HoloLens Clutter Detection and Senior Care Support System
Primary author: Aaron Crandall
Co-author(s): Konstantin Shvedov; Jarred Eagley; Austin Craigie; Jacob Stocklass
Primary college/unit: Voiland College of Engineering and Architecture
With the advent of modern medicine and a declining birthrate, our communities have been faced with an aging population. Older adults who live independently wish to stay safe, retain their independence, and not be a burden on their families. The Center for Advanced Studies in Adaptive Systems (CASAS) has ongoing work in the field of gerontechnology to support adults as they age. Significant research into caregiver needs pointed to issues of home maintenance and tripping hazards among independent older adults. Caregivers for seniors needed to know more about whether a home is cluttered or safe without being intrusive to the senior’s daily lives. This work’s hypothesis is that a 3D mapping system, notably the Microsoft HoloLens, can be used to build in-home models and track the changes in safe walking paths, in-home clutter, and detect tripping hazards. This information is provided to caregivers to help with home care and safety issue tracking. This project has developed a system which builds and algorithmically analyzes 3D maps of the home for clutter, renders the state of the home from a clutter and safety perspective, then notifies caregivers if issues are found. Ongoing work to test and evaluate the quality of the tools and to get user feedback about its effectiveness are underway. Once complete, this work shall provide new insights into how to sense and analyze living spaces for home care, and methods of notifying caregivers of when an independent senior might need an intervention to help take care of living spaces.