Designing a Smart Mat Intelligent Monitoring System for Single-person Households

Abstract

This study designed and evaluated a low-cost, non-invasive smart mat prototype for simulated monitoring of activity, inactivity, and environmental safety in single-person living environments. The system combined an Arduino Mega 2560 central controller, four Arduino Nano 33 BLE Sense nodes positioned at the mat corners, an ESP32 Wi-Fi module, a real-time clock, SD-card logging, an LCD interface, an RGB alert indicator, a light-dependent resistor, and temperature, humidity, and gas-sensing components. Laboratory simulated-use testing over five days examined corner-specific analogue signal responses, baseline and movement-related signal patterns, A0-A1 relationships, walk-rate trends, and basic environmental-sensor calibration. The Arduino Nano 33 BLE Sense modules were used as distributed sensing and communication nodes; A0 and A1 were treated as external analogue proxy channels representing vertical compression-related and lateral tilt/motion-related responses, respectively, and not as direct clinical pressure measurements. The prototype produced distinguishable baseline and movement-associated signal changes, demonstrated measurable relationships among selected analogue channels, and generated a simulated caregiver summary combining activity, environmental status, and device diagnostics. Calibration tests showed strong mathematical fits over the limited tested ranges for temperature, humidity, and light, while the MQ-7 gas sensor showed a nonlinear response under simplified exposure conditions and should be interpreted only as a preliminary indicator. Overall, the proposed smart mat demonstrates engineering feasibility for a privacy-preserving ambient monitoring platform; however, the present work is a proof-of-concept prototype rather than a validated fall-detection or assisted-living device, and future studies require calibrated load testing, larger datasets, human-subject protocols, real-home validation, privacy/security assessment, and formal false-positive/false-negative analysis.