Human activity recognition (HAR) is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive technologies and manual processes analysis. Recently, deep neural networks have been deployed for HAR in the context of activities of daily living using multichannel time-series. These time-series are acquired from body-worn devices, which are composed of different types of sensors. The deep architectures process these measurements for finding basic and complex features in human corporal movements, and for classifying them into a set of human actions. As the devices are worn at different parts of the human body, we propose a novel deep neural network for HAR. This network handles sequence measurements from different body-worn devices separately. An evaluation of the architecture is performed on three datasets, the Oportunity, Pamap2, and an industrial dataset, outperforming the state-of-the-art. In addition, different network configurations will also be evaluated. We find that applying convolutions per sensor channel and per body-worn device improves the capabilities of convolutional neural network (CNNs).
Optimizations in logistics require recognition and analysis of human activities. The potential of sensor-based human activity recognition (HAR) in logistics is not yet well explored. Despite a significant increase in HAR datasets in the past twenty years, no available dataset depicts activities in logistics. This contribution presents the first freely accessible logistics-dataset. In the ’Innovationlab Hybrid Services in Logistics’ at TU Dortmund University, two picking and one packing scenarios were recreated. Fourteen subjects were recorded individually when performing warehousing activities using Optical marker-based Motion Capture (OMoCap), inertial measurement units (IMUs), and an RGB camera. A total of 758 min of recordings were labeled by 12 annotators in 474 person-h. All the given data have been labeled and categorized into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes. The dataset is deployed for solving HAR using deep networks.
Aims The 12-lead electrocardiogram (ECG) is routinely performed in children with hypertrophic cardiomyopathy (HCM). An ECG risk score has been suggested as a useful tool for risk stratification, but this has not been independently validated. This aim of this study was to describe the ECG phenotype of childhood HCM in a large, international, multi-centre cohort and investigate its role in risk prediction for arrhythmic events. Methods and results Data from 356 childhood HCM patients with a mean age of 10.1 years (±4.5) were collected from a retrospective, multi-centre international cohort. Three hundred and forty-seven (97.5%) patients had ECG abnormalities at baseline, most commonly repolarization abnormalities (n = 277, 77.8%); left ventricular hypertrophy (n = 240, 67.7%); abnormal QRS axis (n = 126, 35.4%); or QT prolongation (n = 131, 36.8%). Over a median follow-up of 3.9 years (interquartile range 2.0–7.7), 25 (7%) had an arrhythmic event, with an overall annual event rate of 1.38 (95% CI 0.93–2.04). No ECG variables were associated with 5-year arrhythmic event on univariable or multivariable analysis. The ECG risk score threshold of >5 had modest discriminatory ability [C-index 0.60 (95% CI 0.484–0.715)], with corresponding negative and positive predictive values of 96.7% and 6.7% Conclusion In a large, international, multi-centre cohort of childhood HCM, ECG abnormalities were common and varied. No ECG characteristic, either in isolation or combined in the previously described ECG risk score, was associated with 5-year sudden cardiac death risk. This suggests that the role of baseline ECG phenotype in improving risk stratification in childhood HCM is limited.
Deleterious long-term effects of pulmonary regurgitation after tetralogy of Fallot (TOF) repair have become evident during the last two decades. Subsequently, different groups have developed strategies aimed at preserving the pulmonary valve function. However, the results of these approaches are not well known. From July 2009 through March 2012, 38 patients underwent primary repair of TOF at the authors' institution. Of these, 12 children (7 boys) underwent attempted pulmonary valve-sparing surgery with intraoperative dilation of the pulmonary valve. The technical details as well as the echocardiographic preoperative and follow-up data for this repair were recorded, with a special focus on the feasibility of the technique and the effects on pulmonary valve function. No patient in the series died. At repair, the median age was 6 months (range 3.4-126 months), and the median weight was 7.6 kg (range 4.7-47 kg). Intraoperative dilation of the pulmonary valve was technically feasible for all the patients. Two patients had unsuccessful dilation and underwent a transannular patch procedure. During a median follow-up period of 22 months (range 6-30 months), the pulmonary valve diameter and z-score improved significantly. Moreover, the annular size normalized, whereas the mean right ventricular outflow tract (RVOT) gradient remained at the mild level (median, 24 mmHg; range 12-36 mmHg). At the most recent follow-up evaluation, three patients showed moderate pulmonary regurgitation. Intraoperative dilation of the pulmonary valve in patients undergoing TOF repair is feasible and provides good relief of obstruction. Moreover, the pulmonary valve annulus grows through the follow-up period. Longer follow-up studies are needed to evaluate the exact role of this strategy in this population.
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