AI-Embedded Wheelchairs for Assistive Mobility and Health Monitoring: A Comparative Review and Integrated Framework

Abstract

The amalgamation of artificial intelligence (AI), the Internet of Things (IoT), and embedded systems has catalyzed substantial progress in assistive mobility; nonetheless, notable deficiencies persist in terms of cost, adaptability, and comprehensive usefulness.  This paper synthesizes various seminal works to examine contemporary smart wheelchair architectures, highlighting significant limitations: disjointed sensor-actuator systems, excessive costs in low-resource environments, and insufficient contextual responsiveness.  We present a theoretical framework that utilizes ESP32’s dual-core edge processing to integrate navigation control, medical-grade health monitoring, and adaptive power management.  A comparative analysis indicates that this integration may resolve India's infrastructure difficulties by focusing on: Latency below 200ms, 95% accuracy in vital signs and, Continuous operation for less than 5% of commercial expenses. Whereas previous research concentrates on singular attributes, our approach underscores compassionate autonomy—a design philosophy that prioritizes fail-safes and multi-modal adaptation.  Hardware validation is pending, although this evaluation underscores the feasibility of low-cost, integrated assistive environments for worldwide applications.