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AI Prediction of Brain Signals for Human Gait Using BCI Device and FBG Based Sensorial Platform for Plantar Pressure Measurements

by Asad Muhammad Butt, Hassan Alsaffar, Muhannad Alshareef and Khurram Karim Qureshi

Sensors 2022, 22(8), 3085; https://doi.org/10.3390/s22083085 - 18 Apr 2022

Cited by 9 | Viewed by 4315

Abstract

Artificial intelligence (AI) in developing modern solutions for biomedical problems such as the prediction of human gait for human rehabilitation is gaining ground. An attempt was made to use plantar pressure information through fiber Bragg grating (FBG) sensors mounted on an in-sole, in tandem with a brain-computer interface (BCI) device to predict brain signals corresponding to sitting, standing and walking postures of a person. Posture classification was attained with an accuracy range between 87–93% from FBG and BCI signals using machine learning models such as K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and naïve Bayes (NB). These models were used to identify electrodes responding to sitting, standing and walking activities of four users from a 16 channel BCI device. Six electrode positions based on the 10–20 system for electroencephalography (EEG) were identified as the most sensitive to plantar activities and found to be consistent with clinical investigations of the sensorimotor cortex during foot movement. A prediction of brain EEG corresponding to given FBG data with lowest mean square error (MSE) values (0.065–0.109) was made with the selection of a long-short term memory (LSTM) machine learning model when compared to the recurrent neural network (RNN) and gated recurrent unit (GRU) models. Full article

(This article belongs to the Special Issue Wearable Medical Sensors and Artificial Intelligence)

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15 pages, 2902 KiB

Open AccessArticle

POFBG-Embedded Cork Insole for Plantar Pressure Monitoring

by Débora Vilarinho, Antreas Theodosiou, Cátia Leitão, Arnaldo G. Leal-Junior, Maria De Fátima Domingues, Kyriacos Kalli, Paulo André, Paulo Antunes and Carlos Marques

Sensors 2017, 17(12), 2924; https://doi.org/10.3390/s17122924 - 16 Dec 2017

Cited by 74 | Viewed by 7078

Abstract

We propose a novel polymer optical fiber (POF) sensing system based on fiber Bragg gratings (FBGs) to measure foot plantar pressure. The plantar pressure signals are detected by five FBGs, in the same piece of cyclic transparent optical polymer (CYTOP) fiber, which are embedded in a cork insole for the dynamic monitoring of gait. The calibration and measurements performed with the suggested system are presented, and the results obtained demonstrate the accuracy and reliability of the sensing platform to monitor the foot plantar pressure distribution during gait motion and the application of pressure. This architecture does not compromise the patient’s mobility nor interfere in their daily activities. The results using the CYTOP fiber showed a very good response when compared with solutions using silica optical fibers, resulting in a sensitivity almost twice as high, with excellent repeatability and ease of handling. The advantages of POF (e.g., high flexibility and robustness) proved that this is a viable solution for this type of application, since POF’s high fracture toughness enables its application in monitoring patients with higher body mass compared with similar systems based on silica fiber. This study has demonstrated the viability of the proposed system based on POF technology as a useful alternative for plantar pressure detection systems. Full article

(This article belongs to the Special Issue Fiber Bragg Grating Based Sensors)

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