Nowadays, computer systems play a very important role in the health care context. However, many of these systems are also becoming increasingly complex. In particular, the design of these systems should be compatible with Alzheimer’s situation, as the condition of Alzheimer patients is growing worse, especially with the poor understanding of the disease, lack of support language difficulties, and memory loss. These systems should meet the various needs of such patients. In this paper, we are focusing on evaluating Alzheimer's assistance systems. Usually, in designing those systems, the human computer interaction (HCI) is left behind. Thus, we demonstrate several aspects to improve Alzheimer’s patient ability to interact with these systems. Moreover, we discussed the key factors that affect the success or failure of those systems taking into consideration criterion such as bringing down health care costs, learning ability, usability, dependability, readability, and facilitate automating. In the end, the paper illustrates using charts, and tables to show the impact of applying HCI when designing an interface to achieve user-friendly, intuitive interfaces, and higher usability of the systems.
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Alzheimer’s disease has become a very significant problem in the current era that is very likely to worsen due to increasing life expectancy, Alzheimer's disease is currently ranked as the sixth leading cause of death in the United States.
- Dart
- Flutter
- State Management (BLOC-PROVIDER)
- Google maps
- Dio
- Saving and displaying the photos and names of the patients’ relatives and caregivers or other information related to them, thereby helping the patients review or reconstruct their memories.
- Reminding the patient to take the correct medicines on time, eating meals, drinking water, taking a bath, closing windows and door, joining their events.
- The interaction between each human role (patients, assistant) is the most important of this system. As a result, the developed application consists of two parts: An Alzheimer’s patient interface, and the assistant's interface.
- Let the patient write things they should do to remind.
- Allows patients to use the phone's camera to identify the person standing in front of them using facial recognition technology.
- To test a patient's cognitive abilities, we have divided the tests into 4 different test categories: - (thinking tests, Concentration tests, memory tests, Decision making).
We are working on making this app convenient and useful for all Alzheimer's patients we also work on reporting and data. More accurate and professional processing. We hope to improve Security and performance of the application and making the application competitive with it all global therapeutic applications. So, there are several steps and stages that we hope to develop In the future, like adding more treatment than removing chronic and intractable diseases as an alternative solution, and also for all categories of patients. Make the app more inclusive More than one medical field makes the app more effective and usable from many categories and increases the number of users to request. The use of data science techniques and artificial intelligence in our application makes reports more accurate and professional, and helps the patient's relatives and companions to follow up their patient well. Optimal use of data and analytics will also ensure that improvements are kept up to date on an ongoing basis as a result of the overall vision and measure. Key operational metrics. Use plans and methods that make the application appropriate and Compatible with all Alzheimer's patients we will make the application support English and In addition to the Arabic language that will provide an interface the number of application users. Our main goal is to treat Alzheimer's patients and find a real cure for them instead of going back or forth to the doctor in vain Test the application with 70-100 patients and collect their feedback with them. With interactions automatically generated and analyzed to produce a paper that discusses our experience with the results we found.
MIT License
Copyright (c) 2023 Abdulrhman Khaled Hassan Zaki
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