Deep Scattering Transform with Attention Mechanisms Improves EMG-based Hand Gesture Recognition.

Electromyogram (EMG) signals provide valuable insights into the muscles' activities supporting the different hand movements, but their analysis can be challenging due to their stochastic nature, noise, and non-stationary variations in the signal. We are pioneering the use of a unique combination of wavelet scattering transform (WST) and attention mechanisms adopted from recent sequence modelling developments of deep neural networks for the classification of EMG patterns. Our approach utilizes WST, which decomposes the signal into different frequency components, and then applies a non-linear operation to the wavelet coefficients to create a more robust representation of the extracted features. This is coupled with different variations of attention mechanisms, typically employed to focus on the most important parts of the input data by considering weighted combinations of all input vectors. By applying this technique to EMG signals, we hypothesized that improvement in the classification accuracy could be achieved by focusing on the correlation between the different muscles' activation states associated with the different hand movements. To validate the proposed hypothesis, the study was conducted using three commonly used EMG datasets collected from various environments based on laboratory and wearable devices. This approach shows significant improvement in myoelectric pattern recognition (PR) compared to other methods, with average accuracies of up to 98%.

The Effectiveness of Narrowing the Window size for LD & HD EMG Channels based on Novel Deep Learning Wavelet Scattering Transform Feature Extraction Approach.

The use of the Electromyogram (EMG) signals as a source of control to command externally powered prostheses is often challenged by the signal complexity and non-stationary behavior. Mainly, two factors affect classification accuracy: selecting the optimum feature extraction methods and overlapping segmentation/window size. Nowadays, studies attempt to use deep learning (DL) methods to improve classification accuracy. However, DL models are frequently hampered by their requirements of a vast quantity of training data to attain decent performance and the high computing costs. Therefore, researchers tried to replace the deep learning models with other low computational cost methods like deep wavelet scattering transform (DWST) as a feature extraction technique. In terms of windows size, selecting a larger window size increases the classification accuracy, but at the same time, it increases the processing time, which makes the system unsuitable for real-time applications. Accordingly, researchers attempted to minimise the size of the overlapping windows as much as possible without impacting classification performance. This work suggests to utilise DWST transform to achieve two goals (a) extracting the features from EMG signal with low computational cost. Even though many studies have used DWST approaches to extract features from other biological signals, but not been examined before for EMG signals. (b) study the effect of extracting the features from high-density EMG datasets (HD EMG) and low-density EMG datasets (LD EMG), reducing the analysis window size by up to 32msec with minimal impact on classification performance. The outcomes of the proposed method are compared with other well-known feature extraction algorithms to validate these achievements. The proposed strategy exceeds other methods by more than 25% in accuracy.

Cardinality and Short-Term Memory Concepts based Novel Feature Extraction for Myoelectric Pattern Recognition.

The quality of the extracted traditional hand-crafted Electromyogram (EMG) features has been recently identified in the literature as a limiting factor prohibiting the translation from laboratory to clinical settings. To address this limitation, a shift of focus from traditional feature extraction methods to deep learning models was witnessed, as the latter can learn the best feature representation for the task at hand. However, while deep learning models achieve promising results based on raw EMG data, their clinical implementation is often challenged due to their significantly high computational costs (significantly large number of generated models' parameters and a huge amount of data needed for training). This paper is focused on combining the simplicity and low computational characteristics of traditional feature extraction with the memory concepts from Long Short-Term Memory (LSTM) models to efficiently extract the spatial-temporal dynamics of the EMG signals. The novelty of the proposed method can be summarized in a) the memory concept leveraged from deep learning structures, capturing short-term temporal dependencies of the EMG signals, b) the use of cardinality to generate logical combinations of spatially distinct EMG signals and as a feature extraction method and 3) low computational costs and the enhanced classification performance. The performance of the proposed method is validated using three EMG databases collected with 1) laboratory hardware (9 transradial amputees and 17 intact-limbed), and 2) wearables (22 intact-limed using two wearable consumer armbands). In comparison to several other well-known methods from the literature, the proposed method shows significantly enhanced myoelectric pattern recognition performance, with accuracies reaching up to 99%.

Impact of Coronavirus Disease 2019 (COVID-19) Outbreak Quarantine, Isolation, and Lockdown Policies on Mental Health and Suicide.

The novel coronavirus disease (COVID-19) pandemic has made a huge impact on people's physical and mental health, and it remains a cause of death for many all over the world. To prevent the spread of coronavirus infection, different types of public health measures (social isolation, quarantine, lockdowns, and curfews) have been imposed by governments. However, mental health experts warn that the prolonged lockdown, quarantine, or isolation will create a "second pandemic" with severe mental health issues and suicides. The quarantined or isolated people may suffer from various issues such as physical inactivity, mental health, economic and social problems. As with the SARS outbreak in 2003, many suicide cases have been reported in connection with this current COVID-19 pandemic lockdown due to various factors such as social stigma, alcohol withdrawal syndrome, fear of COVID infection, loneliness, and other mental health issues. This paper provides an overview of risk factors that can cause suicide and outlines possible solutions to prevent suicide in this current COVID-19 pandemic.

Infrared Thermal Imaging for Evaluation of Clubfoot After the Ponseti Casting Method-An Exploratory Study.

Background: Conservative treatment, Ponseti method, has been considered as a standard method to correct the clubfoot deformity among Orthopedic society. Although the result of conservative methods have been reported with higher success rates than surgical methods, many more problems have been reported due to improper casting, casting pressure or bracing discomfort. Nowadays, infrared thermography (IRT) is widely used as a diagnostic tool to assess musculoskeletal disorders or injuries by detecting temperature abnormalities. Similarly, the foot skin temperature evaluation can be added along with the current subjective evaluation to predict if there is any casting pressure, excessive manipulation, or overcorrections of the foot, and other bracing pressure-related complications. Purpose: The main purpose of this study was to explore the foot skin temperature changes before and after using of manipulation and weekly castings. Methods: This is an explorative study design. Infrared Thermography (IRT), E33 FLIR thermal imaging camera model, was used to collect the thermal images of the clubfoot before and after casting intervention. A total of 120 thermal images (Medial region of the foot-24, Lateral side of the foot-24, Dorsal side of the foot-24, Plantar side of the foot-24, and Heel area of the foot-24) were collected from the selected regions of the clubfoot. Results: The results of univariate statistical analysis showed that significant temperature changes in some regions of the foot after casting, especially, at the 2nd (M = 32.05°C, SD = 0.77, p = 0.05), 3rd (M = 31.61, SD = 1.11; 95% CI: 31.27-31.96; p = 0.00), and 6th week of evaluation on the lateral side of the foot (M = 31.15°C, SD = 1.59; 95% CI: 30.75-31.54, p = 0.000). There was no significant temperature changes throughout the weekly casting in the medial side of the foot. In the heel side of the foot, significant temperature changes were noticed after the third and fourth weeks of casting. Conclusion: This study found that a decreased foot skin temperature on the dorsal and lateral side of the foot at the 6th week of thermography evaluation. The finding of this study suggest that the infrared thermography (IRT) might be useful as an adjunct assessment tool to evaluate the thermophysiological changes, which can be used to predict the complications caused by improper casting, over manipulative or stretching and casting-pressure related complications.

Augmentation in Healthcare: Augmented Biosignal Using Deep Learning and Tensor Representation.

In healthcare applications, deep learning is a highly valuable tool. It extracts features from raw data to save time and effort for health practitioners. A deep learning model is capable of learning and extracting the features from raw data by itself without any external intervention. On the other hand, shallow learning feature extraction techniques depend on user experience in selecting a powerful feature extraction algorithm. In this article, we proposed a multistage model that is based on the spectrogram of biosignal. The proposed model provides an appropriate representation of the input raw biosignal that boosts the accuracy of training and testing dataset. In the next stage, smaller datasets are augmented as larger data sets to enhance the accuracy of the classification for biosignal datasets. After that, the augmented dataset is represented in the TensorFlow that provides more services and functionalities, which give more flexibility. The proposed model was compared with different approaches. The results show that the proposed approach is better in terms of testing and training accuracy.

Recurrent Fusion of Time-Domain Descriptors Improves EMG-based Hand Movement Recognition.

Controlling powered prostheses with myoelectric pattern recognition (PR) provides a natural human-robot interfacing scheme for amputees who lost their limbs. Research in this direction reveals that the challenges prohibiting reliable clinical translation of myoelectric interfaces are mainly driven by the quality of the extracted features. Hence, developing accurate and reliable feature extraction techniques is of vital importance for facilitating clinical implementation of Electromyogram (EMG) PR systems. To overcome this challenge, we proposed a combination of Range Spatial Filtering (RSF) and Recurrent Fusion of Time Domain Descriptors (RFTDD) in order to improve the classifier performance and make the prosthetic hand control more appropriate for clinical applications. RSF is used to increase the number of EMG signals available for feature extraction by focusing on the spatial information between all possible logical combinations of the physical EMG channels. RFTDD is then used to capture the temporal information by applying a recurrent data fusion process on the resulting orientation-based time-domain (TD) features, with a sigmoidal function to limit the features range and overcome the vanishing amplitudes problem. The main advantages of the proposed method include 1) its potential in capturing the temporal-spatial dependencies of the EMG signals, leading to reduced classification errors, and 2) the simplicity with which the features are extracted, as any kind of simple TD features can be adopted with this method. The performance of the proposed RFTDD is then benchmarked across many well-known TD features individually and as sets to prove the power of the RFTDD method on two EMG datasets with a total of 31 subjects. Testing results revealed an approximate reduction of 12% in classification errors across all subjects when using the proposed method against traditional feature extraction methods.Clinical Relevance-Establishing significance and importance of RFTDD, with simple time-domain features, for robust and low-cost clinical applications.

A Position Weight Matrix Feature Extraction Algorithm Improves Hand Gesture Recognition.

Recent advances in the biomedical field have generated a massive amount of data and records (signals) that are collected for diagnosis purposes. The correct interpretation and understanding of these signals present a big challenge for digital health vision. In this work, Quantization-based position Weight Matrix (QuPWM) feature extraction method for multiclass classification is proposed to improve the interpretation of biomedical signals. This method is validated on surface Electromyogram (sEMG) signals recognition for eight different hand gestures. The used CapgMyo dataset consists of high-density sEMG signals across 128 channels acquired from 9 intact subjects. Our pilot results show that an accuracy of up to 83% can be achieved for some subjects using a support vector machine classifier, and an average accuracy of 75% has been reached for all studied subjects using the CapgMyo dataset. The proposed method shows a good potential in extracting relevant features from different biomedical signals such as Electroencephalogram (EEG) and Magnetoencephalogram (MEG) signals.

Label Self-Advised Support Vector Machine (LSA-SVM)-Automated Classification of Foot Drop Rehabilitation Case Study.

Stroke represents a major health problem in our society. One of the effects of stroke is foot drop. Foot drop (FD) is a weakness that occurs in specific muscles in the ankle and foot such as the anterior tibialis, gastrocnemius, plantaris and soleus muscles. Foot flexion and extension are normally generated by lower motor neurons (LMN). The affected muscles impact the ankle and foot in both downward and upward motions. One possible solution for FD is to investigate the movement based on the bio signal (myoelectric signal) of the muscles. Bio signal control systems like electromyography (EMG) are used for rehabilitation devices that include foot drop. One of these systems is function electrical stimulation (FES). This paper proposes new methods and algorithms to develop the performance of myoelectric pattern recognition (M-PR), to improve automated rehabilitation devices, to test these methodologies in offline and real-time experimental datasets. Label classifying is a predictive data mining application with multiple applications in the world, including automatic labeling of resources such as videos, music, images and texts. We combine the label classification method with the self-advised support vector machine (SA-SVM) to create an adapted and altered label classification method, named the label self-advised support vector machine (LSA-SVM). For the experimental data, we collected data from foot drop patients using the sEMG device, in the Metro Rehabilitation Hospital in Sydney, Australia using Ethical Approval (UTS HREC NO. ETH15-0152). The experimental results for the EMG dataset and benchmark datasets exhibit its benefits. Furthermore, the experimental results on UCI datasets indicate that LSA-SVM achieves the best performance when working together with SA-SVM and SVM. This paper describes the state-of-the-art procedures for M-PR and studies all the conceivable structures.

Spatially Filtered Low-Density EMG and Time-Domain Descriptors Improves Hand Movement Recognition.

Surface Electromyogram (EMG) pattern recognition has long been utilized for controlling multifunctional myoelectric prostheses. In such an application, a number of EMG channels are usually utilized to acquire more information about the underlying activity of the remaining muscles in the amputee stump. However, despite the multichannel nature of this application, the extracted features are usually acquired from each channel individually, without consideration for the interaction between the different muscles recruited to achieve a specific movement. In this paper, we proposed an approach of spatial filtering, denoted as Range Spatial Filtering (RSF), to increase the number of EMG channels available for feature extraction, by considering the range of all possible logical combinations of each n channels. The proposed RSF method is then combined with conventional time-domain (TD) feature extraction, as an extension of the conventional single channel TD features that are heavily considered in this field. We then show how the addition of a new feature, specifically the minimum absolute value of the range of each two windowed EMG signals, can significantly reduce the different patterns misclassification rate achieved by conventional TD features (with and without our RSF method). The performance of the proposed method is verified on EMG data collected from nine transradial amputees (seven traumatic and two congenital), with six grip and finger movements, for three different levels of forces (low, medium, and high). The classification results showed significant reduction in classification error rates compared to other methods (nearly 10% for some individual TD features and 5% for combined TD features, with Bonferroni corrected p-values <; 0.01).

Optimal feature set for finger movement classification based on sEMG.

One of the most important electrophysiological signal is the Electromyography (EMG) signal, which is widely used in medical and engineering studies. This signal contains a wealth of information about muscle functions. Therefore, the EMG signal is becoming increasingly important and has started to be used in many applications like finger movement rehabilitation. However, an advanced EMG signal analysis method is required for efficient usage of such applications. This signal analysis can include signal detection, decomposition, processing, and classification. There are many approaches in studying the EMG signals, however, one of the important factor of analyzing is to get the most efficient and effective features that can be extracted from the raw signal. This paper presents the best feature extraction set compared to previous studies. Where eighteen well-known features algorithm has been tested using the sequential forward searching (SFS) method to get excellent classification accuracy in a minimum processing time. Among these novel features only four combinations have been selected with perfect results, which are; Hjorth Time Domain parameters (HTD), Mean Absolute Value (MAV), Root Mean Square (RMS) and Wavelet Packet Transform (WPT). The superiority of this feature set has been proven experimentally, and the results show that the classification accuracy could reach up to 99% to recognize the individual and combined for ten classes of finger movements using only two EMG channels.

Spatio-Temporal Inertial Measurements Feature Extraction Improves Hand Movement Pattern Recognition without Electromyography.

Recent studies indicate the limited clinical acceptance of myoelectric prostheses, as upper extremity amputees need improved functionality and more intuitive, effective, and coordinated control of their artificial limbs. Rather than exclusively classifying the electromyogram (EMG) signals, it has been shown that inertial measurements (IMs) can form an excellent complementary signal to the EMG signals to improve the prosthetic control robustness. We present an investigation into the possibility of replacing, rather than complementing, the EMG signals with IMs. We hypothesize that the enhancements achieved by the combined use of the EMG and IM signals may not be significantly different from that achieved by the use of Magnetometer (MAG) or Accelerometer (ACC) signals only, when the temporal and spatial information aspects are considered. A large dataset comprising recordings with 20 ablebodied and two amputee participants, executing 40 movements, was collected. A systematic performance comparison across a number of feature extraction methods was carried out to test our hypothesis. Results suggest that, individually, each of the ACC and MMG signals can form an excellent and potentially independent source of control signal for upper-limb prostheses, with an average classification accuracy of $\approx 93$% across all subjects. This study suggests the feasibility of moving from surface EMG to IM signals as a main source for upper-limb prosthetic control in real-life applications.

Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal.

Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB), k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from the other combinations tested. Graphical abstract Mean of classification accuracies for 14 finger movements obtained with various pairs of SRELM and classifier.

Ponseti method in the management of clubfoot under 2 years of age: A systematic review.

BACKGROUND: Congenital talipes equinovarus (CTEV), also known as clubfoot, is common congenital orthopedic foot deformity in children characterized by four components of foot deformities: hindfoot equinus, hindfoot varus, midfoot cavus, and forefoot adduction. Although a number of conservative and surgical methods have been proposed to correct the clubfoot deformity, the relapses of the clubfoot are not uncommon. Several previous literatures discussed about the technical details of Ponseti method, adherence of Ponseti protocol among walking age or older children. However there is a necessity to investigate the relapse pattern, compliance of bracing, number of casts used in treatment and the percentages of surgical referral under two years of age for clear understanding and better practice to achieve successful outcome without or reduce relapse. Therefore this study aims to review the current evidence of Ponseti method (manipulation, casting, percutaneous Achilles tenotomy, and bracing) in the management of clubfoot under two years of age. MATERIALS AND METHODS: Articles were searched from 2000 to 2015, in the following databases to identify the effectiveness of Ponseti method treatment for clubfoot: Medline, Cumulative Index to Nursing and Allied Health Literature (CINHAL), PubMed, and Scopus. The database searches were limited to articles published in English, and articles were focused on the effectiveness of Ponseti method on children with less than 2 years of age. RESULTS: Of the outcome of 1095 articles from four electronic databases, twelve articles were included in the review. Pirani scoring system, Dimeglio scoring system, measuring the range of motion and rate of relapses were used as outcome measures. CONCLUSIONS: In conclusion, all reviewed, 12 articles reported that Ponseti method is a very effective method to correct the clubfoot deformities. However, we noticed that relapses occur in nine studies, which is due to the non-adherence of bracing regime and other factors such as low income and social economic status.

A Framework of Temporal-Spatial Descriptors-Based Feature Extraction for Improved Myoelectric Pattern Recognition.

The extraction of the accurate and efficient descriptors of muscular activity plays an important role in tackling the challenging problem of myoelectric control of powered prostheses. In this paper, we present a new feature extraction framework that aims to give an enhanced representation of muscular activities through increasing the amount of information that can be extracted from individual and combined electromyogram (EMG) channels. We propose to use time-domain descriptors (TDDs) in estimating the EMG signal power spectrum characteristics; a step that preserves the computational power required for the construction of spectral features. Subsequently, TDD is used in a process that involves: 1) representing the temporal evolution of the EMG signals by progressively tracking the correlation between the TDD extracted from each analysis time window and a nonlinearly mapped version of it across the same EMG channel and 2) representing the spatial coherence between the different EMG channels, which is achieved by calculating the correlation between the TDD extracted from the differences of all possible combinations of pairs of channels and their nonlinearly mapped versions. The proposed temporal-spatial descriptors (TSDs) are validated on multiple sparse and high-density (HD) EMG data sets collected from a number of intact-limbed and amputees performing a large number of hand and finger movements. Classification results showed significant reductions in the achieved error rates in comparison to other methods, with the improvement of at least 8% on average across all subjects. Additionally, the proposed TSDs achieved significantly well in problems with HD-EMG with average classification errors of <5% across all subjects using windows lengths of 50 ms only.

Developing a Three-Dimensional (3D) Assessment Method for Clubfoot-A Study Protocol.

Background: Congenital talipes equinovarus (CTEV) or clubfoot is a common pediatric congenital foot deformity that occurs 1 in 1,000 live births. Clubfoot is characterized by four types of foot deformities: hindfoot equinus; midfoot cavus; forefoot adductus; and hindfoot varus. A structured assessment method for clubfoot is essential for quantifying the initial severity of clubfoot deformity and recording the progress of clubfoot intervention. Aim: This study aims to develop a three-dimensional (3D) assessment method to evaluate the initial severity of the clubfoot and monitor the structural changes of the clubfoot after each casting intervention. In addition, this study explores the relationship between the thermophysiological changes in the clubfoot at each stage of the casting intervention and in the normal foot. Methods: In this study, a total of 10 clubfoot children who are <2 years old will be recruited. Also, the data of the unaffected feet of a total of 10 children with unilateral clubfoot will be obtained as a reference for normal feet. A Kinect 3D scanner will be used to collect the 3D images of the clubfoot and normal foot, and an Infrared thermography camera (IRT camera) will be used to collect the thermal images of the clubfoot. Three-dimensional scanning and IR imaging will be performed on the foot once a week before casting. In total, 6-8 scanning sessions will be performed for each child participant. The following parameters will be calculated as outcome measures to predict, monitor, and quantify the severity of the clubfoot: Angles cross section parameters, such as length, width, and the radial distance; distance between selected anatomical landmarks, and skin temperature of the clubfoot and normal foot. The skin temperature will be collected on selected areas (forefoot, mid foot, and hindfoot) to find out the relationship between the thermophysiological changes in the clubfoot at each stage of the casting treatment and in the normal foot. Ethics: The study has been reviewed and approved on 17 August 2016 by the Sydney Children's Hospitals Network Human Research Ethics Committee (SCHN HREC), Sydney, Australia. The Human Research Ethics Committee (HREC) registration number for this study is: HREC/16/SCHN/163.

Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees.

The success of myoelectric pattern recognition (M-PR) mostly relies on the features extracted and classifier employed. This paper proposes and evaluates a fast classifier, extreme learning machine (ELM), to classify individual and combined finger movements on amputees and non-amputees. ELM is a single hidden layer feed-forward network (SLFN) that avoids iterative learning by determining input weights randomly and output weights analytically. Therefore, it can accelerate the training time of SLFNs. In addition to the classifier evaluation, this paper evaluates various feature combinations to improve the performance of M-PR and investigate some feature projections to improve the class separability of the features. Different from other studies on the implementation of ELM in the myoelectric controller, this paper presents a complete and thorough investigation of various types of ELMs including the node-based and kernel-based ELM. Furthermore, this paper provides comparisons of ELMs and other well-known classifiers such as linear discriminant analysis (LDA), k-nearest neighbour (kNN), support vector machine (SVM) and least-square SVM (LS-SVM). The experimental results show the most accurate ELM classifier is radial basis function ELM (RBF-ELM). The comparison of RBF-ELM and other well-known classifiers shows that RBF-ELM is as accurate as SVM and LS-SVM but faster than the SVM family; it is superior to LDA and kNN. The experimental results also indicate that the accuracy gap of the M-PR on the amputees and non-amputees is not too much with the accuracy of 98.55% on amputees and 99.5% on the non-amputees using six electromyography (EMG) channels.

Current conservative management and classification of club foot: A review.

Clubfoot, known as congenital talipes equinovarus, is one of the complex paediatric foot deformity with the incidence of 1 in every 1000 live births. It consists of four complex foot abnormalities such as forefoot adductus, midfoot cavus, and hindfoot varus and ankle equinus. There are a number of surgical techniques (soft tissue releases, arthrodesis) used to correct clubfoot. However currently the conservative management (manipulation, serial casting, and braces) of clubfoot is considered as the best choice and it is widely accepted among orthopaedists. Clubfoot treated with surgical techniques might suffer various complications such as soft tissues contractures, neurovascular complications, infections, and shortening of the limbs. Although conservative method is generally considered as an effective method, it is still challenging to cure clubfoot in advance stages. Also, the classification of the initial severity of clubfoot is essential to evaluate the outcome of the treatment. In this review, the aim is to review the different types of conservative method and the assessment of clubfoot severity.

Semi-advised learning model for skin cancer diagnosis based on histopathalogical images.

Computer aided classification of skin cancer images is an active area of research and different classification methods has been proposed so far. However, the supervised classification models based on insufficient labeled training data can badly influence the diagnosis process. To deal with the problem of limited labeled data availability this paper presents a semi advised learning model for automated recognition of skin cancer using histopathalogical images. Deep belief architecture is constructed using unlabeled data by making efficient use of limited labeled data for fine tuning done the classification model. In parallel an advised SVM algorithm is used to enhance classification results by counteracting the effect of misclassified data using advised weights. To increase generalization capability of the model, advised SVM and Deep belief network are trained in parallel. Then the results are aggregated using least square estimation weighting. The proposed model is tested on a collection of 300 histopathalogical images taken from biopsy samples. The classification performance is compared with some popular methods and the proposed model outperformed most of the popular techniques including KNN, ANN, SVM and semi supervised algorithms like Expectation maximization algorithm and transductive SVM based classification model.

Adaptive myoelectric pattern recognition for arm movement in different positions using advanced online sequential extreme learning machine.

The performance of the myoelectric pattern recognition system sharply decreases when working in various limb positions. The issue can be solved by cumbersome training procedure that can anticipate all possible future situations. However, this procedure will sacrifice the comfort of the user. In addition, many unpredictable scenarios may be met in the future. This paper proposed a new adaptive myoelectric pattern recognition using advance online sequential extreme learning (AOS-ELM) for classification of the hand movements to five different positions. AOS-ELM is an improvement of OS-ELM that can verify the adaptation validity using entropy. The proposed adaptive MPR was able to classify eight different classes from eleven subjects by accuracy of 95.42 % using data from one position. After learning the data from whole positions, the performance of the proposed system is 86.13 %. This performance was better than the MPR that employed original OS-ELM, but it was worse than the MPR that utilized the batch classifiers. Nevertheless, the adaptation mechanism of AOS-ELM is preferred in the real-time application.