A gait stability evaluation method based on wearable acceleration sensors.

In this study, an accurate tool is provided for the evaluation of the effect of joint motion effect on gait stability. This quantitative gait evaluation method relies exclusively on the analysis of data acquired using acceleration sensors. First, the acceleration signal of lower limb motion is collected dynamically in real-time through the acceleration sensor. Second, an algorithm based on improved dynamic time warping (DTW) is proposed and used to calculate the gait stability index of the lower limbs. Finally, the effects of different joint braces on gait stability are analyzed. The experimental results show that the joint brace at the ankle and the knee reduces the range of motions of both ankle and knee joints, and a certain impact is exerted on the gait stability. In comparison to the ankle joint brace, the knee joint brace inflicts increased disturbance on the gait stability. Compared to the joint motion of the braced side, which showed a large deviation, the joint motion of the unbraced side was more similar to that of the normal walking process. In this paper, the quantitative evaluation algorithm based on DTW makes the results more intuitive and has potential application value in the evaluation of lower limb dysfunction, clinical training and rehabilitation.

MUNIX repeatability evaluation method based on FastICA demixing.

To enhance the reproducibility of motor unit number index (MUNIX) for evaluating neurological disease progression, this paper proposes a negative entropy-based fast independent component analysis (FastICA) demixing method to assess MUNIX reproducibility in the presence of inter-channel mixing of electromyography (EMG) signals acquired by high-density electrodes. First, composite surface EMG (sEMG) signals were obtained using high-density surface electrodes. Second, the FastICA algorithm based on negative entropy was employed to determine the orthogonal projection matrix that minimizes the negative entropy of the projected signal and effectively separates mixed sEMG signals. Finally, the proposed experimental approach was validated by introducing an interrelationship criterion to quantify independence between adjacent channel EMG signals, measuring MUNIX repeatability using coefficient of variation (CV), and determining motor unit number and size through MUNIX. Results analysis shows that the inclusion of the full (128) channel sEMG information leads to a reduction in CV value by $1.5 \pm 0.1$ and a linear decline in CV value with an increase in the number of channels. The correlation between adjacent channels in participants decreases by $0.12 \pm 0.05$ as the number of channels gradually increases. The results demonstrate a significant reduction in the number of interrelationships between sEMG signals following negative entropy-based FastICA processing, compared to the mixed sEMG signals. Moreover, this decrease in interrelationships becomes more pronounced with an increasing number of channels. Additionally, the CV of MUNIX gradually decreases with an increase in the number of channels, thereby optimizing the issue of abnormal MUNIX repeatability patterns and further enhancing the reproducibility of MUNIX based on high-density surface EMG signals.

ULFAC-Net: Ultra-Lightweight Fully Asymmetric Convolutional Network for Skin Lesion Segmentation.

Segmentation of skin lesions is a critical step in the process of skin lesion diagnosis. Such segmentation is challenging due to the irregular shape, fuzzy contours and severe noise interference in the skin lesion region. Existing deep learning-based skin lesion segmentation methods are usually computationally expensive, hindering their deployment in dermoscopic devices with poor computational power. To address these challenges, we propose an ultralightweight fully asymmetric convolutional network for skin lesion segmentation, called ULFAC-Net. we use a parallel asymmetric convolutional (PAC) module to extract features instead of the traditional square convolution, and innovatively propose a PAC module with dual attention (Att-PAC) to enhance the feature representation. Based on the PAC and Att-PAC modules, we further propose a lightweight textual information submodule. To balance the number of parameters and performance of the model, we also hand-design an asymmetric encoder-decoder architecture. In this paper, we validate the effectiveness and robustness of the proposed ULFAC-Net on four publicly available skin lesion segmentation datasets (ISIC2018, ISBI2017, ISIC2016 and PH2 datasets). The experimental results show that ULFAC-Net achieves competitive segmentation performance with only 0.842 million(0.842M) parameters and 3.71 gigabytes of floating point operations (GFLOPs) compared to other state-of-the-art methods.

Evaluation method of motor unit number index based on optimal muscle strength combination.

Repeatability is an important attribute of motor unit number index (MUNIX) technology. This paper proposes an optimal contraction force combination for MUNIX calculation in an effort to improve the repeatability of this technology. In this study, the surface electromyography (EMG) signals of the biceps brachii muscle of eight healthy subjects were initially recorded with high-density surface electrodes, and the contraction strength was the maximum voluntary contraction force of nine progressive levels. Then, by traversing and comparing the repeatability of MUNIX under various combinations of contraction force, the optimal combination of muscle strength is determined. Finally, calculate MUNIX using the high-density optimal muscle strength weighted average method. The correlation coefficient and the coefficient of variation are utilized to assess repeatability. The results show that when the muscle strength combination is 10, 20, 50 and 70% of the maximum voluntary contraction force, the repeatability of MUNIX is greatest, and the correlation between MUNIX calculated using this combination of muscle strength and conventional methods is high (PCC > 0.99), the repeatability of the MUNIX method improved by 11.5-23.8%. The results indicate that the repeatability of MUNIX differs for various combinations of muscle strength and that MUNIX, which is measured with a smaller number and lower-level contractility, has greater repeatability.

Label smoothing and task-adaptive loss function based on prototype network for few-shot learning.

Aiming at solving the problems of prototype network that the label information is not reliable enough and that the hyperparameters of the loss function cannot follow the changes of image feature information, we propose a method that combines label smoothing and hyperparameters. First, the label information of an image is processed by label smoothing regularization. Then, according to different classification tasks, the distance matrix and logarithmic operation of the image feature are used to fuse the distance matrix of the image with the hyperparameters of the loss function. Finally, the hyperparameters are associated with the smoothed label and the distance matrix for predictive classification. The method is validated on the miniImageNet, FC100 and tieredImageNet datasets. The results show that, compared with the unsmoothed label and fixed hyperparameters methods, the classification accuracy of the flexible hyperparameters in the loss function under the condition of few-shot learning is improved by 2%-3%. The result shows that the proposed method can suppress the interference of false labels, and the flexibility of hyperparameters can improve classification accuracy.

Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network.

Epilepsy is a chronic neurological disorder which can affect 65 million patients worldwide. Recently, network based analyses have been of great help in the investigation of seizures. Now graph theory is commonly applied to analyze functional brain networks, but functional brain networks are dynamic. Methods based on graph theory find it difficult to reflect the dynamic changes of functional brain network. In this paper, an approach to extracting features from brain functional networks is presented. Dynamic functional brain networks can be obtained by stacking multiple functional brain networks on the time axis. Then, a tensor decomposition method is used to extract features, and an ELM classifier is introduced to complete epilepsy prediction. In the prediction of epilepsy, the accuracy and F1 score of the feature extracted by tensor decomposition are higher than the degree and clustering coefficient. The features extracted from the dynamic functional brain network by tensor decomposition show better and more comprehensive performance than degree and clustering coefficient in epilepsy prediction.

Multi-feature gait recognition with DNN based on sEMG signals.

This study proposed a gait recognition method based on the deep neural network of surface electromyography (sEMG) signals to improve the stability and accuracy of gait recognition using sEMG signals of the lower limbs. First, we determined the parameters of time domain features, including the mean of absolute value, root mean square, waveform length, the number of zero-crossing points of the sEMG signals after noise elimination, and the frequency domain features, including mean power frequency and median frequency. Second, the time domain feature and frequency domain feature were combined into a multi-feature combination. Then, the classifier was trained and used for gait recognition. Finally, in terms of the recognition rate, the classifier was compared with the support vector machine (SVM) and extreme learning machine (ELM). The results showed the method of deep neural network (DNN) had a better recognition rate than that of SVM and ELM. The experimental results of the participants indicated that the average recognition rate obtained with the method of DNN exceeded 95%. On the other hand, from the statistical results of standard deviation, the difference between subjects ranged from 0.46 to 0.94%, which also proved the robustness and stability of the proposed method.

Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms.

Accuracy is a key index of human gait recognition. In this paper, we propose an improved gait recognition algorithm, which combines multiple feature combination and artificial bee colony for optimizing the support vector machine (ABC-SVM). Firstly, considering the complexity characteristics of surface electromyography (sEMG) signals, four types of features are extracted from the denoised sEMG signals, including the time-domain features of integral of absolute value (IAV), variance (VAR), and number of zero-crossing (ZC) points, frequency-domain features of mean power frequency (MPF) and median frequency (MF), and wavelet features and fuzzy entropy features. Secondly, the classifiers of SVM, linear discriminant analysis (LDA), and extreme learning machine (ELM) are employed to recognize the gait with obtained features, including singe-class features, multiple combination features, and optimized features of dimension reduction by principal component analysis (PCA). Thirdly, the penalty coefficient and kernel function parameter of the SVM classifier are optimized by the ABC algorithm, and the influence of different features and classifiers on the recognition results is studied. Finally, the feature samples selected to construct the SVM classifier are trained and recognized. Results show that the classification performance of the ABC-SVM classifier is significantly better than that of the nonoptimized SVM classifier, and the average recognition rate is increased by 3.18%. In addition, the combined feature samples (time-domain, frequency-domain, wavelet, and fuzzy entropy features) not only improve the gait classification accuracy but also enhance the recognition stability.

A Preliminary Study of Effects of Channel Number and Location on the Repeatability of Motor Unit Number Index (MUNIX).

Motor Unit Number Index (MUNIX) is a technique that provides a susceptive biomarker for monitoring innervation conditions in patients with neurodegenerative diseases. A satisfactory repeatability is essential for the interpretation of MUNIX results. This study aims to examine the effect of channel number and location on the repeatability of MUNIX. In this study, 128 channels of high-density surface electromyography (EMG) signals were recorded from the biceps brachii muscles of eight healthy participants, at 10, 20, 30, 40, 50, 60, 70, 80, and 100% of maximal voluntary contraction. The repeatability was defined by the coefficient of variation (CV) of MUNIX estimated from three experiment trials. Single-channel MUNIX (sMUNIX) was calculated on a channel-specific basis and a multi-channel MUNIX (mMUNIX) approach as the weighted average of multiple sMUNIX results. Results have shown (1) significantly improved repeatability with the proposed mMUNIX approach; (2) a higher variability of sMUNIX when the recording channel is positioned away from the innervation zone. Our results have demonstrated that (1) increasing the number of EMG channels and (2) placing recording channels close to the innervation zone (IZ) are effective methods to improve the repeatability of MUNIX. This study investigated two potential causes of MUNIX variations and provided novel perspectives to improve the repeatability, using high-density surface EMG. The mMUNIX technique proposed can serve as a promising tool for reliable neurodegeneration evaluation.

A Determination Method for Gait Event Based on Acceleration Sensors.

A gait event is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. However, for the data acquisition of a three-dimensional motion capture (3D Mo-Cap) system, the high cost of setups, such as the high standard laboratory environment, limits widespread clinical application. Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. Inertial sensors are now sufficiently small in size and light in weight to be part of a body sensor network for the collection of human gait data. The acceleration signal has found important applications in human gait recognition. In this paper, using the experimental data from the heel and toe, first the wavelet method was used to remove noise from the acceleration signal, then, based on the threshold of comprehensive change rate of the acceleration signal, the signal was primarily segmented. Subsequently, the vertical acceleration signals, from heel and toe, were integrated twice, to compute their respective vertical displacement. Four gait events were determined in the segmented signal, based on the characteristics of the vertical displacement of heel and toe. The results indicated that the gait events were consistent with the synchronous record of the motion capture system. The method has achieved gait event subdivision, while it has also ensured the accuracy of the defined gait events. The work acts as a valuable reference, to further study gait recognition.