Assistive technologies designed to enhance the autonomy of people with reduced mobility require accessible solutions that integrate visual perception and intelligent environmental control. This study presents the design and technical validation of a proof-of-concept eye-tracking assistance system aimed at enhancing environmental interaction and monitoring for individuals with reduced mobility. The proposed solution integrates infrared gaze tracking, object detection based on convolutional neural networks (CNNs), and wireless actuation using Internet of Things (IoT) protocols. The system is implemented with low-cost hardware, including consumer cameras and a microcontroller with Wi-Fi connectivity, all mounted on a 3D-printed head-worn structure. Pupillary coordinates are estimated using the PuReST algorithm, and gaze mapping is performed using a calibrated linear regression model. A YOLOv11n-based object detection model identifies key household objects within the user's field of view. These objects are actuated by prolonged blinks. Experimental validation in a controlled environment demonstrates the system's effectiveness, achieving a gaze estimation error of less than 41 pixels and an average accuracy (mAP@0.5:0.95) of over 70.9% in object detection tasks. These results confirm the feasibility of a modular, cost-effective, and portable solution for smart home interaction in assisted living settings.