Changes in lower limb muscle synchronisation during walking on high-heeled shoes

© 2018 Institution of Engineering and Technology.All right reserved. The goal of this research was to investigate the effect of wearing high-heeled shoes (HHS) on lower limb muscle synchronisation during walking, using beta band (15–30 Hz) coherence analysis. Fifteen females with no previous neuromuscular disorders volunteered in this study. Surface electromyography in frequency domain was studied from rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM) and semitendinosus (ST) muscles during walking by subjects wearing HHS of three different heel heights (low – 4 cm, medium – 6 cm and high – 10 cm). Average coherence values were calculated for RF-VL, RF-VM and RF-ST muscles in beta band to analyse muscle pair synchronisation. In this study, significant increase in beta band coherence was found in all three muscle pairs during walking on HHS of different heel heights (p<0.05). Increased beta band coherence obtained from this study suggested that walking on HHS demands higher muscle pair synchronisation, to maintain stability around the knee joint

Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals

© 2017 IEEE. Freezing of Gait (FOG) is a highly debilitating and poorly understood symptom of Parkinson's disease (PD), causing severe immobility and decreased quality of life. Turning Freezing (TF) is known as the most common sub-type of FOG, also causing the highest rate of falls in PD patients. During a TF, the feet of PD patients appear to become stuck whilst making a turn. This paper presents an electroencephalography (EEG) based classification method for detecting turning freezing episodes in six PD patients during Timed Up and Go Task experiments. Since EEG signals have a time-variant nature, time-frequency Stockwell Transform (S-Transform) techniques were used for feature extraction. The EEG sources were separated by means of independent component analysis using entropy bound minimization (ICA-EBM). The distinctive frequency-based features of selected independent components of EEG were extracted and classified using Bayesian Neural Networks. The classification demonstrated a high sensitivity of 84.2%, a specificity of 88.0% and an accuracy of 86.2% for detecting TF. These promising results pave the way for the development of a real-time device for detecting different sub-types of FOG during ambulation

Image Recovery from Synthetic Noise Artifacts in CT Scans Using Modified U-Net

Computed Tomography (CT) is commonly used for cancer screening as it utilizes low radiation for the scan. One problem with low-dose scans is the noise artifacts associated with low photon count that can lead to a reduced success rate of cancer detection during radiologist assessment. The noise had to be removed to restore detail clarity. We propose a noise removal method using a new model Convolutional Neural Network (CNN). Even though the network training time is long, the result is better than other CNN models in quality score and visual observation. The proposed CNN model uses a stacked modified U-Net with a specific number of feature maps per layer to improve the image quality, observable on an average PSNR quality score improvement out of 174 images. The next best model has 0.54 points lower in the average score. The score difference is less than 1 point, but the image result is closer to the full-dose scan image. We used separate testing data to clarify that the model can handle different noise densities. Besides comparing the CNN configuration, we discuss the denoising quality of CNN compared to classical denoising in which the noise characteristics affect quality

Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain

This paper proposes an innovative methodology for finding how many lifting techniques people with chronic low back pain (CLBP) can demonstrate with camera data collected from 115 participants. The system employs a feature extraction algorithm to calculate the knee, trunk and hip range of motion in the sagittal plane, Ward&rsquo;s method, a combination of K-means and Ensemble clustering method for classification algorithm, and Bayesian neural network to validate the result of Ward&rsquo;s method and the combination of K-means and Ensemble clustering method. The classification results and effect size show that Ward clustering is the optimal method where precision and recall percentages of all clusters are above 90, and the overall accuracy of the Bayesian Neural Network is 97.9%. The statistical analysis reported a significant difference in the range of motion of the knee, hip and trunk between each cluster, F (9, 1136) = 195.67, p &lt; 0.0001. The results of this study suggest that there are four different lifting techniques in people with CLBP. Additionally, the results show that even though the clusters demonstrated similar pain levels, one of the clusters, which uses the least amount of trunk and the most knee movement, demonstrates the lowest pain self-efficacy

Selecting optimal EEG channels for mental tasks classification : an approach using ICA

This paper presents a systematic method to select optimal electroencephalography (EEG) channels for three mental tasks-based brain-computer interface (BCI) classification. A blind source separation (BSS) technique based on independent component analysis (ICA) with its back-projecting of the scalp map was used for selecting the optimal EEG channels. The three mental tasks included: mental letter composing, mental arithmetic and mental Rubik's cube rolling. Based on a power spectral density (PSD), the features of the two-channel EEG data were extracted, and then were classified by Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map showed that the prominent channels could be selected for dominant features from original six EEG channels (C3, C4, P3, P4, O1, O2) to four dominant channels (P3, O1, C4, O2) with the best two EEG channels selection at O1&C4. Two channel combinations classification yielded to the best two EEG channels of O1&C4 with an accuracy of 76.4%, followed by P3&O2 with an accuracy of 74.5%; P3&C4 with an accuracy of 71.9% and O1&O2 with an accuracy of 70%

Driver fatigue classification with independent component by entropy rate bound minimization analysis in an EEG-based system

This paper presents a two-class electroencephal- ography-based classification for classifying of driver fatigue (fatigue state versus alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction, and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8%, and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor), and Bayesian neural network (classifier) provides the best outcome with a p-value< 0.05 with the highest value of area under the receiver operating curve (AUC-ROC = 0.93) against other methods such as power spectral density as feature extractor (AUC-ROC = 0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications