Improving Wheelchair Mobility through Enhanced Kalman Filtering

Abstract

Electric wheelchairs are essential for individuals with lower motor disabilities, enabling daily mobility typically via joystick control. However, users with cerebral palsy often struggle with accurate joystick maneuvering due to motor impairments. This paper proposes a novel control strategy, \textit{Enhanced Kalman Filter-based Wheelchair Control} (\textbf{EKFWC}), which compensates for these deficiencies using a Kalman Filter-based estimation model grounded in state-space representations. The system continuously estimates and corrects wheelchair velocity by filtering noisy sensor data and adjusting joystick commands in real time, aligning motion with optimal speeds for varying terrain stored in a reference table. EKFWC's performance is benchmarked against a hybrid sensor fusion model, \textbf{REF\textsubscript{EKFPF}}, which combines an Extended Kalman Filter and a Modified Particle Filter. Simulation results show that EKFWC yields lower mean absolute error (MAE) and percentage estimation error compared to both EKF and REF\textsubscript{EKFPF}. Additionally, EKFWC significantly reduces navigation time: on smooth indoor surfaces, it completes a point-to-point traversal in $71~\text{sec}$ compared to $100~\text{sec}$ for REF\textsubscript{EKFPF}; on uneven and very rough outdoor terrains, EKFWC achieves traversal times of $87~\text{sec}$ and $95~\text{sec}$, respectively. These improvements stem from EKFWC's ability to maintain smoother trajectories with minimal deviation and prompt response to terrain variations, avoiding the zigzagging, wide turns, and hesitations observed in the reference model. Overall, EKFWC demonstrates robust real-time adaptability and enhanced path efficiency across diverse environments.