2026 ASEE Annual Conference & Exposition

The rapid growth of the aging population in the United States poses urgent challenges for equitable healthcare. Traditional in-clinic assessments often miss early signs of decline, as older adults typically see healthcare providers only during scheduled appointments or after noticeable symptoms appear. Research has demonstrated that at-home assessments can improve outcomes, yet current approaches, such as wearable sensors or camera-based monitoring, face barriers related to compliance, privacy, and intrusiveness. To address these gaps, this NSF Improving Undergraduate STEM Education: Hispanic-Serving Institutions (IUSE:HSI) project is developing a novel, non-intrusive, real-time at-home monitoring system that uses floor vibration sensing and advanced machine learning to estimate gait parameters and infer health status changes.

The intellectual merit of this work lies in its integration of civil engineering, computer engineering, and mathematics to create accurate and adaptive algorithms for gait detection and analysis. Building on prior federally supported research, the project advances event classification, gait parameter estimation, and personalized modeling through deep learning and federated learning. A recently completed summer research project involving three community college students contributed to this effort by developing a system to synchronize floor vibration data with ground-truth labels from security cameras. This system generates high-quality labeled datasets, a critical step for training and validating the AI models. By leveraging continuous vibration data from a newly instrumented “living structure” testbed, the research will generate both general and individualized walker models. These models will support real-time health monitoring while preserving privacy through edge-based computation and decentralized learning.

Equally central is the project’s commitment to broadening participation in STEM. Conducted at a Hispanic-Serving Institution, this work provides a diverse context for engaging students in authentic, interdisciplinary research experiences that connect engineering innovation with pressing societal needs. Through inclusive mentoring, structured training, and integration of experiential learning modules into mathematics and engineering courses, the project cultivates an inclusive mindset among participants and prepares students for future success in STEM fields.