Twin SVM for gesture classification using the surface electromyogram test addition

Surface electromyogram (sEMG) is a measure of the muscle activity from the skin surface, and is an excellent indicator of the strength of muscle contraction. It is an obvious choice for control of prostheses and identification of body gestures. Using sEMG to identify posture and actions that are a result of overlapping multiple active muscles is rendered difficult by interference between different muscle activities. In the literature, attempts have been made to apply independent component analysis to separate sEMG into components corresponding to the activities of different muscles, but this has not been very successful, because some muscles are larger and more active than the others. We address the problem of how to learn to separate each gesture or activity from all others. Multicategory classification problems are usually solved by solving many one-versus-rest binary classification tasks. These subtasks naturally involve unbalanced datasets. Therefore, we require a learning methodology that can take into account unbalanced datasets, as well as large variations in the distributions of patterns corresponding to different classes. This paper reports the use of twin support vector machine for gesture classification based on sEMG, and shows that this technique is eminently suited to such applications

Hybrid independent component analysis and twin support vector machine learning scheme for subtle gesture recognition

Myoelectric signal classification is one of the most difficult pattern recognition problems because large variations in surface electromyogram features usually exist. In the literature, attempts have been made to apply various pattern recognition methods to classify surface electromyography into components corresponding to the activities of different muscles, but this has not been very successful, as some muscles are bigger and more active than others. This results in dataset discrepancy during classification. Multicategory classification problems are usually solved by solving many, one-versus-rest binary classification tasks. These subtasks unsurprisingly involve unbalanced datasets. Consequently, we need a learning methodology that can take into account unbalanced datasets in addition to large variations in the distributions of patterns corresponding to different classes. Here, we attempt to address the above issues using hybrid features extracted from independent component analysis and twin support vector machine techniques

Myo electric classification using twin SVM and blind source separation techniques

Myo electrical activities also known as Surface electromyogram (sEMG) is a measure of the muscle activity from the skin surface, and is an excellent indicator of the strength of muscle contraction. It is an obvious choice for control of prostheses, and identification of body gestures. Using sEMG to identify posture and actions is rendered difficult by interference between different muscle activities making it a multi class classification problem. Multi-category classification problems are usually solved by solving many, one-versus-rest binary classification tasks. These sub-tasks naturally involve unbalanced data sets. Therefore, we require a learning methodology that can take into account unbalanced data sets, as well as large variations in the distributions of patterns corresponding to different classes. This paper reports the use of Twin Support Vector Machine for gesture classification based on sEMG, and shows that this technique is eminently suited to such applications

ICHTHYODIVERSITY OF CHIKKLINGDALLI WATERBODY, WITH REFRENCE TO HYDROCHEMISTRY, KARANATAKA, INDIA

The present was carried out to document the diversity of fish fauna, economically important of fishes, status and Calculated Productivity Point CPP of fish species of Chikklingdalli water body Chincholli., India. A total number of fishes belonging to 5 families, 10 genus and 12 species were recorded. Highest 7 species recorded from family Cyprinidae, Siluridae 2 species, Channidae 01 species, Masticambidae 01 and Notopteridae 01 species. All the physic-chemical parameters are within the permissible limit. Present study suggests that the water temperature can positively correlated with, TDS, pH, total hardness

Suture Artefacts; Explored through polarising microscope

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The pheno-genotypic characteristics of infantile acute leukemia in a regional cancer center from South India

Introduction: Acute leukemia (AL) is uncommon in infants, with an annual incidence of 30 per million live births. They have peculiar biological characteristics. Although remarkable progress is seen in treatment of childhood AL, infantile AL remains a resistant subset with a dismally low 4-year survival of 35%. Objectives: To study the morphological, immunophenotypic, and cytogenetic features of infantile AL. A retrospective study of AL cases in children from birth up to 1 year of age, presenting to the departments of pediatric oncology and hematopathology between January 2010 and April 2015, was conducted. Results: Thirty-eight cases of infantile AL were included. The mean age at presentation was 10.2 months, and a female preponderance (M–F ratio: 0.65:1) was noted. Hyperleukocytosis (total white cell count >50 × 109/mm3) was seen in 13 (39.4%) cases. Immunophenotyping done in 31 cases showed pre-B acute lymphoblastic leukemia (B ALL) in 18 (58%), pre-T ALL in three (9.7%), and acute myeloid leukemia (AML) in 10 (32.3%). CD10 positivity was seen in 12 (57.1%) cases of ALL. Cytogenetic study done in 34 cases showed AML with recurrent genetic abnormalities in four. Mixed lineage leukemia (11q23) abnormality was seen in three cases of ALL. Two cases of AML were associated with trisomy 21. One case with features of AML M7 in a 4-day-old baby turned out to be transient abnormal myelopoiesis on follow-up. Conclusion: Literature on infantile AL from Indian studies is scarce compared to the available Western literature. Hence an epidemiological study of AL cases was done with review of literature, in an attempt to understand their pheno-genotypic features that influence their behavior. This may help in standardizing the treatment of these rare cases

Are Technology-Driven Mobile Phone Applications (Apps) the New Currency for Digital Stent Registries and Patient Communication: Prospective Outcomes Using Urostentz App

Background. Forgotten ureteral stents (FUS) and stent-related symptoms (SRS) lead to increased postprocedural emergency department visits and add to the psychological and financial burden of the patients. Purpose. To review the usage and benefits of ureteral stent tracking and symptom monitoring through a single smartphone-based application (App) platform with 2-way clinician-patient communication. This study also compared the features with other smartphone apps used for stent tracking. Materials and Methods. 100 patients were included in this single-center prospective study conducted between September 2019 and December 2019. Patients who had metallic or long-term indwelling stents, noncomprehensible patients, and those not willing to share their data were excluded from the study. Results. Of 100 patients, 92 downloaded the Urostentz application, and 72 (78.2%) patients answered the pictorial symptom questionnaire. Symptom score analysis suggested that 62 patients (86.1%) had stent-related symptoms of which 3 required readmission and underwent early stent removal. The mean stent duration was 17.2 + 3.5 days (range: 11–23 days), with 69% of patients having their stent removed on the scheduled date and 25% of patients requesting a change of their appointment via the App. Conclusion. In this study, there was no case of FUS encountered. The “Urostentz” App is a freely available patient safety stent tracking application that provides a secure and simplified interface, which can significantly reduce the incidence of FUS and provide digital remote assistance in the management of stent-related symptoms