A rehabilitation framework based on motor imagery induced wheelchair movement using fuzzy vector quantization

Ghosh, Ahona; Saha, Sriparna; Ghosh, Lidia

doi:10.1007/s41870-023-01359-8

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Other1

Book1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Systematic Literature Review: Implemented Motor Imagery Recognition Methods

Arizi,

Setiawan,

Wibirama

2024

2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)

View full text Add to dashboard Cite

Systematic Literature Review: Implemented Motor Imagery Recognition Methods

Arizi,

Setiawan,

Wibirama

2024

2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)

View full text Add to dashboard Cite

Machine Learning Approach for Robot Navigation Using Motor Imagery Signals

Das,

Shankar,

Ghosh

et al. 2023

Handbook of Research on AI and ML for Intelligent Machines and Systems

View full text Add to dashboard Cite

Electroencephalography (EEG) signals have been used for different healthcare applications like motor and cognitive rehabilitation. In this study, motor imagery data of different subjects' rest vs. movement and different movements is categorized from a publicly available dataset. The authors have first applied a lowpass filter to the EEG signals to reduce noise and a fast fourier transform analysis to extract features from the filtered data. Utilizing principal component analysis, relevant features are selected. With an accuracy of 95.02%, they have classified rest vs. movement using the k-nearest neighbor algorithm. Using the random forest algorithm, they have classified various movement types with an accuracy of 96.45%. The success in differentiating between movement and rest raises the possibility that EEG signals can recognize a user's intention to move. Accurately classifying different movement types opens the possibility of navigating robots accordingly in the real-time scenario for people with motor disabilities to assist them with robotic arms and prosthetic limbs.

show abstract

A rehabilitation framework based on motor imagery induced wheelchair movement using fuzzy vector quantization

Cited by 2 publications

References 39 publications

Systematic Literature Review: Implemented Motor Imagery Recognition Methods

Systematic Literature Review: Implemented Motor Imagery Recognition Methods

Machine Learning Approach for Robot Navigation Using Motor Imagery Signals

Contact Info

Product

Resources

About