EEG and EMG-based human-machine interface for navigation of mobility-related assistive wheelchair (MRA-W)

Welihinda, D.V.D.S.; Gunarathne, L.K.P.; Herath, H.M.K.K.M.B.; Yasakethu, S.L.P.; Madusanka, Nuwan; Lee, Byeong-Il

doi:10.1016/j.heliyon.2024.e27777

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…Dey proposed an intelligent wheelchair system for head posture navigation, which integrates modules such as acceleration, ultrasound, and photoresistor, and the system can realize the wheelchair movement in five directions according to different head postures [19]. Welihinda et al proposed a hybrid control system to operate a powered wheelchair using a combination of EEG and EMG, and developed an EEG-based user attention detection system and an EMG-based navigation system [20]. Regarding human-computer interaction between the guardian and the wheelchair, Lu et al used Internet of Things (IoT) technology to monitor the data of the position of the electric wheelchair, the tire wear, the battery power level, etc., and the guardian can know the user's operation status [21].…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Wheelchair Multimode Human–Computer Interaction and Assisted Driving Technology

Cui,

Shang,

et al. 2024

Actuators

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The traditional wheelchair focuses on the “human-chair” motor function interaction to ensure the elderly and people with disabilities’ basic travel. For people with visual, hearing, physical disabilities, etc., the current wheelchairs show shortcomings in terms of accessibility and independent travel for this group. Therefore, this paper develops an intelligent wheelchair with multimodal human–computer interaction and autonomous navigation technology. Firstly, it researches the multimodal human–computer interaction technology of occupant gesture recognition, speech recognition, and head posture recognition and proposes a wheelchair control method of three-dimensional head posture mapping the two-dimensional plane. After testing, the average accuracy of the gesture, head posture and voice control modes of the motorized wheelchair proposed in this study reaches more than 95 percent. Secondly, the LiDAR-based smart wheelchair indoor autonomous navigation technology is investigated to realize the autonomous navigation of the wheelchair by constructing an environment map, using A* and DWA algorithms for global and local path planning, and adaptive Monte Carlo simulation algorithms for real-time localization. Experiments show that the position error of the wheelchair is within 10 cm, and the heading angle error is less than 5° during the autonomous navigation. The multimode human–computer interaction and assisted driving technology proposed in this study can partially compensate and replace the functional deficiencies of the disabled population and improve the quality of life of the elderly and disabled population.

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Section: Introductionmentioning

confidence: 99%