Monitoring Motor Activity Data for Detecting Patients’ Depression Using Data Augmentation and Privacy-Preserving Distributed Learning

AbstractOsteoarthritis (OA) is a chronic condition often associated with pain, affecting approximately fourteen percent of the population, and increasing in prevalence. A globally aging population have made treating OA-associated pain as well as maintaining mobility and activity a public health priority. OA affects all mammals, and the use of spontaneous animal models is one promising approach for improving translational pain research and the development of effective treatment strategies. Accelerometers are a common tool for collecting high-frequency activity data on animals to study the effects of treatment on pain related activity patterns. There has recently been increasing interest in their use to understand treatment effects in human pain conditions. However, activity patterns vary widely across subjects; furthermore, the effects of treatment may manifest in higher or lower activity counts or in subtler ways like changes in the frequency of certain types of activities. We use a zero inflated Poisson hidden semi-Markov model to characterize activity patterns and subsequently derive estimators of the treatment effect in terms of changes in activity levels or frequency of activity type. We demonstrate the application of our model, and its advance over traditional analysis methods, using data from a naturally occurring feline OA-associated pain model.

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UAV Image Multi-Labeling with Data-Efficient Transformers

Applied Sciences ◽

10.3390/app11093974 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3974

Author(s):

Laila Bashmal ◽

Yakoub Bazi ◽

Mohamad Mahmoud Al Rahhal ◽

Haikel Alhichri ◽

Naif Al Ajlan

Keyword(s):

Data Augmentation ◽

Feature Representation ◽

Aerial Image ◽

Remote Sensing Images ◽

Training Set ◽

Proposed Model ◽

Class Labels ◽

Using Data ◽

Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

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Lockdown, slow down: impact of the COVID-19 pandemic on physical activity—an observational study

Open Heart ◽

10.1136/openhrt-2021-001600 ◽

2021 ◽

Vol 8 (1) ◽

pp. e001600

Author(s):

Joanne Kathryn Taylor ◽

Haarith Ndiaye ◽

Matthew Daniels ◽

Fozia Ahmed

Keyword(s):

Physical Activity ◽

New York ◽

Heart Association ◽

Evaluation Study ◽

Activity Data ◽

Cardiac Devices ◽

Significant Difference ◽

New York Heart Association ◽

Using Data ◽

The Uk

AimsIn response to the COVID-19 pandemic, the UK was placed under strict lockdown measures on 23 March 2020. The aim of this study was to quantify the effects on physical activity (PA) levels using data from the prospective Triage-HF Plus Evaluation study.MethodsThis study represents a cohort of adult patients with implanted cardiac devices capable of measuring activity by embedded accelerometery via a remote monitoring platform. Activity data were available for the 4 weeks pre-implementation and post implementation of ‘stay at home’ lockdown measures in the form of ‘minutes active per day’ (min/day).ResultsData were analysed for 311 patients (77.2% men, mean age 68.8, frailty 55.9%. 92.2% established heart failure (HF) diagnosis, of these 51.2% New York Heart Association II), with comorbidities representative of a real-world cohort.Post-lockdown, a significant reduction in median PA equating to 20.8 active min/day was seen. The reduction was uniform with a slightly more pronounced drop in PA for women, but no statistically significant difference with respect to age, body mass index, frailty or device type. Activity dropped in the immediate 2-week period post-lockdown, but steadily returned thereafter. Median activity week 4 weeks post-lockdown remained significantly lower than 4 weeks pre-lockdown (p≤0.001).ConclusionsIn a population of predominantly HF patients with cardiac devices, activity reduced by approximately 20 min active per day in the immediate aftermath of strict COVID-19 lockdown measures.Trial registration numberNCT04177199.

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Exploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation

Communications Chemistry ◽

10.1038/s42004-021-00528-9 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Nadin Ulrich ◽

Kai-Uwe Goss ◽

Andrea Ebert

Keyword(s):

Partition Coefficient ◽

Data Augmentation ◽

Environmental Chemistry ◽

Chemical Properties ◽

Predictive Performance ◽

Chemical Structures ◽

Tautomeric Forms ◽

Learning Techniques ◽

Water Partition Coefficient ◽

Using Data

AbstractToday more and more data are freely available. Based on these big datasets deep neural networks (DNNs) rapidly gain relevance in computational chemistry. Here, we explore the potential of DNNs to predict chemical properties from chemical structures. We have selected the octanol-water partition coefficient (log P) as an example, which plays an essential role in environmental chemistry and toxicology but also in chemical analysis. The predictive performance of the developed DNN is good with an rmse of 0.47 log units in the test dataset and an rmse of 0.33 for an external dataset from the SAMPL6 challenge. To this end, we trained the DNN using data augmentation considering all potential tautomeric forms of the chemicals. We further demonstrate how DNN models can help in the curation of the log P dataset by identifying potential errors, and address limitations of the dataset itself.

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Data Augmentation Strategies for Human Activity Data Using Generative Adversarial Neural Networks

2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) ◽

10.1109/percomworkshops51409.2021.9431046 ◽

2021 ◽

Author(s):

Alexander Hoelzemann ◽

Nimish Sorathiya ◽

Kristof Van Laerhoven

Keyword(s):

Neural Networks ◽

Human Activity ◽

Data Augmentation ◽

Activity Data ◽

Augmentation Strategies

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Comparison of Night, Day and 24 h Motor Activity Data for the Classification of Depressive Episodes

Diagnostics ◽

10.3390/diagnostics10030162 ◽

2020 ◽

Vol 10 (3) ◽

pp. 162 ◽

Cited By ~ 4

Author(s):

Julieta G. Rodríguez-Ruiz ◽

Carlos E. Galván-Tejada ◽

Laura A. Zanella-Calzada ◽

José M. Celaya-Padilla ◽

Jorge I. Galván-Tejada ◽

...

Keyword(s):

Data Mining ◽

Motor Activity ◽

Random Forest Classifier ◽

Machine Learning Algorithms ◽

World Population ◽

Activity Data ◽

The World ◽

Depressive Episodes ◽

Full Day

Major Depression Disease has been increasing in the last few years, affecting around 7 percent of the world population, but nowadays techniques to diagnose it are outdated and inefficient. Motor activity data in the last decade is presented as a better way to diagnose, treat and monitor patients suffering from this illness, this is achieved through the use of machine learning algorithms. Disturbances in the circadian rhythm of mental illness patients increase the effectiveness of the data mining process. In this paper, a comparison of motor activity data from the night, day and full day is carried out through a data mining process using the Random Forest classifier to identified depressive and non-depressive episodes. Data from Depressjon dataset is split into three different subsets and 24 features in time and frequency domain are extracted to select the best model to be used in the classification of depression episodes. The results showed that the best dataset and model to realize the classification of depressive episodes is the night motor activity data with 99.37% of sensitivity and 99.91% of specificity.

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