The integrated VR environment provides ready access to highly precise spatial information of older adults’ homes and their behavioral and health outcomes as they occur within their life spaces. Our data synthesis and analysis demonstrated that our data collection and fusion method across multiple platforms is not only feasible but also valid.
The dataset may offer a valuable platform for researchers, educators, and clinicians where they can explore older adult’s life spaces, pose important research questions, and answer them. For example, researchers interested in the impact of home modifications on older adults’ functional independence, satisfaction, activity levels, and acute stress levels can examine the data set, formulate research hypotheses, and statistically test them. Housing professionals concerned about accessible housing can explore the life space of our participants and learn about where common problems occur. Health care professionals can learn how older adults use their spaces and how they might deploy home health care strategies in the best way possible.
Challenges
While inaccessible homes pose a big challenge to older adults, modifying them to their needs is even more challenging in today’s complex healthcare system and housing industry. The home modification industry as a whole is highly fragmented between healthcare professionals, home assessment, handyman services. Navigating between them while dealing with health conditions is not easy. Mistrust and lack of knowledge prevails.
For the research team, navigating this fragmented system to recruit and contact participants was very challenging. High cost of home modifications also limited the extent with which we could modify their homes to the fullest extent possible. This in turn reduced the potential health benefit of home modification. The home is intrinsically personal and that has a life’s long accumulated meaning to older adults. Changing to their medical needs was not always compatible with their ideal image of home, making them resist some of key recommendations.
An additional challenge came from trying to track the individual in their home. While GPS systems have provided a proven way to track location in outdoor spaces, these technologies do not work while indoors. The team explored a variety of different technological solutions, from the use of bluetooth beacons, to fingerprinting of WIFI routers. While these solutions showed some early promise, it was determined that their accuracy was not yet reliable enough to be used in the field.
In turn the team turned to utilizing a wearable camera that would capture an image on a 30 second interval for 24 hours continuously. Each image was then referenced against other images of the home to determine the location of the individual. This method had a number of challenges as images angles sometimes made locations difficult to decipher. Furthermore, periods of continuous movement would result in rooms being overlooked due to the low sampling rate. Finally, the participants sometimes felt uncomfortable wearing the camera, leading to additional form of potential data loss.
Despite these challenges, the context provided by the location alongside the biomarkers was seen as providing new insights into participant’s health in their home. Future work will aim to provide this data in a more reliable, less invasive and more automated fashion.