Date of Award

6-9-2026

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2026

Departments

Computer Engineering; Computer Science and Engineering; Electrical Engineering; Electrical and Computer Engineering

First Advisor

Radhika Grover

Second Advisor

Michael Schimpf

Abstract

Individuals with special needs or attention deficit disorders struggle to maintain structured daily routines independently, yet existing reminder technologies rely on visual interfaces, touchscreens, and fine motor control that make them inaccessible to this population. The Gesture-Controlled Reminder Device addresses these shortcomings through a compact, wearable assistive technology that eliminates these barriers entirely.

Teachers and caregivers can record personalized voice reminders directly onto the device, which are delivered to the user through haptic vibration and interacted with via simple hand gestures to repeat, snooze, or dismiss them. By processing all interactions on a custom PCB built around the Raspberry Pi Compute Module, the device preserves user privacy, requires no network connectivity, and remains fully accessible to individuals with limited fine motor skills.

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