mnmDTW: An extension to Dynamic Time Warping for Camera-based Movement Error Localization

In this proof of concept, we use Computer Vision (CV) methods to extract pose information out of exercise videos. We then employ a modified version of Dynamic Time Warping (DTW) to calculate the deviation from a gold standard execution of the exercise. Specifically, we calculate the distance between each body part individually to get a more precise measure for exercise accuracy. We can show that exercise mistakes are clearly visible, identifiable and localizable through this metric.Comment: Poster Prague 2023 Conference, 4 page

Automatic Generation of Labeled Data for Video-Based Human Pose Analysis via NLP applied to YouTube Subtitles

With recent advancements in computer vision as well as machine learning (ML), video-based at-home exercise evaluation systems have become a popular topic of current research. However, performance depends heavily on the amount of available training data. Since labeled datasets specific to exercising are rare, we propose a method that makes use of the abundance of fitness videos available online. Specifically, we utilize the advantage that videos often not only show the exercises, but also provide language as an additional source of information. With push-ups as an example, we show that through the analysis of subtitle data using natural language processing (NLP), it is possible to create a labeled (irrelevant, relevant correct, relevant incorrect) dataset containing relevant information for pose analysis. In particular, we show that irrelevant clips ($n=332$) have significantly different joint visibility values compared to relevant clips ($n=298$). Inspecting cluster centroids also show different poses for the different classes.Comment: 4 pages, 5 figure

Mischkulturenanbau Praxisversuche 2013

Das Ziel unserer Praxisversuche besteht darin, die Kenntnisse über den Mischfruchtanbau unter Biobedingungen weiter zu vertiefen und für die Landwirte attraktiver zu machen. Die Hauptfrage ist die richtigen Mischungspartner im richtigen Mischverhältnis zusammenzubringen und standardisierte Mischungen zu entwickeln. Die Proteinproduktion steht klar im Vordergrund und das Getreide oder Leindotter soll die Funktion als Stützfrucht bzw. Sekundärpflanze übernehmen. Bisher hat sich die Mischung von Eiweisserbsen und Gerste wegen dem gleichen Abreife-zeitpunkt am besten bewährt. Bezüglich Proteinertrag schliesst die Mischung mit 40 % der normalen Saatmenge Gerste weniger gut ab als die Mischungen mit nur 20 % Gerste. Im Gesamtertrag hingegen liegen die Mischungen mit 40 % Gerste vorne. Durch weniger dominante Gerstensorten könnte das Verhältnis zugunsten der Eiweisserbsen verbessert werden. Deshalb wurden im Jahr 2013 die Versuche mit zwei verschiedenen Wintergerstensorten, einer kurz- und einer langhalmigen, fortgesetzt. Herbstsaaten haben gegenüber den Frühlingssaaten klare Vorteile wie frühere Blüte, bessere Konkurrenz gegen Unkräuter und weniger Schädlinge wie Blattläuse. Dies führt in der Regel zu höheren Erträgen. Als gewichtiger Nachteil der Winterformen hat sich in Jahren mit Temperatu-ren um -15 ° C in Gegenden mit länger anhaltenden Kahlfrösten die mangelnde Winterhärte erwiesen. Davon betroffen sind Wintereiweisserbsen, Winterackerbohnen und Winterhafer. Aus diesem Grund wurde der Schwerpunkt auf neue, winterhärtere Sorten gesetzt. Besonders win-terhart sollen die buntblühenden Eiweisserbsen EFB33 und Arkta sein. Diese haben wir an zwei Standorten zusammen mit Triticale angebaut. Im Weiteren wurde an drei Standorten die Misch-kultur Ackerbohne / Hafer und an einem Standort Lupine / Triticale angebaut. An den Standor-ten mit Frühjahresaussaaten wurden zu Versuchszwecken Eiweisserbsen, Lupinen und Ackerbohnen mit verschiedenen Mischungspartnern wie Lein, Weizen, Hafer, Gerste und Leindotter in Kleinparzellen angebaut. Bei den Ackerbohnen gab es zudem verschiedene Reinsaaten mit neuen Ackerbohnensorten

An extensive quantitative analysis of the effects of errors in beat-to-beat intervals on all commonly used HRV parameters

Heart rate variability (HRV) analysis is often used to estimate human health and fitness status. More specifically, a range of parameters that express the variability in beat-to-beat intervals are calculated from electrocardiogram beat detections. Since beat detection may yield erroneous interval data, these errors travel through the processing chain and may result in misleading parameter values that can lead to incorrect conclusions. In this study, we utilized Monte Carlo simulation on real data, Kolmogorov–Smirnov tests and Bland–Altman analysis to carry out extensive analysis of the noise sensitivity of different HRV parameters. The used noise models consider Gaussian and student-t distributed noise. As a result we observed that commonly used HRV parameters (e.g. pNN50 and LF/HF ratio) are especially sensitive to noise and that all parameters show biases to some extent. We conclude that researchers should be careful when reporting different HRV parameters, consider the distributions in addition to mean values, and consider reference data if applicable. The analysis of HRV parameter sensitivity to noise and resulting biases presented in this work generalizes over a wide population and can serve as a reference and thus provide a basis for the decision about which HRV parameters to choose under similar conditions.Peer reviewe

Estimating Thoracic Movement with High-Sampling Rate THz Technology

We use a high-sampling rate terahertz (THz) homodyne spectroscopy system to estimate thoracic movement from healthy subjects performing breathing at different frequencies. The THz system provides both the amplitude and phase of the THz wave. From the raw phase information, a motion signal is estimated. An electrocardiogram (ECG) signal is recorded with a polar chest strap to obtain ECG-derived respiration information. While the ECG showed sub-optimal performance for the purpose and only provided usable information for some subjects, the signal derived from the THz system showed good agreement with the measurement protocol. Over all the subjects, a root mean square estimation error of 1.40 BPM is obtained

Design of Hardware Accelerators for Optimized and Quantized Neural Networks to Detect Atrial Fibrillation in Patch ECG Device with RISC-V

Atrial Fibrillation (AF) is one of the most common heart arrhythmias. It is known to cause up to 15% of all strokes. In current times, modern detection systems for arrhythmias, such as single-use patch electrocardiogram (ECG) devices, have to be energy efficient, small, and affordable. In this work, specialized hardware accelerators were developed. First, an artificial neural network (NN) for the detection of AF was optimized. Special attention was paid to the minimum requirements for the inference on a RISC-V-based microcontroller. Hence, a 32-bit floating-point-based NN was analyzed. To reduce the silicon area needed, the NN was quantized to an 8-bit fixed-point datatype (Q7). Based on this datatype, specialized accelerators were developed. Those accelerators included single-instruction multiple-data (SIMD) hardware as well as accelerators for activation functions such as sigmoid and hyperbolic tangents. To accelerate activation functions that require the e-function as part of their computation (e.g., softmax), an e-function accelerator was implemented in the hardware. To compensate for the losses of quantization, the network was expanded and optimized for run-time and memory requirements. The resulting NN has a 7.5% lower run-time in clock cycles (cc) without the accelerators and 2.2 percentage points (pp) lower accuracy compared to a floating-point-based net, while requiring 65% less memory. With the specialized accelerators, the inference run-time was lowered by 87.2% while the F1-Score decreased by 6.1 pp. Implementing the Q7 accelerators instead of the floating-point unit (FPU), the silicon area needed for the microcontroller in 180 nm-technology is below 1 mm²

Exploring novel algorithms for atrial fibrillation detection by driving graduate level education in medical machine learning

During the lockdown of universities and the COVID-Pandemic most students were restricted to their homes. Novel and instigating teaching methods were required to improve the learning experience and so recent implementations of the annual PhysioNet/Computing in Cardiology (CinC) Challenges posed as a reference. For over 20 years, the challenges have proven repeatedly to be of immense educational value, besides leading to technological advances for specific problems. In this paper, we report results from the class ‘Artificial Intelligence in Medicine Challenge’, which was implemented as an online project seminar at Technical University Darmstadt, Germany, and which was heavily inspired by the PhysioNet/CinC Challenge 2017 ‘AF Classification from a Short Single Lead ECG Recording’. Atrial fibrillation is a common cardiac disease and often remains undetected. Therefore, we selected the two most promising models of the course and give an insight into the Transformer-based DualNet architecture as well as into the CNN-LSTM-based model and finally a detailed analysis for both. In particular, we show the model performance results of our internal scoring process for all submitted models and the near state-of-the-art model performance for the two named models on the official 2017 challenge test set. Several teams were able to achieve F1 scores above/close to 90% on a hidden test-set of Holter recordings. We highlight themes commonly observed among participants, and report the results from the self-assessed student evaluation. Finally, the self-assessment of the students reported a notable increase in machine learning knowledge

Climate Change Impact on Neotropical Social Wasps

Establishing a direct link between climate change and fluctuations in animal populations through long-term monitoring is difficult given the paucity of baseline data. We hypothesized that social wasps are sensitive to climatic variations, and thus studied the impact of ENSO events on social wasp populations in French Guiana. We noted that during the 2000 La Niña year there was a 77.1% decrease in their nest abundance along ca. 5 km of forest edges, and that 70.5% of the species were no longer present. Two simultaneous 13-year surveys (1997–2009) confirmed the decrease in social wasps during La Niña years (2000 and 2006), while an increase occurred during the 2009 El Niño year. A 30-year weather survey showed that these phenomena corresponded to particularly high levels of rainfall, and that temperature, humidity and global solar radiation were correlated with rainfall. Using the Self-Organizing Map algorithm, we show that heavy rainfall during an entire rainy season has a negative impact on social wasps. Strong contrasts in rainfall between the dry season and the short rainy season exacerbate this effect. Social wasp populations never recovered to their pre-2000 levels. This is probably because these conditions occurred over four years; heavy rainfall during the major rainy seasons during four other years also had a detrimental effect. On the contrary, low levels of rainfall during the major rainy season in 2009 spurred an increase in social wasp populations. We conclude that recent climatic changes have likely resulted in fewer social wasp colonies because they have lowered the wasps' resistance to parasitoids and pathogens. These results imply that Neotropical social wasps can be regarded as bio-indicators because they highlight the impact of climatic changes not yet perceptible in plants and other animals

Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial

Background: Glucagon-like peptide 1 receptor agonists differ in chemical structure, duration of action, and in their effects on clinical outcomes. The cardiovascular effects of once-weekly albiglutide in type 2 diabetes are unknown. We aimed to determine the safety and efficacy of albiglutide in preventing cardiovascular death, myocardial infarction, or stroke. Methods: We did a double-blind, randomised, placebo-controlled trial in 610 sites across 28 countries. We randomly assigned patients aged 40 years and older with type 2 diabetes and cardiovascular disease (at a 1:1 ratio) to groups that either received a subcutaneous injection of albiglutide (30–50 mg, based on glycaemic response and tolerability) or of a matched volume of placebo once a week, in addition to their standard care. Investigators used an interactive voice or web response system to obtain treatment assignment, and patients and all study investigators were masked to their treatment allocation. We hypothesised that albiglutide would be non-inferior to placebo for the primary outcome of the first occurrence of cardiovascular death, myocardial infarction, or stroke, which was assessed in the intention-to-treat population. If non-inferiority was confirmed by an upper limit of the 95% CI for a hazard ratio of less than 1·30, closed testing for superiority was prespecified. This study is registered with ClinicalTrials.gov, number NCT02465515. Findings: Patients were screened between July 1, 2015, and Nov 24, 2016. 10 793 patients were screened and 9463 participants were enrolled and randomly assigned to groups: 4731 patients were assigned to receive albiglutide and 4732 patients to receive placebo. On Nov 8, 2017, it was determined that 611 primary endpoints and a median follow-up of at least 1·5 years had accrued, and participants returned for a final visit and discontinuation from study treatment; the last patient visit was on March 12, 2018. These 9463 patients, the intention-to-treat population, were evaluated for a median duration of 1·6 years and were assessed for the primary outcome. The primary composite outcome occurred in 338 (7%) of 4731 patients at an incidence rate of 4·6 events per 100 person-years in the albiglutide group and in 428 (9%) of 4732 patients at an incidence rate of 5·9 events per 100 person-years in the placebo group (hazard ratio 0·78, 95% CI 0·68–0·90), which indicated that albiglutide was superior to placebo (p<0·0001 for non-inferiority; p=0·0006 for superiority). The incidence of acute pancreatitis (ten patients in the albiglutide group and seven patients in the placebo group), pancreatic cancer (six patients in the albiglutide group and five patients in the placebo group), medullary thyroid carcinoma (zero patients in both groups), and other serious adverse events did not differ between the two groups. There were three (<1%) deaths in the placebo group that were assessed by investigators, who were masked to study drug assignment, to be treatment-related and two (<1%) deaths in the albiglutide group. Interpretation: In patients with type 2 diabetes and cardiovascular disease, albiglutide was superior to placebo with respect to major adverse cardiovascular events. Evidence-based glucagon-like peptide 1 receptor agonists should therefore be considered as part of a comprehensive strategy to reduce the risk of cardiovascular events in patients with type 2 diabetes. Funding: GlaxoSmithKline

Multiple instance learning framework can facilitate explainability in murmur detection.

ObjectiveCardiovascular diseases (CVDs) account for a high fatality rate worldwide. Heart murmurs can be detected from phonocardiograms (PCGs) and may indicate CVDs. Still, they are often overlooked as their detection and correct clinical interpretation require expert skills. In this work, we aim to predict the presence of murmurs and clinical outcomes from multiple PCG recordings employing an explainable multitask model.ApproachOur approach consists of a two-stage multitask model. In the first stage, we predict the murmur presence in single PCGs using a multiple instance learning (MIL) framework. MIL also allows us to derive sample-wise classifications (i.e. murmur locations) while only needing one annotation per recording ("weak label") during training. In the second stage, we fuse explainable hand-crafted features with features from a pooling-based artificial neural network (PANN) derived from the MIL framework. Finally, we predict the presence of murmurs and the clinical outcome for a single patient based on multiple recordings using a simple feed-forward neural network.Main resultsWe show qualitatively and quantitatively that the MIL approach yields useful features and can be used to detect murmurs on multiple time instances and may thus guide a practitioner through PCGs. We analyze the second stage of the model in terms of murmur classification and clinical outcome. We achieved a weighted accuracy of 0.714 and an outcome cost of 13612 when using the PANN model and demographic features on the CirCor dataset (hidden test set of the George B. Moody PhysioNet challenge 2022, team "Heart2Beat", rank 12 / 40).SignificanceTo the best of our knowledge, we are the first to demonstrate the usefulness of MIL in PCG classification. Also, we showcase how the explainability of the model can be analyzed quantitatively, thus avoiding confirmation bias inherent to many post-hoc methods. Finally, our overall results demonstrate the merit of employing MIL combined with handcrafted features for the generation of explainable features as well as for a competitive classification performance