Publications by authors named "Neelum Y Sattar"
This research study proposes a unique framework that takes input from a surface electromyogram (sEMG) and functional near-infrared spectroscopy (fNIRS) bio-signals. These signals are trained using convolutional neural networks (CNN). The framework entails a real-time neuro-machine interface to decode the human intention of upper limb motions.
View Article and Find Full Text PDFArticle Synopsis
- Prosthetic arms help individuals with amputations perform daily tasks, but current technology faces challenges in motion prediction and rehabilitation for transhumeral amputees.
- This study proposes a functional near-infrared spectroscopy (fNIRS) method to identify six specific upper limb motions by analyzing brain signals from both healthy individuals and transhumeral amputees.
- By utilizing an artificial neural network on the collected fNIRS data, the approach achieved a classification accuracy of 78%, indicating promising potential for real-time control of transhumeral prostheses.