Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults

Falls are a major cause of morbidity and mortality in neurological disorders. Technical means of detecting falls are of high interest as they enable rapid notification of caregivers and emergency services. Such approaches must reliably differentiate between normal daily activities and fall events. A promising technique might be based on the classification of movements based on accelerometer signals by machine-learning algorithms, but the generalizability of classifiers trained on laboratory data to real-world datasets is a common issue. Here, three machine-learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were trained to detect fall events. We used a dataset containing intentional falls (SisFall) to train the classifier and validated the approach on a different dataset which included real-world accidental fall events of elderly people (FARSEEING). The results suggested that the linear SVM was the most suitable classifier in this cross-dataset validation approach and reliably distinguished a fall event from normal everyday activity at an accuracy of 93% and similarly high sensitivity and specificity. Thus, classifiers based on linear SVM might be useful for automatic fall detection in real-world applications

Sensitivity of multi-PMT Optical Modules in Antarctic Ice to Supernova Neutrinos of MeV energy

New optical sensors with a segmented photosensitive area are being developed for the next generation of neutrino telescopes at the South Pole. In addition to increasing sensitivity to high-energy astrophysical neutrinos, we show that this will also lead to a significant improvement in sensitivity to MeV neutrinos, such as those produced in core-collapse supernovae (CCSN). These low-energy neutrinos can provide a detailed picture of the events after stellar core collapse, testing our understanding of these violent explosions. We present studies on the event-based detection of MeV neutrinos with a segmented sensor and, for the first time, the potential of a corresponding detector in the deep ice at the South Pole for the detection of extra-galactic CCSN. We find that exploiting temporal coincidences between signals in different photocathode segments, a $27\ \mathrm{M}_{\odot}$ progenitor mass CCSN can be detected up to a distance of 341 kpc with a false detection rate of $0.01$ year$^{-1}$ with a detector consisting of 10000 sensors. Increasing the number of sensors to 20000 and reducing the optical background by a factor of 70 expands the range such that a CCSN detection rate of $0.1$ per year is achieved, while keeping the false detection rate at $0.01$ year$^{-1}$.Comment: Published versio

Evaluation of Three Machine Learning Algorithms for the Automatic Classification of EMG Patterns in Gait Disorders

Gait disorders are common in neurodegenerative diseases and distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge even for the experienced clinician. Ultimately, muscle activity underlies the generation of kinematic patterns. Therefore, one possible way to address this problem may be to differentiate gait disorders by analyzing intrinsic features of muscle activations patterns. Here, we examined whether it is possible to differentiate electromyography (EMG) gait patterns of healthy subjects and patients with different gait disorders using machine learning techniques. Nineteen healthy volunteers (9 male, 10 female, age 28.2 ± 6.2 years) and 18 patients with gait disorders (10 male, 8 female, age 66.2 ± 14.7 years) resulting from different neurological diseases walked down a hallway 10 times at a convenient pace while their muscle activity was recorded via surface EMG electrodes attached to 5 muscles of each leg (10 channels in total). Gait disorders were classified as predominantly hypokinetic (n = 12) or ataxic (n = 6) gait by two experienced raters based on video recordings. Three different classification methods (Convolutional Neural Network—CNN, Support Vector Machine—SVM, K-Nearest Neighbors—KNN) were used to automatically classify EMG patterns according to the underlying gait disorder and differentiate patients and healthy participants. Using a leave-one-out approach for training and evaluating the classifiers, the automatic classification of normal and abnormal EMG patterns during gait (2 classes: “healthy” and “patient”) was possible with a high degree of accuracy using CNN (accuracy 91.9%), but not SVM (accuracy 67.6%) or KNN (accuracy 48.7%). For classification of hypokinetic vs. ataxic vs. normal gait (3 classes) best results were again obtained for CNN (accuracy 83.8%) while SVM and KNN performed worse (accuracy SVM 51.4%, KNN 32.4%). These results suggest that machine learning methods are useful for distinguishing individuals with gait disorders from healthy controls and may help classification with respect to the underlying disorder even when classifiers are trained on comparably small cohorts. In our study, CNN achieved higher accuracy than SVM and KNN and may constitute a promising method for further investigation

Measuring satisfaction with health care in young persons with inflammatory bowel disease -an instrument development and validation study

Background: Patient satisfaction is a relevant prognostic factor in young persons with chronic disease and may be both age and disease specific. To assess health care quality from the patient's view in young persons with inflammatory bowel disease, an easy to use, valid, reliable and informative specific instrument was needed. Methods: All parts of the study were directed at persons with inflammatory bowel disease aged 15 to 24 (" youth"). A qualitative internet patient survey was used to generate items, complemented by a physician survey and literature search. A 2nd internet survey served to reduce items based on perceived importance and representativeness. Following pilot testing to assess ease of use and face validity, 150 respondents to a postal survey in patients from a paediatric clinical registry were included for validation analyses. Construct validity was assessed by relating summary scores to results from global questions on satisfaction with care using ANOVA. To assess test-retest reliability using intraclass correlation coefficients (ICC),a subset of patients were assessed twice within 3 months. Results: 302 persons with IBD and 55 physicians participated in the item generating internet survey, resulting in 3, 954 statements. After discarding redundancies 256 statements were presented in the 2nd internet survey. Of these, 32 items were retained. The resulting instrument assesses both the perceived relevance (importance) of an item as well as the performance of the care giver for each item for calculation of a summary satisfaction score (range 0 to 1). Sensibility testing showed good acceptance for most items. Construct validity was good, with mean scores of 0.63 (0.50 to 0.76),0.71 (0.69 to 0.74) and 0.81 (0.79 to 0.83) for no, some and good global satisfaction (ANOVA, p < 0.001). Test-retest reliability was satisfactory (ICC 0.6 to 0.7). Conclusions: We developed an easy to use, patient oriented, valid instrument to assess satisfaction with care in young persons with IBD for use in survey research

Comparative Approach to Define Increased Regulatory T Cells in Different Cancer Subtypes by Combined Assessment of CD127 and FOXP3

In recent years an increase of functional CD4+CD25+ regulatory T cells (Treg cells) has been established for patients with solid tumors, acute leukemias, and lymphomas. We have reported an expanded pool of CD4+CD25high Treg cells in patients with chronic lymphatic leukemia (CLL), multiple myeloma (MM) as well as its premalignant precursor monoclonal gammopathy of undetermined significance (MGUS). In healthy individuals, low-level expression of CD127 on T cells in addition to the expression of FOXP3 has been associated with Treg cells. Here, we demonstrate that the expanded FOXP3+ T-cell population in patients with colorectal cancer, CLL, MGUS, MM, follicular lymphoma, and Hodgkin's disease are exclusively CD127low Treg cells and were strongly suppressive. A significant portion of CD127lowFOXP3+ Treg cells expressed only low levels of CD25 suggesting that the previously reported expansion of CD25+ Treg cells underestimates the true expansion. The assessment of CCR7 and CD45RA expression on the expanded CD4+CD127lowFOXP3+ Treg cells revealed an increase of both naïve as well as central and effector memory Treg cells in peripheral blood. Our data strongly support superiority of combined CD127 and FOXP3 analysis in comparison to CD25 and FOXP3 assessment for further quantification of Treg cells in malignant diseases

Dissociation and hierarchical assembly of chiral esters on metallic surfaces

The interaction of a de novo synthesised ester with single crystal metal surfaces has been investigated as a model system for the heterogeneous hydrogenolysis of esters. Scanning tunnelling microscopy measurements show dissociative adsorption at room temperature on Cu(110) but no significant reaction on Au(111). The dissociative pathway has been identified by comparing with possible fragment species, demonstrating that the ester cleavage occurs along the RCH(CH3)–OC(O)R bond

School-related experience and performance with inflammatory bowel disease: results from a cross-sectional survey in 675 children and their parents

Objective We describe school performance and experience in children with inflammatory bowel disease (IBD) across Germany and Austria. Predictors of compromised performance and satisfaction were evaluated to identify subgroups of increased risk. Design This cross-sectional analysis was based on a postal survey in children aged 10-15 with Crohn's disease, ulcerative colitis or unclassified IBD and their families. Multivariate regression analysis was used to assess influential factors on parental satisfaction with school, attending advanced secondary education (ASE), having good marks and having to repeat a class. Satisfaction was assessed based on the Child Healthcare-Satisfaction, Utilisation and Needs instrument (possible range 1.00-5.00). Results Of 1367 families contacted, 675 participated in the study (49.4%). Sixty-eight participants (10.2%) had repeated a year, 312 (46.2%) attended ASE. The median school satisfaction score was 2.67 (IQR 2.00-3.33). High socioeconomic status (SES) and region within Germany were predictive for ASE (OR high SES 8.2, 95% CI 4.7 to 14.2). SES, female sex and region of residence predicted good marks. Grade retention was associated with an active disease course (OR 2.7, 95% CI 1.4 to 5.3) and prolonged periods off school due to IBD (OR 3.9, 95% CI 1.8 to 8.6). Conclusions A severe disease course impacted on the risk of grade retention, but not on type of school attended and school marks. Low satisfaction of parents of chronically ill children with the school situation underlines the need for a more interdisciplinary approach in health services and health services research in young people