Eee projects on speech recognition controlled wheelchair for disabled — Simulation & Power Modeling

Abstract — Eee projects on speech recognition controlled wheelchair for disabled

advances assistive mobility through three main contributions. First, we introduce an inclusive speech-recognition module built from a fine-tuned deep learning model trained on a custom dataset that incorporates recordings from users with mild speech impairments. This adaptation improves robustness to non-standard pronunciation and maintains reliable command execution under realistic noise conditions (70–75 dB), achieving a Word Error Rate of 6.7% in quiet environments. Second, rather than proposing a new SLAM method, we develop a system-level navigation framework that optimally integrates 2D LiDAR-based SLAM (GMapping), AMCL localization, and a dual-level voice-command interface within a real-time coordination layer. This includes a quantitatively parameterized safety module featuring adaptive speed modulation and experimentally calibrated emergency-stop thresholds, ensuring reliable operation in dynamic indoor environments. Third, we conduct an extensive experimental campaign in both simulation and real-world conditions to provide a reproducible and quantitative evaluation of system performance. Tests involving dynamic obstacles (pedestrians, wheeled carts, small animals), constrained passages, and diverse acoustic settings demonstrate a mean localization error below 10 cm, a 94% goal-completion rate, and an end-to-end voice-to-motion latency of 0.8 s. Together, these contributions provide a low-cost, experimentally validated assistive-mobility platform that emphasizes inclusive voice interaction, robust real-time navigation, and safety-aware behavior.

Analytical & State-Space Control Methodology

The proposed framework moves beyond component-level studies by offering a coherent, deployable, and reproducible solution for everyday indoor environments. Wheelchairs have long been an indispensable aid for individuals with motor disabilities. The evolution of models, from manual to powered wheelchairs, is embedded in a history marked by both technological and social advances 1 , 2 . Historical studies show that these devices are not only tools for mobility but also symbols of integration or exclusion depending on the context 3 . As early as the 1990s, the first studies on intelligent control applied to wheelchairs paved the way for automated and adaptive control solutions 4 .

Simulink Waveforms & Power Quality Findings

Figure 1: Power Converter Circuit Schematic & Electrical Interfacing for Eee Projects On Speech Recognition Controlled Wheelchair For Disabled The history of the powered wheelchair thus illustrates a progressive evolution of mobility aids, from rudimentary models to today’s intelligent devices. On the sociocultural level, Wolfson 3 analyzes two centuries of wheelchair design, demonstrating that these devices are not merely mechanical tools but also identity and social markers. Furthermore, Cooper’s work 4 introduced at an early stage the concepts of automated assistance and sensor integration to improve safety and maneuverability. These foundations have served as the basis for contemporary research on smart wheelchairs.

Scope & Practical Applications

• High-efficiency DC-DC converters and isolated resonant topologies (LLC / DAB)
• Sinusoidal and Space Vector PWM inverters for grid integration & industrial motor drives
• MOSFET / IGBT gate driver optimization with active Miller-clamp shoot-through protection
• Power quality conditioning with Total Harmonic Distortion (THD) strictly compliant with IEEE 519
• Complete Simscape .SLX model files, parameter calculation scripts, and IEEE dissertation reports