Time series classification based on fractal properties

The article considers classification task of fractal time series by the meta algorithms based on decision trees. Binomial multiplicative stochastic cascades are used as input time series. Comparative analysis of the classification approaches based on different features is carried out. The results indicate the advantage of the machine learning methods over the traditional estimating the degree of self-similarity.Comment: 4 pages, 2 figures, 3 equations, 1 tabl

On the Use of Fuzzy and Permutation Entropy in Hand Gesture Characterization from EMG Signals: Parameters Selection and Comparison

The surface electromyography signal (sEMG) is widely used for gesture characterization; its reliability is strongly connected to the features extracted from sEMG recordings. This study aimed to investigate the use of two complexity measures, i.e., fuzzy entropy (FEn) and permutation entropy (PEn) for hand gesture characterization. Fourteen upper limb movements, sorted into three sets, were collected on ten subjects and the performances of FEn and PEn for gesture descriptions were analyzed for different computational parameters. FEn and PEn were able to properly cluster the expected numbers of gestures, but computational parameters were crucial for ensuring clusters' separability and proper gesture characterization. FEn and PEn were also compared with other eighteen classical time and frequency domain features through the minimum redundancy maximum relevance algorithm and showed the best predictive importance scores in two gesture sets; they also had scores within the subset of the best five features in the remaining one. Further, the classification accuracies of four different feature sets presented remarkable increases when FEn and PEn are included as additional features. Outcomes support the use of FEn and PEn for hand gesture description when computational parameters are properly selected, and they could be useful in supporting the development of robotic arms and prostheses myoelectric control

Evaluation of performance fatigability through surface EMG in health and muscle disease: state of the art

In literature, it is commonly reported that the progress of performance fatigability may be indirectly assessed through the changes in the features of the surface electromyogram (sEMG) signal. In particular, during isometric constant force contractions, changes in the sEMG signal are caused by several physiological factors, such as a decay in muscle fibers conduction velocity (CV), an increase of the degree of synchronization between the firing times of simultaneously active motor units (MUs), by the central nervous system, and a reduction of the recruitment threshold and a modulation of MUs firing rate. Amplitude and spectral parameters may be used to characterize the global contributions to performance fatigability, such as MU control properties and fiber membrane properties, or central and peripheral factors, respectively. In addition, being CV a physiological parameter, its estimation is of marked interest to the study of fatigue both in physiological and in presence of neuromuscular diseases

A novel Approach for sEMG Gesture Recognition using Resource-constrained Hardware Platforms

Classifying human gestures using surface electromyografic sensors (sEMG) is a challenging task. Wearable sensors have proven to be extremely useful in this context, but their performance is limited by several factors (signal noise, computing resources, battery consumption, etc.). In particular, computing resources impose a limitation in many application scenarios, in which lightweight classification approaches are desirable. Recent research has shown that machine learning techniques are useful for human gesture classification once their salient features have been determined. This paper presents a novel approach for human gesture classification in which two different strategies are combined: a) a technique based on autoencoders is used to perform feature extraction; b) two alternative machine learning algorithms (namely J48 and K*) are then used for the classification stage. Empirical results are provided, showing that for limited computing power platforms our approach outperforms other alternative methodologies.Fil: Micheletto, Matías Javier. Universidad Nacional de la Patagonia Austral. Centro de Investigaciones y Transferencia Golfo San Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia Golfo San Jorge. Universidad Nacional de la Patagonia "San Juan Bosco". Centro de Investigaciones y Transferencia Golfo San Jorge; ArgentinaFil: Chesñevar, Carlos Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Santos, Rodrigo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin

Novel finger movement classification method based on multi-centered binary pattern using surface electromyogram signals

The number of individuals who have lost their fingers in our world is quite high and these individuals experience great difficulties in performing their daily work. Finger movements classification and prediction are one of the hot-topic research areas for biomedical engineering, machine learning and computer sciences. This study purposes finger movements classification and prediction. For this purpose, a novel finger movements classification method is presented by using surface electromyogram (sEMG) signals. To accurately classify these movements, a novel binary pattern like textural feature extractor is presented and this textural micro pattern is called as multi-centered binary pattern (MCBP). In the MCBP, five odd-indexed values of a block are utilized as center. The proposed MCBP based multileveled finger movements classification method evaluate by three cases. In the first case, the raw sEMG signals are utilized as input. In the second and third case, sEMG signals are divided into frames and these frames are utilized as input. A two-layered feature selector is used to choose the most valuable features. The purpose of using these two feature selectors together is to choose the optimum number of features. In the classification phase, two fine-tuned classifiers have been used and they are k-nearest neighbor (k-NN) and support vector machine (SVM). The proposed MCBP based method achieved 99.17%, 99.70% and 99.62% classification rates using SVM classifier according to Case 1, Case 2 and Case3 respectively. The results show that the study is a highly accurate method.</p

Modeling the Electromyogram (EMG) of Patients Undergoing Anesthesia During Surgery

All fields of science have advanced and still advance significantly. One of the facts that contributes positively is the synergy between areas. In this case, the present research shows the Electromyogram (EMG) modeling of patients undergoing to anesthesia during surgery. With the aim of predicting the patient EMG signal, a model that allows to know its performance from the Bispectral Index (BIS) and the Propofol infusion rate has been developed. The proposal has been achieved by using clustering combined with regression techniques and using a real dataset obtained from patients undergoing to anesthesia during surgeries. Finally, the created model has been tested with very satisfactory results

Computed tomography image analysis for the detection of obstructive lung diseases

Damage to the small airways resulting from direct lung injury or associated with many systemic disorders is not easy to identify. Non-invasive techniques such as chest radiography or conventional tests of lung function often cannot reveal the pathology. On Computed Tomography (CT) images, the signs suggesting the presence of obstructive airways disease are subtle, and inter- and intra-observer variability can be considerable. The goal of this research was to implement a system for the automated analysis of CT data of the lungs. Its function is to help clinicians establish a confident assessment of specific obstructive airways diseases and increase the precision of investigation of structure/function relationships. To help resolve the ambiguities of the CT scans, the main objectives of our system were to provide a functional description of the raster images, extract semi-quantitative measurements of the extent of obstructive airways disease and propose a clinical diagnosis aid using a priori knowledge of CT image features of the diseased lungs. The diagnostic process presented in this thesis involves the extraction and analysis of multiple findings. Several novel low-level computer vision feature extractors and image processing algorithms were developed for extracting the extent of the hypo-attenuated areas, textural characterisation of the lung parenchyma, and morphological description of the bronchi. The fusion of the results of these extractors was achieved with a probabilistic network combining a priori knowledge of lung pathology. Creating a CT lung phantom allowed for the initial validation of the proposed methods. Performance of the techniques was then assessed with clinical trials involving other diagnostic tests and expert chest radiologists. The results of the proposed system for diagnostic decision-support demonstrated the feasibility and importance of information fusion in medical image interpretation.Open acces

The selection and evaluation of a sensory technology for interaction in a warehouse environment

In recent years, Human-Computer Interaction (HCI) has become a significant part of modern life as it has improved human performance in the completion of daily tasks in using computerised systems. The increase in the variety of bio-sensing and wearable technologies on the market has propelled designers towards designing more efficient, effective and fully natural User-Interfaces (UI), such as the Brain-Computer Interface (BCI) and the Muscle-Computer Interface (MCI). BCI and MCI have been used for various purposes, such as controlling wheelchairs, piloting drones, providing alphanumeric inputs into a system and improving sports performance. Various challenges are experienced by workers in a warehouse environment. Because they often have to carry objects (referred to as hands-full) it is difficult to interact with traditional devices. Noise undeniably exists in some industrial environments and it is known as a major factor that causes communication problems. This has reduced the popularity of using verbal interfaces with computer applications, such as Warehouse Management Systems. Another factor that effects the performance of workers are action slips caused by a lack of concentration during, for example, routine picking activities. This can have a negative impact on job performance and allow a worker to incorrectly execute a task in a warehouse environment. This research project investigated the current challenges workers experience in a warehouse environment and the technologies utilised in this environment. The latest automation and identification systems and technologies are identified and discussed, specifically the technologies which have addressed known problems. Sensory technologies were identified that enable interaction between a human and a computerised warehouse environment. Biological and natural behaviours of humans which are applicable in the interaction with a computerised environment were described and discussed. The interactive behaviours included the visionary, auditory, speech production and physiological movement where other natural human behaviours such paying attention, action slips and the action of counting items were investigated. A number of modern sensory technologies, devices and techniques for HCI were identified with the aim of selecting and evaluating an appropriate sensory technology for MCI. iii MCI technologies enable a computer system to recognise hand and other gestures of a user, creating means of direct interaction between a user and a computer as they are able to detect specific features extracted from a specific biological or physiological activity. Thereafter, Machine Learning (ML) is applied in order to train a computer system to detect these features and convert them to a computer interface. An application of biomedical signals (bio-signals) in HCI using a MYO Armband for MCI is presented. An MCI prototype (MCIp) was developed and implemented to allow a user to provide input to an HCI, in a hands-free and hands-full situation. The MCIp was designed and developed to recognise the hand-finger gestures of a person when both hands are free or when holding an object, such a cardboard box. The MCIp applies an Artificial Neural Network (ANN) to classify features extracted from the surface Electromyography signals acquired by the MYO Armband around the forearm muscle. The MCIp provided the results of data classification for gesture recognition to an accuracy level of 34.87% with a hands-free situation. This was done by employing the ANN. The MCIp, furthermore, enabled users to provide numeric inputs to the MCIp system hands-full with an accuracy of 59.7% after a training session for each gesture of only 10 seconds. The results were obtained using eight participants. Similar experimentation with the MYO Armband has not been found to be reported in any literature at submission of this document. Based on this novel experimentation, the main contribution of this research study is a suggestion that the application of a MYO Armband, as a commercially available muscle-sensing device on the market, has the potential as an MCI to recognise the finger gestures hands-free and hands-full. An accurate MCI can increase the efficiency and effectiveness of an HCI tool when it is applied to different applications in a warehouse where noise and hands-full activities pose a challenge. Future work to improve its accuracy is proposed

Classification Techniques Using EHG Signals for Detecting Preterm Births

Premature birth is defined as an infant born before 37 weeks of gestation and can be sub-categorized into three phrases; late preterm delivery between 34 and 36 weeks of gestation; moderately preterm between 32 and 34 weeks, and extreme preterm less than 28 weeks of gestation. Globally, the rate of preterm births is increasing, thus resulting in significant health, development and economic problems. The current methods for the detection of preterm birth are inadequate due to the fact that the exact cause of premature uterine contractions leading to delivery is mostly unknown. Another problem is the interpretation of temporal and spectral characteristics of Electromyography (EMG), which is an electrodiagnostic medicine technique for recording and evaluating the electrical activity produced by uterine muscles during pregnancy and parturition – significant variability exists among obstetric care practitioners. Apart from a small number of potential causes for preterm birth, such as medication, uterine over-distension, preterm premature rupture of membranes (PPROM), intrauterine inflammation, precocious foetal endocrine activation, surgery, ethnicity and lifestyle, there is still a large amount of uncertainty about their specific risks. Hence, it is currently very difficult to make reliable predictions about preterm delivery risk. There has also been some evidence that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect the onset of preterm delivery. Early detection opens up new avenues for the development of an automated ambulatory system, based on uterine EMG, for patient monitoring during pregnancy. This can be made possible through the use of machine learning. The essence of machine learning is the utilisation of previously recorded data outcomes to train algorithms to ii stimulate software learning elements. Such learned models can, as a result, be used to detect and predict the early signs associated with the onset of preterm birth. Therefore in this thesis, Electrohysterography signals are used to classify uterine activity associated with preterm birth. This is achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies are utilized, as well as feature-ranking techniques to determine their discriminative capabilities in detecting term and preterm records. The results illustrate that the combination of the Levenberg-Marquardt trained Feed-Forward Neural Network, Radial Basis Function Neural Network and the Random Neural Network classifiers performed the best, with 91% for sensitivity, 84% for specificity, 94% for the area under the curve and 12% for the mean error rate. Applying advanced machine learning algorithms, in conjunction with innovative signal processing techniques and the analysis of Electrohysterography signals shows significant benefits for use in clinical interventions for preterm birth assessments