance of developing characteristic towns that focus on health and wellness demands, particularly for the older adults. However, there is a gap in the literature regarding the systematic use of technology to support the health and daily living needs of this demographic. This study aims to bridge this gap by proposing a wireless sensor network (WSN)-based system that integrates health monitoring and smart home assistance, specifically tailored for older adults residents in sports and wellness towns. The system's design involves collecting and analyzing historical activity data and basic physiological parameters from older adults residents' homes. The data are cleaned, combined and stored to prepare it for behavior analysis, which is vital for controlling smart home equipment. Preliminary health assessments are conducted using the collected physiological data. A hybrid network leveraging Sub-G and Wi-Fi technologies is optimized for the purposes of collecting and uploading data, based on a comparative analysis of wireless communication options. This study significantly contributes to the advancement of sports and wellness towns by promoting healthy aging through cutting-edge technological solutions.

Rehabilitasi Pecandu Narkotika melalui Konsep Pendidikan Islam Menurut Al-Ghazali

This research aims to examine and analyze the implementation of rehabilitation for narcotics addicts based on the concept of Islamic education according to Imam Al Ghazali at IPWL Bahrul Maghfiroh, Malang City. The research was conducted qualitatively using a phenomenological approach with purposive sampling techniques through interviews with Rehabilitation Home Assistants, Guardians of Addicts, and Representatives of Addicts who are currently undergoing or have completed the rehabilitation period. The research results show that Therapeutic Community (TC) is the main program at IPWL BMCI Malang City based on Islamic education through an Islamic emotional approach because it prioritizes environmentally friendly therapy. The TC concept applied is Al-Ghazali's educational concept within the scope of Hablumminallah and Hablumminannas. The Hablumminallah concept can be seen in the principle of Man Helping Man to Help by the Mujahadah Principle, the Recovery Concept by Tadzkiyatun Nafs, as well as the Right Living concept by Al-Ghazali's Educational Curriculum Principles, namely that they are both oriented towards a straight approach to Allah. The problem with the Islamic education-based Therapeutic Community program is that the companion cannot know the truth of the expressions expressed by the rehab students as a whole, the companion can only detect changes in behavior and prevent behavior that will arise from signs obtained through information extracted after counseling. personally.

Two Wheeler Showroom Android Application

The two-wheeler Android application is a bike display area Android application, which can deal with all client subtleties carefully. Individuals are dealing with issues due to the disconnected framework like adjusting dates get due, likewise deal with issues while buying the vehicle. There hasn't been an Android application for the bike display area for the past ten years. Help individuals by saving their time, and get their decision vehicle with shading, the frill is likewise accessible on the web. So this application helps buy vehicles, doesn't bother keeping page receipts, saves individuals time with pick and drop home assistance, and is online accessible 24*7

Emotional competence and help-seeking intentions as predictors of educational success in vocational training students

Given the high prevalence of psychological distress among vocational training (VT) students, this study aimed to assess the role of interpersonal emotional competence as a resilience factor promoting the educational success of this population. We postulated that emotional competence would promote educational success, both directly and indirectly by fostering students’ help-seeking intentions when facing a personal or school-related problem. To test these hypotheses, we used a sample of 219 VT students from the Canadian province of Quebec (68% women, Mage = 24.58; SDage = 7.95) enrolled in various programs (e.g. institutional and home care assistance, welding and fitting, secretarial studies, and professional cooking). These students were assessed two times, during the first half of their training and again after their training. Results from structural equation modelling revealed that emotional competence was a positive predictor of help-seeking intentions and educational success. However, having the intention to seek help did not translate into higher levels of educational success. Overall, these results highlight the importance of supporting VT students in the development and strengthening of their emotional competence to promote their educational success. Future research is needed to further understand the help-seeking process among VT students and its implications for their academic outcomes.

Multidisciplinary care use in neurodegenerative complex diseases: The example of progressive supranuclear palsy and advanced Parkinson's disease in real-life

BackgroundIn spite of being considered the gold-standard of care, little is known about the real-life use of in-home and multidisciplinary care in atypical parkinsonism. ObjectivePrimary: Examine real-life multidisciplinary care use for Progressive Supranuclear Palsy (PSP). Secondary: a) Compare PSP care to advanced Parkinson's disease (APD) care; (b) Explore demographic and clinical variables associated with care needs in both groups. MethodsA cross-sectional multicenter observational study enrolled 129 PSP patients and 65 APD patients (Hoehn and Yahr ≥3), matched for sex and age. Univariate and multivariate regression analysis were performed. ResultsOver the previous year, 40 % of PSP patients did not encounter a physical therapist, while only one-third met a speech and language therapist and 5 % an occupational therapist. More than 20 % received in-home care and 32 % needed home structural changes. Compared to APD, PSP patients required more day-time, night-time and home structural changes. When considering both PSP and APD in multivariate analysis, reduced functional autonomy and living without a family caregiver were both related to day-time home assistance and to the need of at least one home care service. A PSP diagnosis compared to APD was a risk factor for having at least four multidisciplinary visits in a year. Finally, PSP diagnosis and being from the Northern Italy were significantly related with home structural changes. ConclusionsThere's a significant gap in providing multidisciplinary care for PSP patients. Our findings emphasize the need for a shared, integrated care plan at a national level for patients with atypical parkinsonism.

Types of AI and Their Suitability for Performing Daily Activities

Abstract The usage of robots and artificial intelligence in the business field and in the everyday life of consumers has been a topic of discussion for a long time. While some people are fascinated by the opportunity of having robots supporting them in their daily activities, others are more hesitant. Nowadays, there are different types of robots with artificial intelligence, and consumers, depending on their preferences and needs, prefer a certain type of robot. With the help of an online survey, we found out which type of robot is most often used by the consumer and why. Among the various types of robots with artificial intelligence that were mentioned in the questionnaire, personal home assistants are the robot with artificial intelligence that consumers prefer when they want to perform an action. The second most preferred robot with artificial intelligence is the self-service portal with AI, and in third place is the personal voice assistant. The main results show that personal home assistants are robots with artificial intelligence, which is used most often by the consumer to order food, different products and to perform different activities. With the help of the results, we can also observe that consumers also use the self-service portal with AI as a navigation system or as an alarm clock. The personal voice assistant is the type of artificial intelligence robot that is least used by consumers to do something, which is again an important result.

Modeling and Sentiment Analysis of Social Relationships in Elderly Smart Homes Based on Graph Neural Networks

With the expansion of Speech Emotion Recognition in the consumer domain, several devices, particularly those designed for managing smart home personal assistants for the elderly, have been widely available on the market. The increasing processing power and connection, together with the growing need to facilitate longer residency through technological interventions, highlight the potential benefits of smart home assistants. Enabling these assistants to recognize human emotions would greatly improve user-assistant communication, allowing the assistant to deliver more constructive and customized feedback to the user. In this research work, Modeling and Sentiment Analysis of Social Relationships in Elderly Smart Homes Based on Graph Neural Networks (SASR-MBHNN-BBOA) is proposed. The input data are collected from Social Recommendation Dataset. Then, input data are pre-processed utilizing Inverse Optimal Safety Filters (IOSF) for cleaning the data and removing the background noise. Then the pre-processed data are given to Memristive Bi-neuron Hopfield Neural Network (MBHNN) for predicting the sentiments like positive, negative and neutral. In general, MBHNN doesn’t express some adoption of optimization approaches for determining optimal parameters to predicting the sentiments accurately. Hence BBOA is proposed to optimize MBHNN classifier which precisely predicts the sentiments in elderly smart home. The proposed SASR-MBHNN-BBOA method is implemented in Python, and it assessed with numerous performance metrics such as accuracy, precision, recall, F1-score, ROC. The outcomes show SASR-MBHNN-BBOA attains 20.8%, 19.5%, and 29.6% higher Accuracy, 28.8%, 22.5%, and 32.6% higher Precision, 15.5%, 27.4%, and 18.2% higher Recall are analysed with existing methods such as, Emotional speech analysis in real time for smart home assistants.(SASR-CNN-SHA), Machine Learning to Investigate Elderly Care Requirements in China via the Lens of Family Caregivers (SASR-ML-IECR),Identifying User Emotions via Audio Conversations with Smart Assistants (SASR-DNN-EASA) methods respectively.

Designing Home Automation Routines Using an LLM-Based Chatbot

Designing Home Automation Routines Using an LLM-Based Chatbot

Care-receivers with physical disabilities’ perceptions on having humanoid assistive robots as assistants: a qualitative study

BackgroundPeople with physical disabilities due to disease or injury face barriers to their daily activities and participation in society. Many depend on formal or informal caregivers for assistance to live independently. However, future healthcare challenges due to demographic changes threaten access to home care and assistants. Assistive technologies, such as robots for physical assistance, can support the independence and autonomy of people with physical disabilities. This study explore Norwegian care-receivers’ perceptions of using robot assistance in their homes, including preferences for tasks acceptable or unacceptable for robot assistance and the underlying reasons.MethodPurposive sampling was employed to recruit 18 participants, aged between 18 and 77 years, with differences in physical function including diagnoses such as stroke, spinal cord injury, amputations, and muscular dystrophy. Qualitative data were gathered through four focus group interviews wherein participants watched videos featuring a humanoid assistive robot, EVEr3. The collected data underwent analysis using reflexive thematic analysis.ResultsThree themes with associated sub-themes were constructed: (a) How a robot could assist in daily life, (b) The robot’s appearance and functionality, and (c) Concerns about having a robot as an assistant. The participants welcomed the idea of a future robotic assistant in areas that may contribute to an increased feeling of independence and autonomy.ConclusionA robot assisting in activities of daily living would need to be individually customized to meet the needs of each user in terms of which tasks to assist with, how to assist in these defined tasks, and how it is controlled.

Use of Digital Home Assistants for Community-Dwelling Older Adults with Mobility Disabilities

Use of Digital Home Assistants for Community-Dwelling Older Adults with Mobility Disabilities

Sasha

Smart home assistants function best when user commands are direct and well-specified---e.g., "turn on the kitchen light"---or when a hard-coded routine specifies the response. In more natural communication, however, human speech is unconstrained, often describing goals (e.g., "make it cozy in here" or "help me save energy") rather than indicating specific target devices and actions to take on those devices. Current systems fail to understand these under-specified commands since they cannot reason about devices and settings as they relate to human situations. We introduce large language models (LLMs) to this problem space, exploring their use for controlling devices and creating automation routines in response to under-specified user commands in smart homes. We empirically study the baseline quality and failure modes of LLM-created action plans with a survey of age-diverse users. We find that LLMs can reason creatively to achieve challenging goals, but they experience patterns of failure that diminish their usefulness. We address these gaps with Sasha, a smarter smart home assistant. Sasha responds to loosely-constrained commands like "make it cozy" or "help me sleep better" by executing plans to achieve user goals---e.g., setting a mood with available devices, or devising automation routines. We implement and evaluate Sasha in a hands-on user study, showing the capabilities and limitations of LLM-driven smart homes when faced with unconstrained user-generated scenarios.

Ageing with Parkinson's: Identification of Personal Needs in the Northern Spanish Context.

As individuals diagnosed with Parkinson's disease enter older age, the myriad challenges and complications associated with this condition tend to escalate. Hence, there is a critical necessity to comprehensively discern the perceived needs of these individuals, along with their proposed remedies and essential support requisites. Additionally, understanding the perspectives of their families becomes imperative to formulate tailored interventions aimed at enhancing their overall development, progression, and quality of life. The study's main objective is to assess the perceived needs of individuals with PD and their family members, propose necessary solutions, and suggest future perspectives. The study encompassed a cohort of 268 participants, comprising 179 individuals diagnosed with Parkinson's disease and 89 of their relatives. A meticulously designed structured interview instrument consisting of 93 items was employed to assess various domains encompassing perceived needs, institutional support mechanisms, essential solutions, and future anticipations. Results: Statistically significant differences were found in health resources, social services resources, obstacles, solutions, and future outlook, with higher mean values from the relatives. Conclusions: The results highlight the most concerning needs in this context. Specifically, those needs related to health resources, social services resources, and future outlook present the greatest differences between the two subsamples, with the family members perceiving more needs. This alignment extended to both the categorization of unmet needs and the requisite solutions envisioned to address them. Suggested improvements include a sociosanitary strategy, stakeholder involvement, and prioritizing flexible home assistance to support older individuals with PD and their families.

Effects of the COVID-19 Pandemic on Breastfeeding Initiation and Duration: A Retrospective Cohort Study.

Several studies support the health benefits of breastfeeding for both the mother and the newborn. However, a significant number of mothers discontinue breastfeeding within the first six months of childbirth, with several factors influencing breastfeeding adherence.The purpose of this study is to assess the impact of the COVID-19 pandemic on the prevention of mother-to-newborn infection transmission, breastfeeding patterns and duration, and the incidence of other infections during the first year of life. Data from a sample of 39 mothers who gave birth at the Hospital Pedro Hispano in Porto, Portugual, between March 2020 and November 2021 were collected and a telephone questionnaire was administered. Statistical analysis was conducted using R software, v. 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria). In terms of the impact of the COVID-19 norm 18/2020, which went into effect on March 30th, our research found that the type of feeding during hospitalization was significantly influenced by this norm (X2=10.30, p=0.006). We also confirmed that mothers who received home assistance breastfed for an extra 4.5 months (95% CI: 1-7.5) compared with mothers who did not receive such assistance. Regarding the effect of COVID-19 and breastfeeding on newborn health, our study found that if the total duration of breastfeeding is less than six months, an infection is approximately five times more likely (95% CI = 1.06- 29.56). Overall, the findings of this study indicate that the efforts implemented at Hospital Pedro Hispano to limit the effects of the COVID-19 pandemic had some effect on immediate breastfeeding patterns, but not on the total duration of breastfeeding or newborn health. Nonetheless, more continuous assistance at home would have been beneficial.

Investigation of Machine Learning Analytic-Based Data Classification Frameworks on Iot-Based Infrastructures

The Internet of Things revolutionizes and enables convenient, automated lifestyles for people today. Over the past ten years, advances in computer, connectivity, and application design have led to its development. The Internet of Things has quickly spread around the world, impacting every person on the planet. Everyday IoT devices, such as smartphones, Google Home assistants, smart vehicles, and automated systems in buildings, including smart elevators and temperature control, as well as drones for environmental monitoring and leisure, play crucial roles in our daily lives. Therefore, the purpose of this research is to explore data classification frameworks based on machine learning analytics in Internet of Things-based infrastructures. This study focuses on articles published between 2010 and 2023, utilizing a comprehensive literature review approach. The study entails reading through at least eighty peer-reviewed conference proceedings, industry white papers, and journal articles. An overview of the relationship between machine learning and the Internet of Things (IoT) is given in this study. The approach consists of four standard processes that are used in review papers: gathering articles from Thomson Reuters Web of Science, identifying categories, carrying out descriptive analysis, and assessing the information acquired. A discussion on the categorization of diverse analytics methods for IoT is presented in the paper. Moreover, it introduces and discusses the architectural Framework for IoT and the challenges in IoT Data Classification. Furthermore, Machine Learning Strategies for Identifying and Classifying IoT were presented. Researchers must investigate machine learning analytic-based data classification frameworks for Internet of Things infrastructures. The challenges arising from the unique characteristics of IoT data call for innovative solutions, and current frameworks demonstrate promising results.

Neighbourhood Care Teams: Integrating Health Care and Social Services for Seniors in Toronto Community Housing

Toronto Seniors Housing Corporation (TSHC), owned by the City of Toronto, provides housing for 15,000 low-income seniors in 83 seniors-designated buildings across Toronto. The North Toronto Ontario Health Team (including primary care, hospital, community and home-care) partnered with one of the TSHC buildings in North Toronto to develop and implement a Neighbourhood Care Team (NCT) model to support TSHC’s Integrated Service Model to address tenants' health and social needs, co-designed with the tenants. The goal of the NCT is to provide an integrated model of care that is accountable to meeting the needs of people living within a specific neighbourhood so that people experience one system that provides simple access to service, and care that is coordinated with streamlined communication of health care providers.&#x0D; The NCT objectives include:&#x0D; &#x0D; Increasing primary care provider connections&#x0D; Increasing mental health &amp; addictions care access and support options&#x0D; Increase Digital Health access and literacy to support primary care and specialist access, reduce social isolation and increase wellness&#x0D; Reduce avoidable ED and hospital use.&#x0D; &#x0D; The service design is guided by a co-design process with the tenants as follows:&#x0D; &#x0D; Door to door survey to engage tenants in identifying their barriers and the services and supports most meaningful to them&#x0D; Eliciting and voting on key education and support initiatives at an influenza vaccination clinic&#x0D; Communication back to tenants regarding the results of the survey and how the strategies/activities planned for the building have been prioritized based on their feedback.&#x0D; Multi-organization Education Fair focusing on the top issues addressed during the vaccination clinic survey which was well attended&#x0D; Regular educational sessions in response to tenant interest, combined with a self-screening component to help link the information to a concrete service/intervention to promote better health.&#x0D; Providing translation support to enable access and engagement by tenants from a variety of cultural backgrounds.&#x0D; Ongoing commitment to continue and co-design services and elicit tenants’ feedback.&#x0D; &#x0D; The team has worked to design structures to strengthen coordination and collaboration among the various delivery partners:&#x0D; &#x0D; Multi-organizational bi-weekly huddles to discuss residents identified with unmet needs (with consent or anonymized without consent) and identify options for improving their access to health care/social services and respond to their needs in a timely manner&#x0D; Designed pathways for ensuring attachment to primary care, access to primary care and specialist support, access to home care services and assistance with social determinants of health&#x0D; Established mechanisms to obtain informed consent and enable information sharing between delivery partners.&#x0D; &#x0D; Multi-modality tenant engagement to tailor services and supports to a TSHC building has led to increased involvement by tenants and a growing interest by tenants in strategies to improve their health and social inclusion. Cross-sector collaboration is an efficient and effective way to establish needs-based integration of health and social care services in this setting. Strong leadership as well as co-developed processes, frequent building meetings and cross-sector huddles were effective ways of sharing innovative ways of meeting needs with limited resources.&#x0D; &#x0D; &#x0D; &#x0D;

Hospital-to-Home: logic model and theory of change exercise to identify outcomes arising from integrated health and social care.

Introduction: Although Canada boasts universal access to hospital-based services, bed capacity has been a challenge with long wait times for acute admissions to a hospital bed from the Emergency Department. The use of hospital beds by ‘alternate level of care’ (ALC) patients is a critical concern. These ALC patients exist because of a lack of an appropriate level of care in the community. Integrated health and social care (IHSC) can limit avoidable acute care utilization including in-patient stays in hospitals. CHATS--Community &amp; Home Assistance to Seniors is a community-based agency that provides transportation, meals, in-home services, caregiver relief, education and support, community wellness, and 24/7 assisted living among other services, with the aim of supporting ageing in place. CHATS has partnered with several local hospitals and homecare providers to create a number of hospital-to-home programs (HTH) that provide seamless and coordinated care from hospital to home for older adults enabling safe and effective transitions. Hospital-based data has shown that ALC days and inpatient and ED hospital readmissions declined following implementation of HTH programs. Although hospital-based data is available, community-based health and social care outcome-based metrics have not been developed nor collected. Despite limited qualitative data showing high levels of client and caregiver satisfaction with its HTH services, the lack of outcome-based data makes it difficult for CHATS to articulate the benefits of the HTH programs from a community-based perspective that reflects both health and social care outcomes with the aim of informing policy and programming decisions in the future, including program scale and spread.&#x0D; Methodology: A logic model (LM) and theory of change (ToC) exercise was undertaken by a broad representation of CHATS staff at all levels of the organization to identify the theory of change for the HTH programs and critical activity-based and outcome-based metrics to support future measurement and evaluation work. The development of these models clarified the fundamental program theory behind community agency contributions to the HTH programs that enable their success.&#x0D; Results: The theory of change noted that the HTH program was able to achieve hospital to home transition success because it facilitates older adult clients to achieve greater self-determination that can support more independent living. The HTH program’s components, case management, personal support, transportation, training, counselling, and assessments ensure that the personal goals of clients and their caregivers are identified and addressed. Feelings of empowerment, improved mental health, health and social care navigation literacy, and reduced acute care readmissions are some of the outcomes perceived as being attributable to the HTH programs. These outcomes, in turn, allow for greater overall health and wellbeing, and sense of independence among older adult clients and their caregivers. Ultimately, the HTH programs contribute towards greater systems efficiency and enhanced quality of life for clients and their family/caregivers.&#x0D; Next steps: The results of the LM and ToC exercises have enabled CHATS to identify critical input, activity and outcome measures for the HTH programs that reflect the contributions of CHATS, and a towards a future summative evaluation of the HTH programs.&#x0D;

Multi-objective crowd-aware robot navigation system using deep reinforcement learning

Navigating efficiently and safely through human crowds is essential for mobile robots in diverse applications such as delivery services, home assistance, healthcare, and manufacturing. However, traditional navigation methods are adversely affected by the high randomness of human movements, seriously hindering robot navigation in crowd environments. To tackle these problems, this paper proposes a deep reinforcement learning-based multi-objective crowd-aware robot navigation system called Multi-Objective Dual-Selection Reinforcement Learning (MODSRL). To deal with multiple objectives, including safety, time efficiency, collision avoidance, and path smoothness during navigation, a set of reward functions is used in MODSRL. To address the challenge of hesitation at the beginning when navigating in a crowd environment, a Dual-Selection Attention Module is developed, which enables the robot to make efficient decisions while reducing hesitation. Experimental results demonstrate that the proposed MODSRL outperforms existing approaches in terms of five different metrics. In particular, the average success rate of the proposed MODSRL outperforms ERVO, CADRL, LSTM-RL, and OM-SARL algorithms by 17.1%, 26.4%, 16.6%, and 9.2%, respectively, sufficing to show its robustness in complex crowd environments.

Family Caregiving in a Great Lakes Native American Community: A Mixed Methods Study of Service Utilization

AbstractBackgroundNative Americans are disproportionately impacted by ADRD, and family caregiving is uniquely significant within Indigenous communities. Caregiving services can improve outcomes but are underutilized, and increasing use of caregiver services is key to addressing the dementia care crisis. Indigenous populations are underrepresented in the ADRD caregiving literature, resulting in a significant gap in understanding of how to improve engagement with services. We examine perspectives on service utilization among caregivers in a large tribal nation in the US Great Lakes region.MethodWithin a community‐based research partnership between the Wisconsin ADRC and the Oneida Nation of Wisconsin, N = 481 completed a survey on use of seven common caregiver resources, including support groups, respite, caregiver training, household help, transportation for care recipient, nursing home, and financial assistance. Drawing on the Andersen model of utilization, multiple regression models examined correlates of positive attitudes toward use of services among caregivers (N = 207), defined as past, current, or intended future use of services. Follow‐up in‐depth interviews with 10 caregivers of elders with ADRD (Table 3) explored decision‐making regarding service utilization using qualitative content analysis.ResultThere were few demographic differences between caregivers and non‐caregivers in the sample (Mage = 52 and 54 yrs; 75% female; Table 1). Over half (54.6%) of caregivers were employed; most (64%) cared for someone with ADRD. In the final regression model in the caregiver subsample, only level of belief that traditional Oneida values and practices support caregivers in using caregiver services predicted positive attitudes toward service utilization (p&lt;.001; Table 2). Interviews revealed barriers to service use include low awareness, complexities of service funding structures, and Elder preferences. Perceptions of alignment of traditional cultural values with service use were complex and nuanced.ConclusionUsing mixed methods in a Native American community sample, we identified unique barriers to access resources to support elders with ADRD and their families, including perceived cultural support for service use. Our findings contribute to the scant literature on ADRD caregiving among Native Americans and provide support for tailored interventions (e.g., Savvy Caregiver in Indian Country). Results can inform public health initiatives to address ADRD disparities and improve outcomes for Indigenous persons with ADRD.