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Machine learning in physical activity

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https://www.readbyqxmd.com/read/29784928/statistical-machine-learning-of-sleep-and-physical-activity-phenotypes-from-sensor-data-in-96-220-uk-biobank-participants
#1
Matthew Willetts, Sven Hollowell, Louis Aslett, Chris Holmes, Aiden Doherty
Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanced random forests with Hidden Markov Models, to reliably detect a number of activity modes. We show that physical activity and sleep behaviours can be classified with 87% accuracy in 159,504 minutes of recorded free-living behaviours from 132 adults. These trained models can be used to infer fine resolution activity patterns at the population scale in 96,220 participants...
May 21, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29772818/integrated-method-for-personal-thermal-comfort-assessment-and-optimization-through-users-feedback-iot-and-machine-learning-a-case-study-%C3%A2
#2
Francesco Salamone, Lorenzo Belussi, Cristian Currò, Ludovico Danza, Matteo Ghellere, Giulia Guazzi, Bruno Lenzi, Valentino Megale, Italo Meroni
Thermal comfort has become a topic issue in building performance assessment as well as energy efficiency. Three methods are mainly recognized for its assessment. Two of them based on standardized methodologies, face the problem by considering the indoor environment in steady-state conditions (PMV and PPD) and users as active subjects whose thermal perception is influenced by outdoor climatic conditions (adaptive approach). The latter method is the starting point to investigate thermal comfort from an overall perspective by considering endogenous variables besides the traditional physical and environmental ones...
May 17, 2018: Sensors
https://www.readbyqxmd.com/read/29756840/discriminative-cooperative-networks-for-detecting-phase-transitions
#3
Ye-Hua Liu, Evert P L van Nieuwenburg
The classification of states of matter and their corresponding phase transitions is a special kind of machine-learning task, where physical data allow for the analysis of new algorithms, which have not been considered in the general computer-science setting so far. Here we introduce an unsupervised machine-learning scheme for detecting phase transitions with a pair of discriminative cooperative networks (DCNs). In this scheme, a guesser network and a learner network cooperate to detect phase transitions from fully unlabeled data...
April 27, 2018: Physical Review Letters
https://www.readbyqxmd.com/read/29756762/molecular-properties-that-define-the-activities-of-antibiotics-in-escherichia-coli-and-pseudomonas-aeruginosa
#4
Sarah J Cooper, Ganesh Krishnamoorthy, David Wolloscheck, John K Walker, Valentin V Rybenkov, Jerry M Parks, Helen I Zgurskaya
The permeability barrier of Gram-negative cell envelopes is the major obstacle in the discovery and development of new antibiotics. In Gram-negative bacteria, these difficulties are exacerbated by the synergistic interaction between two biochemically distinct phenomena, the low permeability of the outer membrane (OM) and active multidrug efflux. In this study, we used Pseudomonas aeruginosa and Escherichia coli strains with controllable permeability barriers-achieved through hyperporination of the OMs and varied efflux capacities-to evaluate the contributions of each of the barriers to protection from antibacterials...
May 14, 2018: ACS Infectious Diseases
https://www.readbyqxmd.com/read/29731511/learning-in-the-machine-random-backpropagation-and-the-deep-learning-channel
#5
Pierre Baldi, Peter Sadowski, Zhiqin Lu
Random backpropagation (RBP) is a variant of the backpropagation algorithm for training neural networks, where the transpose of the forward matrices are replaced by fixed random matrices in the calculation of the weight updates. It is remarkable both because of its effectiveness, in spite of using random matrices to communicate error information, and because it completely removes the taxing requirement of maintaining symmetric weights in a physical neural system. To better understand random backpropagation, we first connect it to the notions of local learning and learning channels...
July 2018: Artificial Intelligence
https://www.readbyqxmd.com/read/29723236/health-management-and-pattern-analysis-of-daily-living-activities-of-people-with-dementia-using-in-home-sensors-and-machine-learning-techniques
#6
Shirin Enshaeifar, Ahmed Zoha, Andreas Markides, Severin Skillman, Sahr Thomas Acton, Tarek Elsaleh, Masoud Hassanpour, Alireza Ahrabian, Mark Kenny, Stuart Klein, Helen Rostill, Ramin Nilforooshan, Payam Barnaghi
The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes...
2018: PloS One
https://www.readbyqxmd.com/read/29588361/sequential-regulatory-activity-prediction-across-chromosomes-with-convolutional-neural-networks
#7
David R Kelley, Yakir Reshef, Maxwell Bileschi, David Belanger, Cory Y McLean, Jasper Snoek
Functional genomics approaches to better model genotype-phenotype relationships have important applications toward understanding genomic function and improving human health. In particular, thousands of noncoding loci associated with diseases and physical traits lack mechanistic explanation. Here, we develop the first machine-learning system to predict cell type-specific epigenetic and transcriptional profiles in large mammalian genomes from DNA sequence alone. Using convolutional neural networks, this system identifies promoters and distal regulatory elements and synthesizes their content to make effective gene expression predictions...
March 27, 2018: Genome Research
https://www.readbyqxmd.com/read/29581467/extracting-biological-age-from-biomedical-data-via-deep-learning-too-much-of-a-good-thing
#8
Timothy V Pyrkov, Konstantin Slipensky, Mikhail Barg, Alexey Kondrashin, Boris Zhurov, Alexander Zenin, Mikhail Pyatnitskiy, Leonid Menshikov, Sergei Markov, Peter O Fedichev
Age-related physiological changes in humans are linearly associated with age. Naturally, linear combinations of physiological measures trained to estimate chronological age have recently emerged as a practical way to quantify aging in the form of biological age. In this work, we used one-week long physical activity records from a 2003-2006 National Health and Nutrition Examination Survey (NHANES) to compare three increasingly accurate biological age models: the unsupervised Principal Components Analysis (PCA) score, a multivariate linear regression, and a state-of-the-art deep convolutional neural network (CNN)...
March 26, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29555624/social-media-as-a-catalyst-for-policy-action-and-social-change-for-health-and-well-being-viewpoint
#9
Douglas Yeung
This viewpoint paper argues that policy interventions can benefit from the continued use of social media analytics, which can serve as an important complement to traditional social science data collection and analysis. Efforts to improve well-being should provide an opportunity to explore these areas more deeply, and encourage the efforts of those conducting national and local data collection on health to incorporate more of these emerging data sources. Social media remains a relatively untapped source of information to catalyze policy action and social change...
March 19, 2018: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/29531065/blindfold-learning-of-an-accurate-neural-metric
#10
Christophe Gardella, Olivier Marre, Thierry Mora
The brain has no direct access to physical stimuli but only to the spiking activity evoked in sensory organs. It is unclear how the brain can learn representations of the stimuli based on those noisy, correlated responses alone. Here we show how to build an accurate distance map of responses solely from the structure of the population activity of retinal ganglion cells. We introduce the Temporal Restricted Boltzmann Machine to learn the spatiotemporal structure of the population activity and use this model to define a distance between spike trains...
March 27, 2018: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/29463052/personalized-physical-activity-coaching-a-machine-learning-approach
#11
Talko B Dijkhuis, Frank J Blaauw, Miriam W van Ittersum, Hugo Velthuijsen, Marco Aiello
Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant's progress in achieving a personal step goal...
February 19, 2018: Sensors
https://www.readbyqxmd.com/read/29432115/the-pediatric-smartshoe-wearable-sensor-system-for-ambulatory-monitoring-of-physical-activity-and-gait
#12
Nagaraj Hegde, Ting Zhang, Gitendra Uswatte, Edward Taub, Joydip Barman, Staci McKay, Andrea Taylor, David M Morris, Angi Griffin, Edward S Sazonov
Cerebral palsy (CP) is a group of nonprogressive neuro-developmental conditions occurring in early childhood that causes movement disorders and physical disability. Measuring activity levels and gait patterns is an important aspect of CP rehabilitation programs. Traditionally, such programs utilize commercially available laboratory systems, which cannot to be utilized in community living. In this study, a novel, shoe-based, wearable sensor system (pediatric SmartShoe) was tested on 11 healthy children and 10 children with CP to validate its use for monitoring of physical activity and gait...
February 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/29371177/evaluating-machine-learning-based-automated-personalized-daily-step-goals-delivered-through-a-mobile-phone-app-randomized-controlled-trial
#13
Mo Zhou, Yoshimi Fukuoka, Yonatan Mintz, Ken Goldberg, Philip Kaminsky, Elena Flowers, Anil Aswani
BACKGROUND: Growing evidence shows that fixed, nonpersonalized daily step goals can discourage individuals, resulting in unchanged or even reduced physical activity. OBJECTIVE: The aim of this randomized controlled trial (RCT) was to evaluate the efficacy of an automated mobile phone-based personalized and adaptive goal-setting intervention using machine learning as compared with an active control with steady daily step goals of 10,000. METHODS: In this 10-week RCT, 64 participants were recruited via email announcements and were required to attend an initial in-person session...
January 25, 2018: JMIR MHealth and UHealth
https://www.readbyqxmd.com/read/29369742/cross-validation-and-out-of-sample-testing-of-physical-activity-intensity-predictions-using-a-wrist-worn-accelerometer
#14
Alexander Hk Montoye, Bradford S Westgate, Morgan R Fonley, Karin A Pfeiffer
Wrist-worn accelerometers are gaining popularity for measurement of physical activity. However, few methods for predicting physical activity intensity from wrist-worn accelerometer data have been tested on data not used to create the methods (out-of-sample data). This study utilized two previously collected datasets (BSU and MSU) in which participants wore a GENEActiv accelerometer on the left wrist while performing sedentary, lifestyle, ambulatory, and exercise activities in simulated free-living settings...
January 25, 2018: Journal of Applied Physiology
https://www.readbyqxmd.com/read/29348200/active-learning-machine-learns-to-create-new-quantum-experiments
#15
Alexey A Melnikov, Hendrik Poulsen Nautrup, Mario Krenn, Vedran Dunjko, Markus Tiersch, Anton Zeilinger, Hans J Briegel
How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments...
February 6, 2018: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/29324298/calibration-of-raw-accelerometer-data-to-measure-physical-activity-a-systematic-review
#16
REVIEW
Márcio de Almeida Mendes, Inácio C M da Silva, Virgílio V Ramires, Felipe F Reichert, Rafaela C Martins, Elaine Tomasi
Most of calibration studies based on accelerometry were developed using count-based analyses. In contrast, calibration studies based on raw acceleration signals are relatively recent and their evidences are incipient. The aim of the current study was to systematically review the literature in order to summarize methodological characteristics and results from raw data calibration studies. The review was conducted up to May 2017 using four databases: PubMed, Scopus, SPORTDiscus and Web of Science. Methodological quality of the included studies was evaluated using the Landis and Koch's guidelines...
March 2018: Gait & Posture
https://www.readbyqxmd.com/read/29202343/interactive-effects-of-water-quality-physical-habitat-and-watershed-anthropogenic-activities-on-stream-ecosystem-health
#17
Hehuan Liao, Emily Sarver, Leigh-Anne H Krometis
Ecological degradation of streams remains a major environmental concern worldwide. While stream restoration has received considerable attention, mitigation efforts focused on the improvement of physical habitat have not proven completely effective. Several small-scale studies have emphasized that effective restoration strategies require a more holistic understanding of the variables at play, although the generalization of the findings based on the small-scale studies remains unclear. Using a comprehensive statewide stream monitoring database from West Virginia (WV), a detailed landscape dataset, and a machine learning algorithm, this study explores the interactive impacts of water quality and physical habitat on stream ecosystem health as indicated by benthic macroinvertebrate scores...
March 1, 2018: Water Research
https://www.readbyqxmd.com/read/29184889/the-conceptualization-of-a-just-in-time-adaptive-intervention-jitai-for-the-reduction-of-sedentary-behavior-in-older-adults
#18
REVIEW
Andre Matthias Müller, Ann Blandford, Lucy Yardley
Low physical activity and high sedentary behavior in older adults can be addressed with interventions that are delivered through modern technology. Just-In-Time Adaptive Interventions (JITAIs) are an emerging technology-driven behavior-change intervention type and capitalize on data that is collected via mobile sensing technology (e.g., smartphones) to trigger appropriate support in real-life. In this paper we integrated behavior change and aging theory and research as well as knowledge around older adult's technology use to conceptualize a JITAI targeting the reduction of sedentary behavior in older adults...
2017: MHealth
https://www.readbyqxmd.com/read/29174728/brain-activity-elicited-by-viewing-pictures-of-the-own-virtually-amputated-body-predicts-xenomelia
#19
Silvia Oddo-Sommerfeld, Jürgen Hänggi, Ludovico Coletta, Silke Skoruppa, Aylin Thiel, Aglaja V Stirn
BACKGROUND: Xenomelia is a rare condition characterized by the persistent desire for the amputation of physically healthy limbs. Prior studies highlighted the importance of superior and inferior parietal lobuli (SPL/IPL) and other sensorimotor regions as key brain structures associated with xenomelia. We expected activity differences in these areas in response to pictures showing the desired body state, i.e. that of an amputee in xenomelia. METHODS: Functional magnetic resonance images were acquired in 12 xenomelia individuals and 11 controls while they viewed pictures of their own real and virtually amputated body...
January 8, 2018: Neuropsychologia
https://www.readbyqxmd.com/read/29174666/combining-ecological-momentary-assessment-with-objective-ambulatory-measures-of-behavior-and-physiology-in-substance-use-research
#20
Jeremiah W Bertz, David H Epstein, Kenzie L Preston
Whereas substance-use researchers have long combined self-report with objective measures of behavior and physiology inside the laboratory, developments in mobile/wearable electronic technology are increasingly allowing for the collection of both subjective and objective information in participants' daily lives. For self-report, ecological momentary assessment (EMA), as implemented on contemporary smartphones or personal digital assistants, can provide researchers with near-real-time information on participants' behavior and mood in their natural environments...
November 16, 2017: Addictive Behaviors
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