A Mobile Application for Alzheimer's Caregivers

Qamar, Nafees

doi:10.1109/ichi54592.2022.00082

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…2) E-Health E-health describes the use of digital technologies, including online platforms, mobile apps, and electronic health records, to give persons with AD access to health services and support. The following sources have made noteworthy contributions to the 46 articles that make up this survey: [9], [32], [33], and [17]. These sources explicitly address the application and developments of e-health technology in the context of AD care.…”

Section: ) Telemedicinementioning

confidence: 99%

“…In [9], Qamar, et al, Developed an Android-based assistive healthcare application to support caregivers of Alzheimer's patients. The application aimed to manage daily tasks, medications, and improve patient memory through brain games, utilizing mobile technology without the need for sensors.…”

Section: ) Telemedicinementioning

confidence: 99%

“…Telemedicine [8] helps doctors assess and treat patients remotely using technology. E-health [9] uses apps and websites to track patients' health and let them communicate with healthcare providers. Moving from virtual care to the real world, smart environment technologies [10] create homes that adapt to patients' needs.…”

Section: Introductionmentioning

confidence: 99%

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ICT-Based Solutions for Alzheimer’s Disease Care: A Systematic Review

Ali,

Turetta,

Demrozi

et al. 2024

IEEE Access

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In recent years, there has been a growing recognition of the significant challenges posed by Alzheimer's Disease (AD) and the need for innovative solutions to improve the quality of life for affected individuals. As AD prevalence continues to rise, technological advancements offer promising opportunities to address the multifaceted needs of patients and caregivers. This survey paper thoroughly investigates technological innovations in AD care, offering valuable insights into cutting-edge approaches that have the potential to positively impact the lives of affected individuals. By providing a holistic view of available assistive solutions, we review 2459 papers and select 46 relevant studies published between 2015 and 2023, specifically focusing on healthcare technologies and solutions, and utilized sensing methods. The former will include Telemedicine, E-Health, Smart Environment, Internet of Things (IoT), Ambient Assisted Living (AAL), Internet of Medical Things (IoMT), and Personalized Assistive Solutions (PAS), while the latter encompasses Wearable/Environmental, Radio/Audio, Video/Image, and Digital Platforms. Our comparative assessment of recent survey papers reveals the unique contribution of this study, as it comprehensively examines the intersection of multiple parameters. By summarizing insights from these studies, we identify gaps and recommend future directions for advancements in AD care.INDEX TERMS Ambient Assisted Living (AAL), Alzheimer's disease (AD), Internet of Medical Things (IoMT), Internet of Things (IoT), Systematic Literature review, Personalized Assistive Solutions.

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Section: ) Telemedicinementioning

confidence: 99%

Section: ) Telemedicinementioning

confidence: 99%

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ICT-Based Solutions for Alzheimer’s Disease Care: A Systematic Review

Ali,

Turetta,

Demrozi

et al. 2024

IEEE Access

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“…As reminders, notifications can help provide important information about appointments [ 52 ], deadlines [ 53 ], or tasks that need to be completed [ 54 ]. With notifications, individuals can avoid delays or forgetting to carry out important activities.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Forecasted Activity Notifications with Reinforcement Learning

Fikry

Inoue

2023

Sensors

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In this paper, we propose the notification optimization method by providing multiple alternative times as a reminder for a forecasted activity with and without probabilistic considerations for the activity that needs to be completed and needs notification. It is important to consider various factors when sending notifications to people after obtaining the results of the forecasted activity. We should not send notifications only when we have forecasted results because future daily activities are unpredictable. Therefore, it is important to strike a balance between providing useful reminders and avoiding excessive interruptions, especially for low probabilities of forecasted activity. Our study investigates the impact of the low probability of forecasted activity and optimizes the notification time with reinforcement learning. We also show the gaps between forecasted activities that are useful for self-improvement by people for the balance of important tasks, such as tasks completed as planned and additional tasks to be completed. For evaluation, we utilize two datasets: the existing dataset and data we collected in the field with the technology we have developed. In the data collection, we have 23 activities from six participants. To evaluate the effectiveness of these approaches, we assess the percentage of positive responses, user response rate, and response duration as performance criteria. Our proposed method provides a more effective way to optimize notifications. By incorporating the probability level of activity that needs to be done and needs notification into the state, we achieve a better response rate than the baseline, with the advantage of reaching 27.15%, as well as than the other criteria, which are also improved by using probability.

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