AR Platform for Indoor Navigation: New Potential Approach Extensible to Older People with Cognitive Impairment

Abstract

Background: Cognitive loss is one of the biggest health problems for older people. The incidence of dementia increases with age, so Alzheimer’s disease (AD), the most prevalent type of dementia, is expected to increase. Patients with dementia find it difficult to cope with their daily activities and resort to family members or caregivers. However, aging generally leads to a loss of orientation and navigation skills. This phenomenon creates great inconvenience for autonomous walking, especially in individuals with Mild Cognitive Impairment (MCI) or those suffering from Alzheimer’s disease. The loss of orientation and navigation skills is most felt when old people move from their usual environments to nursing homes or residential facilities. This necessarily involves a person’s constant presence to prevent the patient from moving without a defined destination or incurring dangerous situations. Methods: A navigation system is a support to allow older patients to move without resorting to their caregivers. This application meets the need for helping older people to move without incurring dangers. The aim of the study was to verify the possibility of applying the technology normally used for video games for the development of an indoor navigation system. There is no evidence of this in the literature. Results: We have developed an easy-to-use solution that can be extended to patients with MCI, easing the workload of caregivers and improving patient safety. The method applied was the use of the Unity Vuforia platform, with which an augmented reality APK application was produced on a smartphone. Conclusions: The model differs from traditional techniques because it does not use arrows or labels to identify the desired destination. The solution was tested in the laboratory with staff members. No animal species have been used. The destinations were successfully reached, with an error of 2%. A test was conducted against some evaluation parameters on the use of the model. The values are all close to the maximum expected value. Future developments include testing the application with a predefined protocol in a real-world environment with MCI patients.