Segmentation of Augmented Reality 3D Meshes to Discover In Home Safe Walking Spaces for Older Adults
Authors: Crandall Aaron 
Published: November 2024

Falling continues to be a significant risk factor for older adults and other mobility limited individuals. Around 16% of all US adults 65 and over fell at least once in a 3 month window, and falling affects 50% of adults over the age of 80. These falls account for 0.1% of all US healthcare costs, or about $4 billion per year. Falls are the number one reason older adults move from independent living to long term care, so any reduction in their frequency has significant benefits to our communities. Around 55% of all fall related injuries occur within the home, and 35% to 40% of those from environmental conditions such as clutter, slippery floors, narrow walkways, and poor lighting. Monitoring and maintaining clear, tripping hazard free pathways in living spaces is invaluable in helping people live independently and safely in their home. This paper proposes and demonstrates a space segmentation approach to discovering which areas of the home are classified as ‘walkable’ in a safe manner. The goal is to then provide residents and caregivers guidance about reducing in-home environmental risk factors for falls. The system leverages 3D mesh-based maps of the home gathered by the Microsoft HoloLens v1's sensors. The algorithm was designed to work with many different kinds of sensor platforms that provide mesh-style maps of spaces, such as LiDAR, vision-based solutions, and other Augmented Reality platforms. The collection of algorithms needed for the system were collected into the Walking Spaces Algorithm (WSA) package. These algorithms first used a denoising floor detection algorithm and a waterfall-based furniture and clutter detection to handle the noisy raw mesh data to help identify uncluttered floor spaces. The resulting reduced noise 3D model of the home was then processed using a segmentation algorithm to find pathways that would be considered safe under the United States Occupational Safety and Health Administration (OSHA) guidelines. The home's safe and unsafe spaces were then visualized for users to see and understand where the home is safe and accessible or not. To test the WSA algorithm, data was collected from both lab and apartment spaces for testing. All data collected was stored on a database backend with a web frontend to see real time updates of the home as the resident carried the AR headset through the space, with final images being generated on a daily basis for long term analysis as the home's space changed due to clutter or furniture movements. The long term goals of these technologies are to monitor the living space’s clutter and clear walkways over time. The information it generates shall then be provided to the residents and their caregivers during environmental home assessments. It informs them about how well the home is being maintained so proactive interventions may be taken before a fall occurs. Overall, the results from the WSA algorithm in the testing spaces were successful at identifying open vs. cluttered spaces and allowed the algorithm to demonstrate where residents had OSHA-rated clear walking spaces in their home.