Physical Exercise Recommendation and Success Prediction Using Interconnected Recurrent Neural Networks
01 Sep 2021-pp 148-153
TL;DR: In this article, an exercise recommendation system consisting of two interconnected recurrent neural networks (RNNs) was proposed to predict the probability of successful completion of the predicted activity by the individual.
Abstract: Unhealthy behaviors, e.g., physical inactivity and unhealthful food choice, are the primary healthcare cost drivers in developed countries. Pervasive computational, sensing, and communication technology provided by smartphones and smart-watches have made it possible to support individuals in their everyday lives to develop healthier lifestyles. In this paper, we propose an exercise recommendation system that also predicts individual success rates. The system, consisting of two interconnected recurrent neural networks (RNNs), uses the history of workouts to recommend the next workout activity for each individual. The system then predicts the probability of successful completion of the predicted activity by the individual. The prediction accuracy of this interconnected-RNN model is assessed on previously published data from a four-week mobile health experiment and is shown to improve upon previous predictions from a computational cognitive model.
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TL;DR: In this paper , the authors present an extensive literature review to explore the current trends in ML-based adaptive systems for health and well-being, focusing on five key areas across the target domains: data collection strategies, model development process, ML techniques utilized, model evaluation techniques, as well as adaptive or personalization strategies for health-related interventions.
Abstract: Abstract Traditional health systems mostly rely on rules created by experts to offer adaptive interventions to patients. However, with recent advances in artificial intelligence (AI) and machine learning (ML) techniques, health-related systems are becoming more sophisticated with higher accuracy in providing more personalized interventions or treatments to individual patients. In this paper, we present an extensive literature review to explore the current trends in ML-based adaptive systems for health and well-being. We conduct a systematic search for articles published between January 2011 and April 2022 and selected 87 articles that met our inclusion criteria for review. The selected articles target 18 health and wellness domains including disease management, assistive healthcare, medical diagnosis, mental health, physical activity, dietary management, health monitoring, substance use, smoking cessation, homeopathy remedy finding, patient privacy, mobile health (mHealth) apps finder, clinician knowledge representation for neonatal emergency care, dental and oral health, medication management, disease surveillance, medical specialty recommendation, and health awareness. Our review focuses on five key areas across the target domains: data collection strategies, model development process, ML techniques utilized, model evaluation techniques, as well as adaptive or personalization strategies for health and wellness interventions. We also identified various technical and methodological challenges including data volume constraints, data quality issues, data diversity or variability issues, infrastructure-related issues, and suitability of interventions which offer directions for future work in this area. Finally, we offer recommendations for tackling these challenges, leveraging on technological advances such as multimodality, Cloud technology, online learning, edge computing, automatic re-calibration, Bluetooth auto-reconnection, feedback pipeline, federated learning, explainable AI, and co-creation of health and wellness interventions.
8 citations
TL;DR: In this paper , a real-time active learner is proposed to calculate the uncertainty of the recommendation system at each time step for each user and asks an expert for recommendation when the certainty is low.
Abstract: Recommendation systems play an important role in today’s digital world. They have found applications in various areas such as music platforms, e.g., Spotify, and movie streaming services, e.g., Netflix. Less research effort has been devoted to physical exercise recommendation systems. Sedentary lifestyles have become the major driver of several diseases as well as healthcare costs. In this paper, we develop a recommendation system to recommend daily exercise activities to users based on their history, profiles and similar users. The developed recommendation system uses a deep recurrent neural network with user-profile attention and temporal attention mechanisms. Moreover, exercise recommendation systems are significantly different from streaming recommendation systems in that we are not able to collect click feedback from the participants in exercise recommendation systems. Thus, we propose a real-time, expert-in-the-loop active learning procedure. The active learner calculates the uncertainty of the recommendation system at each time step for each user and asks an expert for recommendation when the certainty is low. In this paper, we derive the probability distribution function of marginal distance , and use it to determine when to ask experts for feedback. Our experimental results on a mHealth and MovieLens datasets show improved accuracy after incorporating the real-time active learner with the recommendation system.
4 citations
23 Jan 2023
TL;DR: Recommendation systems have become popular in widespread applications and many researchers are exploring it to make them more efficient and effective as mentioned in this paper , however, many research challenges and issues in the design of effective recommendation systems are yet to be addressed in an effective manner.
Abstract: In recent times information available on Internet is growing at the exponential rate. This makes searching correct information very difficult and it is challenging to perform the same within shortest amount of time. In such context there is need of recommender systems which can help in information filtering and promoting the information which is likely as per corresponding user’s interest. Recommender systems have become popular in widespread applications and many researchers are exploring it to make them more efficient and effective. Though recommendations systems are being used for a quite long time, many research challenges and issues in the design of effective recommendation systems are yet to be addressed in an effective manner. This paper discusses research challenges, opportunities and possible applications of recommender systems. Recommendation systems are categorized into collaborative and content-based filtering. This paper briefs working of both types of recommender systems and also discusses research challenges within them. The collaborative filtering approach suffers from many drawbacks, including data sparsity, gray sheep, cold start problem, and scalability. The content-based filtering approach suffers from reciprocity, sparsity and limited content analysis issues. Also, future research directions in collaborative filtering and content-based recommendation systems discussed. Various application domains have listed out where recommendation systems can be improved, such as healthcare, agriculture, etc. The paper describes possible future extensions in all these applications. Overall this paper will act as a guide for those who are interested in doing research in the recommendation system.
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Proceedings Article•
01 Jan 2015TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Abstract: We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.
111,197 citations
Book•
01 Jan 1997TL;DR: SelfSelf-Efficacy (SE) as discussed by the authors is a well-known concept in human behavior, which is defined as "belief in one's capabilities to organize and execute the courses of action required to produce given attainments".
Abstract: Albert Bandura and the Exercise of Self-Efficacy Self-Efficacy: The Exercise of Control Albert Bandura. New York: W. H. Freeman (www.whfreeman.com). 1997, 604 pp., $46.00 (hardcover). Enter the term "self-efficacy" in the on-line PSYCLIT database and you will find over 2500 articles, all of which stem from the seminal contributions of Albert Bandura. It is difficult to do justice to the immense importance of this research for our theories, our practice, and indeed for human welfare. Self-efficacy (SE) has proven to be a fruitful construct in spheres ranging from phobias (Bandura, Jeffery, & Gajdos, 1975) and depression (Holahan & Holahan, 1987) to career choice behavior (Betz & Hackett, 1986) and managerial functioning (Jenkins, 1994). Bandura's Self-Efficacy: The Exercise of Control is the best attempt so far at organizing, summarizing, and distilling meaning from this vast and diverse literature. Self-Efficacy may prove to be Bandura's magnum opus. Dr. Bandura has done an impressive job of summarizing over 1800 studies and papers, integrating these results into a coherent framework, and detailing implications for theory and practice. While incorporating prior works such as Social Learning Theory (Bandura, 1977) and "Self-efficacy mechanism in human agency" (Bandura, 1982), Self-Efficacy extends these works by describing results of diverse new research, clarifying and extending social cognitive theory, and fleshing out implications of the theory for groups, organizations, political bodies, and societies. Along the way, Dr. Bandura masterfully contrasts social cognitive theory with many other theories of human behavior and helps chart a course for future research. Throughout, B andura' s clear, firm, and self-confident writing serves as the perfect vehicle for the theory he espouses. Self-Efficacy begins with the most detailed and clear explication of social cognitive theory that I have yet seen, and proceeds to delineate the nature and sources of SE, the well-known processes via which SE mediates human behavior, and the development of SE over the life span. After laying this theoretical groundwork, subsequent chapters delineate the relevance of SE to human endeavor in a variety of specific content areas including cognitive and intellectual functioning; health; clinical problems including anxiety, phobias, depression, eating disorders, alcohol problems, and drug abuse; athletics and exercise activity; organizations; politics; and societal change. In Bandura's words, "Perceived self-efficacy refers to beliefs in one's capabilities to organize and execute the courses of action required to produce given attainments" (p. 3). People's SE beliefs have a greater effect on their motivation, emotions, and actions than what is objectively true (e.g., actual skill level). Therefore, SE beliefs are immensely important in choice of behaviors (including occupations, social relationships, and a host of day-to-day behaviors), effort expenditure, perseverance in pursuit of goals, resilience to setbacks and problems, stress level and affect, and indeed in our ways of thinking about ourselves and others. Bandura affirms many times that humans are proactive and free as well as determined: They are "at least partial architects of their own destinies" (p. 8). Because SE beliefs powerfully affect human behaviors, they are a key factor in human purposive activity or agency; that is, in human freedom. Because humans shape their environment even as they are shaped by it, SE beliefs are also pivotal in the construction of our social and physical environments. Bandura details over two decades of research confirming that SE is modifiable via mastery experiences, vicarious learning, verbal persuasion, and interpretation of physiological states, and that modified SE strongly and consistently predicts outcomes. SE beliefs, then, are central to human self-determination. STRENGTHS One major strength of Self-Efficacy is Bandura's ability to deftly dance from forest to trees and back again to forest, using specific, human examples and concrete situations to highlight his major theoretical premises, to which he then returns. …
46,839 citations
Posted Content•
TL;DR: This paper proposed two novel model architectures for computing continuous vector representations of words from very large data sets, and the quality of these representations is measured in a word similarity task and the results are compared to the previously best performing techniques based on different types of neural networks.
Abstract: We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.
20,077 citations
TL;DR: In this article, the authors present an integrative theoretical framework to explain and predict psychological changes achieved by different modes of treatment, including enactive, vicarious, exhortative, and emotive sources.
16,833 citations
Book•
01 Jan 2013
TL;DR: This book discusses data mining through the lens of cluster analysis, which examines the relationships between data, clusters, and algorithms, and some of the techniques used to solve these problems.
Abstract: 1 Introduction 1.1 What is Data Mining? 1.2 Motivating Challenges 1.3 The Origins of Data Mining 1.4 Data Mining Tasks 1.5 Scope and Organization of the Book 1.6 Bibliographic Notes 1.7 Exercises 2 Data 2.1 Types of Data 2.2 Data Quality 2.3 Data Preprocessing 2.4 Measures of Similarity and Dissimilarity 2.5 Bibliographic Notes 2.6 Exercises 3 Exploring Data 3.1 The Iris Data Set 3.2 Summary Statistics 3.3 Visualization 3.4 OLAP and Multidimensional Data Analysis 3.5 Bibliographic Notes 3.6 Exercises 4 Classification: Basic Concepts, Decision Trees, and Model Evaluation 4.1 Preliminaries 4.2 General Approach to Solving a Classification Problem 4.3 Decision Tree Induction 4.4 Model Overfitting 4.5 Evaluating the Performance of a Classifier 4.6 Methods for Comparing Classifiers 4.7 Bibliographic Notes 4.8 Exercises 5 Classification: Alternative Techniques 5.1 Rule-Based Classifier 5.2 Nearest-Neighbor Classifiers 5.3 Bayesian Classifiers 5.4 Artificial Neural Network (ANN) 5.5 Support Vector Machine (SVM) 5.6 Ensemble Methods 5.7 Class Imbalance Problem 5.8 Multiclass Problem 5.9 Bibliographic Notes 5.10 Exercises 6 Association Analysis: Basic Concepts and Algorithms 6.1 Problem Definition 6.2 Frequent Itemset Generation 6.3 Rule Generation 6.4 Compact Representation of Frequent Itemsets 6.5 Alternative Methods for Generating Frequent Itemsets 6.6 FP-Growth Algorithm 6.7 Evaluation of Association Patterns 6.8 Effect of Skewed Support Distribution 6.9 Bibliographic Notes 6.10 Exercises 7 Association Analysis: Advanced Concepts 7.1 Handling Categorical Attributes 7.2 Handling Continuous Attributes 7.3 Handling a Concept Hierarchy 7.4 Sequential Patterns 7.5 Subgraph Patterns 7.6 Infrequent Patterns 7.7 Bibliographic Notes 7.8 Exercises 8 Cluster Analysis: Basic Concepts and Algorithms 8.1 Overview 8.2 K-means 8.3 Agglomerative Hierarchical Clustering 8.4 DBSCAN 8.5 Cluster Evaluation 8.6 Bibliographic Notes 8.7 Exercises 9 Cluster Analysis: Additional Issues and Algorithms 9.1 Characteristics of Data, Clusters, and Clustering Algorithms 9.2 Prototype-Based Clustering 9.3 Density-Based Clustering 9.4 Graph-Based Clustering 9.5 Scalable Clustering Algorithms 9.6 Which Clustering Algorithm? 9.7 Bibliographic Notes 9.8 Exercises 10 Anomaly Detection 10.1 Preliminaries 10.2 Statistical Approaches 10.3 Proximity-Based Outlier Detection 10.4 Density-Based Outlier Detection 10.5 Clustering-Based Techniques 10.6 Bibliographic Notes 10.7 Exercises Appendix A Linear Algebra Appendix B Dimensionality Reduction Appendix C Probability and Statistics Appendix D Regression Appendix E Optimization Author Index Subject Index
7,356 citations