In this study we propose the use of functional near-infrared spectroscopy (fNIRS) and deep learning for the assessment of human pain.

Jun 12, 2021 · Results: The results showed that the deep learning models exhibited favourable results in the identification of different types of pain in our ...

Abstract—Assessing pain in patients unable to speak (also called non-verbal patients) is extremely complicated and often is done by clinical judgement.

This study proposes the use of functional near-infrared spectroscopy (fNIRS) and deep learning for the assessment of human pain and shows that the Bi-LSTM model

Assessing pain in patients unable to speak (also called non-verbal patients) is extremely complicated and often is done by clinical judgement.

Fingerprint. Dive into the research topics of 'Pain Assessment based on fNIRS using Bi-LSTM RNNs'. Together they form a unique fingerprint.

Dec 24, 2020 · The results showed that wavelet-based features produced the highest accuracy (\(88.33\%\)) to distinguish between heat and cold pain while ...

Feb 14, 2024 · In this study, we aim to expand previous studies by exploring the use of deep learning models Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) ...

This study used fNIRS signals to predict the state of pain in humans using machine learning methods and showed that wavelet-based features produced the ...

Automated facial expression based pain assessment using deep convolutional neural network ... Pain assessment based on fNIRS using Bi-LSTM RNNs. 10th ...