The mobility behavior of individuals with visual impairments differs significantly from that of individuals with non-visual impairments when experiencing emotional stress, particularly in unfamiliar environments. They often struggle to manage stress independently, indicating the need for an early warning system or a supportive companion to ensure their safety. This study introduces a non-invasive stress detection system based on a photoplethysmography (PPG) sensor and a fuzzy logic model to classify non-stress and stress conditions. The system analyzes five physiological parameters: heart rate (HR), heart rate variability (HRV), respiratory rate (RR), peripheral oxygen saturation (SpO2), and body temperature (BT). Each parameter is categorized into three linguistic levels, resulting in 243 fuzzy rules embedded in a stand-alone device. Experiments were conducted with visually impaired students under three scenarios—relaxation, cognitive, and physical—across five phases, with 120 samples collected per phase. The system achieved a classification accuracy of 99.5%. © 2025 IEEE.