A Myoelectric Signal-Driven Intelligent Wheelchair System | #Sciencefather #Researcherawards



Introduction

The development of assistive mobility devices has undergone significant transformation in recent years, moving from traditional manual and joystick-controlled wheelchairs to more intelligent, user-centric systems. This research proposes a novel electric wheelchair controlled by surface electromyographic (sEMG) signals generated by the occlusal muscles during clenching. Unlike conventional devices that require hand or foot coordination, this system offers a hands-free and more inclusive approach, particularly targeting users with paraplegia and severe mobility impairments.

Myoelectric Signal-Based Control

The foundation of this research lies in leveraging sEMG signals from occlusal muscles to generate control commands for the wheelchair. By detecting and processing these myoelectric signals during clenching, the system bypasses traditional motor functions, providing a unique solution for individuals unable to use their limbs effectively. This method demonstrates an innovative step toward non-invasive and reliable control strategies for assistive mobility devices.

Integration of Cloud and IoT Technologies

A key feature of this system is the integration of Wi-Fi 6E-based communication for seamless data transmission. The electric wheelchair communicates critical user data and GPS information to the cloud in real time. This IoT-driven design ensures robust connectivity, enabling remote monitoring, improved accessibility, and efficient data-driven insights into user mobility and safety.

Emergency Response and Safety Mechanisms

Cloud-enabled connectivity facilitates not only enhanced usability but also critical safety features. By instantly transmitting user location and movement patterns, the system supports rapid emergency responses in case of accidents or health complications. This aspect of the design underscores the broader impact of combining assistive technology with telecommunication advancements.

Standardization of sEMG Electrode Setup

The accuracy of the wheelchair’s control mechanism depends heavily on proper electrode placement and signal analysis. This research acknowledges the role of standardized sEMG electrode setup and established signal processing techniques to ensure reliable performance. Optimized electrode configuration enhances signal clarity, reduces noise, and ensures robust control of the wheelchair across different users.

Proof-of-Concept and Future Directions

This work serves as a proof-of-concept feasibility study rather than a fully validated clinical trial. While preliminary results highlight the potential of occlusal muscle-based myoelectric control, further research is required to optimize hardware, enhance machine learning algorithms for signal interpretation, and conduct large-scale clinical validation. Future efforts may also expand on user adaptability, personalization, and long-term usability in daily activities.

Global Particle Physics Excellence Awards

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Hashtags

#Sciencefather, #Reseacherawards, #MyoelectricWheelchair, #sEMGControl, #AssistiveTechnology, #ParaplegiaSupport, #OcclusalMuscleSignals, #SmartMobility, #HealthcareInnovation, #CloudConnectedDevices, #WiFi6E, #RehabilitationTech, #Neuroengineering, #HumanMachineInterface, #IntelligentWheelchair, #MedicalIoT, #SignalProcessing, #ProofOfConcept, #MobilityResearch, #WearableElectrodes, #DisabilityInclusion, #NextGenHealthcare,

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