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General General

Bayesian Active Learning for Wearable Stress and Affect Detection

ArXiv Preprint

In the recent past, psychological stress has been increasingly observed in humans, and early detection is crucial to prevent health risks. Stress detection using on-device deep learning algorithms has been on the rise owing to advancements in pervasive computing. However, an important challenge that needs to be addressed is handling unlabeled data in real-time via suitable ground truthing techniques (like Active Learning), which should help establish affective states (labels) while also selecting only the most informative data points to query from an oracle. In this paper, we propose a framework with capabilities to represent model uncertainties through approximations in Bayesian Neural Networks using Monte-Carlo (MC) Dropout. This is combined with suitable acquisition functions for active learning. Empirical results on a popular stress and affect detection dataset experimented on a Raspberry Pi 2 indicate that our proposed framework achieves a considerable efficiency boost during inference, with a substantially low number of acquired pool points during active learning across various acquisition functions. Variation Ratios achieves an accuracy of 90.38% which is comparable to the maximum test accuracy achieved while training on about 40% lesser data.

Abhijith Ragav, Gautham Krishna Gudur

2020-12-04

General General

Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives.

In Non-coding RNA

Long non-coding RNAs (lncRNA), the pervasively transcribed part of the mammalian genome, have played a significant role in changing our protein-centric view of genomes. The abundance of lncRNAs and their diverse roles across cell types have opened numerous avenues for the research community regarding lncRNAome. To discover and understand lncRNAome, many sophisticated computational techniques have been leveraged. Recently, deep learning (DL)-based modeling techniques have been successfully used in genomics due to their capacity to handle large amounts of data and produce relatively better results than traditional machine learning (ML) models. DL-based modeling techniques have now become a choice for many modeling tasks in the field of lncRNAome as well. In this review article, we summarized the contribution of DL-based methods in nine different lncRNAome research areas. We also outlined DL-based techniques leveraged in lncRNAome, highlighting the challenges computational scientists face while developing DL-based models for lncRNAome. To the best of our knowledge, this is the first review article that summarizes the role of DL-based techniques in multiple areas of lncRNAome.

Alam Tanvir, Al-Absi Hamada R H, Schmeier Sebastian

2020-Nov-30

Attention mechanism, CNN, LSTM, convolutional neural network, deep learning, lncRNA, lncRNAome, long non-coding RNA, machine learning

General General

Psychological screening and tracking of athletes and the potential for digital mental health solutions in a hybrid model of care: A mini review.

In JMIR formative research

BACKGROUND : There is a persistent need for mental ill-health prevention and intervention among 'at-risk' and vulnerable subpopulations. Major disruptions to humanity such as the COVID-19 pandemic present an opportunity for a better understanding of the experience of stressors and vulnerability. Faster and better ways of psychological screening and tracking are more generally required in response to the increased demand upon mental health care services. The argument that mental and physical health should be considered together as part of a biopsychosocial approach is garnering acceptance in elite athlete literature. However, the sporting population are unique in that there is an existing stigma of mental health, an under-recognition of mental ill-health as well as engagement difficulties which have hindered research, prevention and intervention efforts.

OBJECTIVE : To summarize and evaluate the literature on athletes' increased vulnerability to mental ill-health, and digital mental health solutions as a complement to prevention and intervention. To show relationships between athlete mental health problems and resilience as well as digital mental health screening and tracking and faster and better treatment algorithms.

METHODS : Mini review.

RESULTS : Consensus statements and systematic reviews indicated that elite athletes have comparable rates of mental ill-health prevalence as the general population. However, peculiar subgroups require disentangling. Innovative expansion of data collection and analytics is required to respond to engagement issues and advance research and treatment programs in the process. Digital platforms, machine learning, deep learning and artificial intelligence are useful for mental health screening and tracking in various subpopulations. It is necessary to determine appropriate conditions for algorithms for utilization in recommendations. Partnered with real-time automation and machine learning models, valid and reliable behavior sensing and digital mental health screening and tracking tools have the potential to drive a consolidated, measurable and balanced risk assessment and management strategy for the prevention and intervention of the sequelae of mental ill-health.

CONCLUSIONS : Athletes are an 'at-risk' subpopulation for mental health problems. However, a subgroup of high-level athletes displayed a resilience which helps them to positively adjust after a period of 'overwhelming' stress. Further consideration of stress and adjustments in brief screening tools is recommended to validate this finding. There is an unrealized potential for broadening the scope of mental health especially symptom and disorder interpretation. Digital platforms for psychological screening and tracking have been widely utilized among general populations but there is yet to be an eminent athlete version. Sport in combination with mental health education should address the barriers to seeking help by increasing awareness of the range of mental ill-health through to positive functioning. A hybrid model of care is recommended, combining traditional face-to-face approaches along with innovative and evaluated digital technologies that may be utilized in prevention and early intervention strategies.

CLINICALTRIAL :

Balcombe Luke, De Leo Diego

2020-Oct-17

General General

Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring

ArXiv Preprint

Various health-care applications such as assisted living, fall detection, etc., require modeling of user behavior through Human Activity Recognition (HAR). Such applications demand characterization of insights from multiple resource-constrained user devices using machine learning techniques for effective personalized activity monitoring. On-device Federated Learning proves to be an effective approach for distributed and collaborative machine learning. However, there are a variety of challenges in addressing statistical (non-IID data) and model heterogeneities across users. In addition, in this paper, we explore a new challenge of interest -- to handle heterogeneities in labels (activities) across users during federated learning. To this end, we propose a framework for federated label-based aggregation, which leverages overlapping information gain across activities using Model Distillation Update. We also propose that federated transfer of model scores is sufficient rather than model weight transfer from device to server. Empirical evaluation with the Heterogeneity Human Activity Recognition (HHAR) dataset (with four activities for effective elucidation of results) on Raspberry Pi 2 indicates an average deterministic accuracy increase of at least ~11.01%, thus demonstrating the on-device capabilities of our proposed framework.

Gautham Krishna Gudur, Satheesh K. Perepu

2020-12-04

General General

Applying Internet information technology combined with deep learning to tourism collaborative recommendation system.

In PloS one ; h5-index 176.0

Recently, more personalized travel methods have emerged in the tourism industry, such as individual travel and self-guided travel. The service models of traditional tourism limit the diversity of service options and cannot fully meet the individual needs of tourists anymore. The aim is to integrate sparse tourism information on the Internet, thereby providing more convenient, faster, and more personalized tourism services. Based on the shortcomings of the traditional tourism recommendation system, a deep learning-based classification processing method of tourism product information is proposed. This method uses word embedding in the data preprocessing stage. The Convolutional Neural Network (CNN) is used to process review information of users and tourism service items. The Deep Neural Network (DNN) is used to process the necessary information of users and tourism service items. Also, factorization machine technology is used to learn the interaction between the extracted features to improve the prediction model. The results show that the proposed model can maintain an excellent precision of 64.2% when generating personalized recommendation lists for users. The sensitivity and accuracy of the recommendation list are better than other algorithms. By adding DNN, the word embedding method, and the factorization machine model, the precision is improved by 30%, 33.3%, and 40%, respectively. The model accuracy is the highest with 40 hidden factors, 100 convolutions, and a 100+50 combination hidden layer. Compared with traditional methods, the proposed algorithm can provide users with personalized travel products more accurately in personalized travel recommendations. The results have enriched and developed the theory of tourism service supply chain, providing a reference for constructing a personalized tourism service system.

Wang Meng

2020

General General

Psychological screening and tracking of athletes and the potential for digital mental health solutions in a hybrid model of care: A mini review.

In JMIR formative research

BACKGROUND : There is a persistent need for mental ill-health prevention and intervention among 'at-risk' and vulnerable subpopulations. Major disruptions to humanity such as the COVID-19 pandemic present an opportunity for a better understanding of the experience of stressors and vulnerability. Faster and better ways of psychological screening and tracking are more generally required in response to the increased demand upon mental health care services. The argument that mental and physical health should be considered together as part of a biopsychosocial approach is garnering acceptance in elite athlete literature. However, the sporting population are unique in that there is an existing stigma of mental health, an under-recognition of mental ill-health as well as engagement difficulties which have hindered research, prevention and intervention efforts.

OBJECTIVE : To summarize and evaluate the literature on athletes' increased vulnerability to mental ill-health, and digital mental health solutions as a complement to prevention and intervention. To show relationships between athlete mental health problems and resilience as well as digital mental health screening and tracking and faster and better treatment algorithms.

METHODS : Mini review.

RESULTS : Consensus statements and systematic reviews indicated that elite athletes have comparable rates of mental ill-health prevalence as the general population. However, peculiar subgroups require disentangling. Innovative expansion of data collection and analytics is required to respond to engagement issues and advance research and treatment programs in the process. Digital platforms, machine learning, deep learning and artificial intelligence are useful for mental health screening and tracking in various subpopulations. It is necessary to determine appropriate conditions for algorithms for utilization in recommendations. Partnered with real-time automation and machine learning models, valid and reliable behavior sensing and digital mental health screening and tracking tools have the potential to drive a consolidated, measurable and balanced risk assessment and management strategy for the prevention and intervention of the sequelae of mental ill-health.

CONCLUSIONS : Athletes are an 'at-risk' subpopulation for mental health problems. However, a subgroup of high-level athletes displayed a resilience which helps them to positively adjust after a period of 'overwhelming' stress. Further consideration of stress and adjustments in brief screening tools is recommended to validate this finding. There is an unrealized potential for broadening the scope of mental health especially symptom and disorder interpretation. Digital platforms for psychological screening and tracking have been widely utilized among general populations but there is yet to be an eminent athlete version. Sport in combination with mental health education should address the barriers to seeking help by increasing awareness of the range of mental ill-health through to positive functioning. A hybrid model of care is recommended, combining traditional face-to-face approaches along with innovative and evaluated digital technologies that may be utilized in prevention and early intervention strategies.

CLINICALTRIAL :

Balcombe Luke, De Leo Diego

2020-Oct-17