Multisensory Human-Machine Interfaces for Wheelchair Operation and Posture Monitoring

Recently, robotic wheelchairs commanded by human-machine interfaces (HMIs) have gained recognition for enhancing the quality of life of people with physical disabilities. In this sense, implementing sensors that allow for accurate and comfortable user intention recognition remains challenging. Additionally, posture monitoring for pressure ulcer prevention in wheelchair users is often overlooked. In this study, three HMIs are proposed to recognize the user's intention using information linked to head and neck movements, and visually evoked potentials to control an electric-powered wheelchair in four directions: forward, left, right, and back. These HMIs incorporate technologies, such as an inertial measurement unit (IMU)-based system, pressure sensors based on polymeric optical fiber (POF), and steady-state visual-evoked potential (SSVEP)-based brain-computer interface (BCI) to generate control commands. The POF-based pressure sensors also allow for posture classification. The HMIs were evaluated functionally, and the user's experience (UX) was considered from healthy subjects. The head-motion-based system obtained the highest accuracy (ACC) rate (similar to 0.99) and less workload. In contrast, the BCI reached the highest satisfaction and usability, whereas the neck-motion-based system achieved the lowest latency (similar to 28 ms). The posture classification system achieved an acceptable ACC (similar to 0.80), latency (similar to 117 ms), and good perception. These results have great implications for the design of wheelchair systems to improve the independence of people with reduced mobility using information from multiple sources and for posture monitoring toward the prevention of pressure ulcer generation.