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Machine learning in physical activity

Yago Saez, Alejandro Baldominos, Pedro Isasi
Physical activity is widely known to be one of the key elements of a healthy life. The many benefits of physical activity described in the medical literature include weight loss and reductions in the risk factors for chronic diseases. With the recent advances in wearable devices, such as smartwatches or physical activity wristbands, motion tracking sensors are becoming pervasive, which has led to an impressive growth in the amount of physical activity data available and an increasing interest in recognizing which specific activity a user is performing...
December 30, 2016: Sensors
Michael V McConnell, Anna Shcherbina, Aleksandra Pavlovic, Julian R Homburger, Rachel L Goldfeder, Daryl Waggot, Mildred K Cho, Mary E Rosenberger, William L Haskell, Jonathan Myers, Mary Ann Champagne, Emmanuel Mignot, Martin Landray, Lionel Tarassenko, Robert A Harrington, Alan C Yeung, Euan A Ashley
Importance: Studies have established the importance of physical activity and fitness, yet limited data exist on the associations between objective, real-world physical activity patterns, fitness, sleep, and cardiovascular health. Objectives: To assess the feasibility of obtaining measures of physical activity, fitness, and sleep from smartphones and to gain insights into activity patterns associated with life satisfaction and self-reported disease. Design, Setting, and Participants: The MyHeart Counts smartphone app was made available in March 2015, and prospective participants downloaded the free app between March and October 2015...
January 1, 2017: JAMA Cardiology
Manasi A Pethe, Aliza B Rubenstein, Sagar D Khare
Characterizing the substrate specificity of protease enzymes is critical for illuminating the molecular basis of their diverse and complex roles in a wide array of biological processes. Rapid and accurate prediction of their extended substrate specificity would also aid in the design of custom proteases capable of selectively and controllably cleaving biotechnologically or therapeutically relevant targets. However, current in silico approaches for protease specificity prediction, rely on, and are therefore limited by, machine learning of sequence patterns in known experimental data...
December 6, 2016: Journal of Molecular Biology
Abraham Yosipof, Klimentiy Shimanovich, Hanoch Senderowitz
Material informatics is engaged with the application of informatic principles to materials science in order to assist in the discovery and development of new materials. Central to the field is the application of data mining techniques and in particular machine learning approaches, often referred to as Quantitative Structure Activity Relationship (QSAR) modeling, to derive predictive models for a variety of materials-related "activities". Such models can accelerate the development of new materials with favorable properties and provide insight into the factors governing these properties...
December 2016: Molecular Informatics
Jacqueline Kerr, Catherine R Marinac, Katherine Ellis, Suneeta Godbole, Aaron Hipp, Karen Glanz, Jonathan Mitchell, Francine Laden, Peter James, David Berrigan
PURPOSE: To compare physical activity estimates across different accelerometer wear locations, wear time protocols, and data processing techniques. METHODS: A convenience sample of middle aged to older women wore a GT3X+ accelerometer at the wrist and hip for 7 days. Physical activity estimates were calculated using three data processing techniques: single axis cut points, raw vector magnitude thresholds, and machine learning algorithms applied to the raw data from the three axes...
October 14, 2016: Medicine and Science in Sports and Exercise
Farzaneh Karimi-Alavijeh, Saeed Jalili, Masoumeh Sadeghi
BACKGROUND: Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome...
May 2016: ARYA Atherosclerosis
Quynh C Nguyen, Dapeng Li, Hsien-Wen Meng, Suraj Kath, Elaine Nsoesie, Feifei Li, Ming Wen
BACKGROUND: Studies suggest that where people live, play, and work can influence health and well-being. However, the dearth of neighborhood data, especially data that is timely and consistent across geographies, hinders understanding of the effects of neighborhoods on health. Social media data represents a possible new data resource for neighborhood research. OBJECTIVE: The aim of this study was to build, from geotagged Twitter data, a national neighborhood database with area-level indicators of well-being and health behaviors...
October 17, 2016: JMIR Public Health and Surveillance
Barnan Das, Diane J Cook, Narayanan C Krishnan, Maureen Schmitter-Edgecombe
Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions...
August 2016: IEEE Journal of Selected Topics in Signal Processing
Georgia Spiridon Karanasiou, Evanthia Eleftherios Tripoliti, Theofilos Grigorios Papadopoulos, Fanis Georgios Kalatzis, Yorgos Goletsis, Katerina Kyriakos Naka, Aris Bechlioulis, Abdelhamid Errachid, Dimitrios Ioannis Fotiadis
Heart failure (HF) is a chronic disease characterised by poor quality of life, recurrent hospitalisation and high mortality. Adherence of patient to treatment suggested by the experts has been proven a significant deterrent of the above-mentioned serious consequences. However, the non-adherence rates are significantly high; a fact that highlights the importance of predicting the adherence of the patient and enabling experts to adjust accordingly patient monitoring and management. The aim of this work is to predict the adherence of patients with HF, through the application of machine learning techniques...
September 2016: Healthcare Technology Letters
Katherine Ellis, Jacqueline Kerr, Suneeta Godbole, John Staudenmayer
No abstract text is available yet for this article.
May 2016: Medicine and Science in Sports and Exercise
Fernando García-García, Pedro J Benito, María E Hernando
BACKGROUND: Physical activity (PA) is essential to prevent and to treat a variety of chronic diseases. The automated detection and quantification of PA over time empowers lifestyle interventions, facilitating reliable exercise tracking and data-driven counseling. METHODS: We propose and compare various combinations of machine learning (ML) schemes for the automatic classification of PA from multi-modal data, simultaneously captured by a biaxial accelerometer and a heart rate (HR) monitor...
December 7, 2016: Methods of Information in Medicine
Hiram Ponce, María de Lourdes Martínez-Villaseñor, Luis Miralles-Pechuán
Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data...
2016: Sensors
Robert A Mason, Marcel Adam Just
We used functional MRI (fMRI) to assess neural representations of physics concepts (momentum, energy, etc.) in juniors, seniors, and graduate students majoring in physics or engineering. Our goal was to identify the underlying neural dimensions of these representations. Using factor analysis to reduce the number of dimensions of activation, we obtained four physics-related factors that were mapped to sets of voxels. The four factors were interpretable as causal motion visualization, periodicity, algebraic form, and energy flow...
June 2016: Psychological Science
Jacqueline Kerr, Ruth E Patterson, Katherine Ellis, Suneeta Godbole, Eileen Johnson, Gert Lanckriet, John Staudenmayer
PURPOSE: Walking for health is recommended by health agencies, partly based on epidemiological studies of self-reported behaviors. Accelerometers are now replacing survey data, but it is not clear that intensity-based cut points reflect the behaviors previously reported. New computational techniques can help classify raw accelerometer data into behaviors meaningful for public health. METHODS: Five hundred twenty days of triaxial 30-Hz accelerometer data from three studies (n = 78) were employed as training data...
May 2016: Medicine and Science in Sports and Exercise
David Taylor, Jennifer Murphy, Mian Ahmad, Sanjay Purkayastha, Samantha Scholtz, Ramin Ramezani, Ivaylo Vlaev, Alexandra I F Blakemore, Ara Darzi
Physical activity levels in bariatric patients have not been well documented, despite their importance in maintaining weight loss following surgery. This study investigated the feasibility of tracking physical activity using a smartphone app with minimal user interaction. Thus far, we have obtained good quality data from 255 patients at various points in their weight loss journey. Preliminary analyses indicate little change in physical activity levels following surgery with pre-surgery patients reaching an average of 16 minutes per day and post-surgery patients achieving a daily average of 21 minutes...
2016: Studies in Health Technology and Informatics
Brian B Masek, David S Baker, Roman J Dorfman, Karen DuBrucq, Victoria C Francis, Stephan Nagy, Bree L Richey, Farhad Soltanshahi
We describe a "multistep reaction driven" evolutionary algorithm approach to de novo molecular design. Structures generated by the approach include a proposed synthesis path intended to aid the chemist in assessing the synthetic feasibility of the ideas that are generated. The methodology is independent of how the design ideas are scored, allowing multicriteria drug design to address multiple issues including activity at one or more pharmacological targets, selectivity, physical and ADME properties, and off target liabilities; the methods are compatible with common computer-aided drug discovery "scoring" methodologies such as 2D- and 3D-ligand similarity, docking, desirability functions based on physiochemical properties, and/or predictions from 2D/3D QSAR or machine learning models and combinations thereof to be used to guide design...
April 25, 2016: Journal of Chemical Information and Modeling
Laura D Ellingson, Isaac J Schwabacher, Youngwon Kim, Gregory J Welk, Dane B Cook
UNLABELLED: Accurate assessments of both physical activity and sedentary behaviors are crucial to understand the health consequences of movement patterns and to track changes over time and in response to interventions. PURPOSE: The study evaluates the validity of an integrative, machine learning method for processing activity monitor data in relation to a portable metabolic analyzer (Oxycon mobile [OM]) and direct observation (DO). METHODS: Forty-nine adults (age 18-40 yr) each completed 5-min bouts of 15 activities ranging from sedentary to vigorous intensity in a laboratory setting while wearing ActiGraph (AG) on the hip, activPAL on the thigh, and OM...
August 2016: Medicine and Science in Sports and Exercise
Joanna F Dipnall, Julie A Pasco, Michael Berk, Lana J Williams, Seetal Dodd, Felice N Jacka, Denny Meyer
BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. METHODS: The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010)...
2016: PloS One
Jose Manuel Lopez-Guede, Aitor Moreno-Fernandez-de-Leceta, Alexeiw Martinez-Garcia, Manuel Graña
This paper introduces Lynx, an intelligent system for personal safety at home environments, oriented to elderly people living independently, which encompasses a decision support machine for automatic home risk prevention, tested in real-life environments to respond to real time situations. The automatic system described in this paper prevents such risks by an advanced analytic methods supported by an expert knowledge system. It is minimally intrusive, using plug-and-play sensors and machine learning algorithms to learn the elder's daily activity taking into account even his health records...
2015: BioMed Research International
Eleni I Georga, Vasilios C Protopappas, Demosthenes Polyzos, Dimitrios I Fotiadis
We propose an online machine-learning solution to the problem of nonlinear glucose time series prediction in type 1 diabetes. Recently, extreme learning machine (ELM) has been proposed for training single hidden layer feed-forward neural networks. The high accuracy and fast learning speed of ELM drive us to investigate its applicability to the glucose prediction problem. Given that diabetes self-monitoring data are received sequentially, we focus on online sequential ELM (OS-ELM) and online sequential ELM kernels (KOS-ELM)...
2015: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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