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Machine learning in physical activity

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https://www.readbyqxmd.com/read/28937298/probing-the-toxicity-of-nanoparticles-a-unified-in-silico-machine-learning-model-based-on-perturbation-theory
#1
Riccardo Concu, Valeria V Kleandrova, Alejandro Speck-Planche, M Natália D S Cordeiro
Nanoparticles (NPs) are part of our daily life, having a wide range of applications in engineering, physics, chemistry, and biomedicine. However, there are serious concerns regarding the harmful effects that NPs can cause to the different biological systems and their ecosystems. Toxicity testing is an essential step for assessing the potential risks of the NPs, but the experimental assays are often very expensive and usually too slow to flag the number of NPs that may cause adverse effects. In silico models centered on quantitative structure-activity/toxicity relationships (QSAR/QSTR) are alternative tools that have become valuable supports to risk assessment, rationalizing the search for safer NPs...
September 22, 2017: Nanotoxicology
https://www.readbyqxmd.com/read/28929121/differential-and-combined-effects-of-physical-activity-profiles-and-prohealth-behaviors-on-diabetes-prevalence-among-blacks-and-whites-in-the-us-population-a-novel-bayesian-belief-network-machine-learning-analysis
#2
Azizi A Seixas, Dwayne A Henclewood, Aisha T Langford, Samy I McFarlane, Ferdinand Zizi, Girardin Jean-Louis
The current study assessed the prevalence of diabetes across four different physical activity lifestyles and infer through machine learning which combinations of physical activity, sleep, stress, and body mass index yield the lowest prevalence of diabetes in Blacks and Whites. Data were extracted from the National Health Interview Survey (NHIS) dataset from 2004-2013 containing demographics, chronic diseases, and sleep duration (N = 288,888). Of the total sample, 9.34% reported diabetes (where the prevalence of diabetes was 12...
2017: Journal of Diabetes Research
https://www.readbyqxmd.com/read/28906437/developing-fine-grained-actigraphies-for-rheumatoid-arthritis-patients-from-a-single-accelerometer-using-machine-learning
#3
Javier Andreu-Perez, Luis Garcia-Gancedo, Jonathan McKinnell, Anniek Van der Drift, Adam Powell, Valentin Hamy, Thomas Keller, Guang-Zhong Yang
In addition to routine clinical examination, unobtrusive and physical monitoring of Rheumatoid Arthritis (RA) patients provides an important source of information to enable understanding the impact of the disease on quality of life. Besides an increase in sedentary behaviour, pain in RA can negatively impact simple physical activities such as getting out of bed and standing up from a chair. The objective of this work is to develop a method that can generate fine-grained actigraphies to capture the impact of the disease on the daily activities of patients...
September 14, 2017: Sensors
https://www.readbyqxmd.com/read/28880923/automatic-machine-learning-based-identification-of-jogging-periods-from-accelerometer-measurements-of-adolescents-under-field-conditions
#4
Eftim Zdravevski, Biljana Risteska Stojkoska, Marie Standl, Holger Schulz
BACKGROUND: Assessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are: to develop an algorithm for automatic identification of intended jogging periods; and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared to when using only one at either position...
2017: PloS One
https://www.readbyqxmd.com/read/28813938/online-sparse-gaussian-process-based-human-motion-intent-learning-for-an-electrically-actuated-lower-extremity-exoskeleton
#5
Yi Long, Zhi-Jiang Du, Chao-Feng Chen, Wei Dong, Wei-Dong Wang
The most important step for lower extremity exoskeleton is to infer human motion intent (HMI), which contributes to achieve human exoskeleton collaboration. Since the user is in the control loop, the relationship between human robot interaction (HRI) information and HMI is nonlinear and complicated, which is difficult to be modeled by using mathematical approaches. The nonlinear approximation can be learned by using machine learning approaches. Gaussian Process (GP) regression is suitable for high-dimensional and small-sample nonlinear regression problems...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28809669/muscle-activation-and-inertial-motion-data-for-non-invasive-classification-of-activities-of-daily-living
#6
Michael S Totty, Eric Wade
OBJECTIVE: Remote monitoring of physical activity using body-worn sensors provides an objective alternative to current functional assessment tools. The purpose of this study was to assess the feasibility of classifying categories of activities of daily living from the Functional Arm Activity Behavioral Observation System (FAABOS) using muscle activation and motion data. METHODS: Ten non-disabled, healthy adults were fitted with a Myo armband on the upper forearm...
August 10, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28798008/activity-recognition-in-individuals-walking-with-assistive-devices-the-benefits-of-device-specific-models
#7
Luca Lonini, Aakash Gupta, Susan Deems-Dluhy, Shenan Hoppe-Ludwig, Konrad Kording, Arun Jayaraman
BACKGROUND: Wearable sensors gather data that machine-learning models can convert into an identification of physical activities, a clinically relevant outcome measure. However, when individuals with disabilities upgrade to a new walking assistive device, their gait patterns can change, which could affect the accuracy of activity recognition. OBJECTIVE: The objective of this study was to assess whether we need to train an activity recognition model with labeled data from activities performed with the new assistive device, rather than data from the original device or from healthy individuals...
August 10, 2017: JMIR Rehabil Assist Technol
https://www.readbyqxmd.com/read/28751371/wearable-knee-health-system-employing-novel-physiological-biomarkers
#8
Omer T Inan, Daniel C Whittingslow, Caitlin N Teague, Sinan Hersek, Maziyar Baran Pouyan, Mindy Millard-Stafford, Geza F Kogler, Michael N Sawka
Knee injuries and chronic disorders, such as arthritis, affect millions of Americans leading to missed workdays and reduced quality of life. Currently, after an initial diagnosis, there are few quantitative technologies available to provide sensitive sub-clinical feedback to patients regarding improvements or setbacks to their knee health status; instead, most assessments are qualitative, relying on patient-reported symptoms, performance during functional tests, and physical examinations. Recent advances have been made with wearable technologies for assessing the health status of the knee (and potentially other joints) with the goal of facilitating personalized rehabilitation of injuries and care for chronic conditions...
July 27, 2017: Journal of Applied Physiology
https://www.readbyqxmd.com/read/28751369/wearable-technology-for-compensatory-reserve-to-sense-hypovolemia
#9
Victor A Convertino, Michael N Sawka
Traditional monitoring technologies fail to provide accurate or early indications of hypovolemia-mediated extremis because physiological systems (as measured by vital signs) effectively compensate until circulatory failure occurs. Hypovolemia is the most life-threatening physiological condition associated with circulatory shock in hemorrhage or sepsis, and it impairs one's ability to sustain physical exertion during heat stress. This review focuses on the physiology underlying the development of a novel non-invasive wearable technology that allows for real-time evaluation of the cardiovascular system's ability to compensate to hypovolemia, or its compensatory reserve, which provides an individualized estimate of impending circulatory collapse...
July 27, 2017: Journal of Applied Physiology
https://www.readbyqxmd.com/read/28743606/the-botanical-drug-substance-crofelemer-as-a-model-system-for-comparative-characterization-of-complex-mixture-drugs
#10
Peter A Kleindl, Jian Xiong, Asha Hewarathna, Olivier Mozziconacci, Maulik K Nariya, Adam C Fisher, Eric J Deeds, Sangeeta B Joshi, C Russell Middaugh, Christian Schöneich, David B Volkin, M Laird Forrest
Crofelemer is a botanical polymeric proanthocyanidin that inhibits chloride channel activity and is used clinically for treating HIV-associated secretory diarrhea. Crofelemer lots may exhibit significant physicochemical variation due to the natural source of the raw material. A variety of physical, chemical, and biological assays were utilized to identify potential critical quality attributes (CQAs) of crofelemer, which may be useful in characterizing differently sourced and/or processed drug products. Crofelemer drug substance was extracted from tablets of one commerical drug product lot, fractionated, and subjected to accelerated thermal degradation studies to produce derivative lots with variations in chemical and physical composition potentially representative of manufacturing and raw material variation...
July 22, 2017: Journal of Pharmaceutical Sciences
https://www.readbyqxmd.com/read/28719743/machine-learning-on-signal-to-noise-ratios-improves-peptide-array-design-in-samdi-mass-spectrometry
#11
Albert Y Xue, Lindsey C Szymczak, Milan Mrksich, Neda Bagheri
Emerging peptide array technologies are able to profile molecular activities within cell lysates. However, the structural diversity of peptides leads to inherent differences in peptide signal-to-noise ratios (S/N). These complex effects can lead to potentially unrepresentative signal intensities and can bias subsequent analyses. Within mass spectrometry-based peptide technologies, the relation between a peptide's amino acid sequence and S/N remains largely nonquantitative. To address this challenge, we present a method to quantify and analyze mass spectrometry S/N of two peptide arrays, and we use this analysis to portray quality of data and to design future arrays for SAMDI mass spectrometry...
September 5, 2017: Analytical Chemistry
https://www.readbyqxmd.com/read/28668251/bicycle-trains-cycling-and-physical-activity-a-pilot-cluster-rct
#12
Jason A Mendoza, Wren Haaland, Maya Jacobs, Mark Abbey-Lambertz, Josh Miller, Deb Salls, Winifred Todd, Rachel Madding, Katherine Ellis, Jacqueline Kerr
INTRODUCTION: Increasing children's cycling to school and physical activity are national health goals. The objective was to conduct an RCT of a bicycle train program to assess impact on students' school travel mode and moderate-to-vigorous physical activity (MVPA). STUDY DESIGN: Pilot cluster RCT with randomization at the school level and N=54 participants. SETTING/PARTICIPANTS: Fourth-fifth graders from four public schools serving low-income families in Seattle, WA in 2014 with analyses in 2015-2016...
June 23, 2017: American Journal of Preventive Medicine
https://www.readbyqxmd.com/read/28621708/breathing-analysis-using-thermal-and-depth-imaging-camera-video-records
#13
Aleš Procházka, Hana Charvátová, Oldřich Vyšata, Jakub Kopal, Jonathon Chambers
The paper is devoted to the study of facial region temperature changes using a simple thermal imaging camera and to the comparison of their time evolution with the pectoral area motion recorded by the MS Kinect depth sensor. The goal of this research is to propose the use of video records as alternative diagnostics of breathing disorders allowing their analysis in the home environment as well. The methods proposed include (i) specific image processing algorithms for detecting facial parts with periodic temperature changes; (ii) computational intelligence tools for analysing the associated videosequences; and (iii) digital filters and spectral estimation tools for processing the depth matrices...
June 16, 2017: Sensors
https://www.readbyqxmd.com/read/28599558/computational-analysis-of-acoustic-events-in-everyday-environments
#14
Tuomas Virtanen
Sounds carry a large amount of information about our everyday environment and physical events that take place in it. Recent advances in machine learning allows automatic methods to analyze this information, for example, by detecting and classifying acoustic events produced by various sources. This allows several applications, for example, in acoustic surveillance, context-aware devices, and multimedia indexing. This talk will present signal processing and machine learning methods that can be used to detect and classify everyday acoustic events originating, e...
May 2017: Journal of the Acoustical Society of America
https://www.readbyqxmd.com/read/28596271/extracting-aerobic-system-dynamics-during-unsupervised-activities-of-daily-living-using-wearable-sensor-machine-learning-models
#15
Thomas Beltrame, Robert Amelard, Alexander Wong, Richard L Hughson
Physical activity levels are related through algorithms to the energetic demand with no information regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (VO2) by wearable sensors in realistic settings might permit development of a practical tool for the study of the longitudinal aerobic system dynamics (i.e., VO2 kinetics). This study evaluated aerobic system dynamics based on predicted VO2 data obtained from wearable sensors during unsupervised activities of daily living (uADL)...
June 8, 2017: Journal of Applied Physiology
https://www.readbyqxmd.com/read/28582264/machine-learning-to-improve-energy-expenditure-estimation-in-children-with-disabilities-a-pilot-study-in-duchenne-muscular-dystrophy
#16
Amit Pande, Prasant Mohapatra, Alina Nicorici, Jay J Han
BACKGROUND: Children with physical impairments are at a greater risk for obesity and decreased physical activity. A better understanding of physical activity pattern and energy expenditure (EE) would lead to a more targeted approach to intervention. OBJECTIVE: This study focuses on studying the use of machine-learning algorithms for EE estimation in children with disabilities. A pilot study was conducted on children with Duchenne muscular dystrophy (DMD) to identify important factors for determining EE and develop a novel algorithm to accurately estimate EE from wearable sensor-collected data...
July 19, 2016: JMIR Rehabil Assist Technol
https://www.readbyqxmd.com/read/28536269/a-cognition-related-neural-oscillation-pattern-generated-in-the-prelimbic-cortex-can-control-operant-learning-in-rats
#17
Samuel Hernández-González, Celia Andreu-Sánchez, Miguel Ángel Martín-Pascual, Agnès Gruart, José María Delgado-García
The prelimbic (PrL) cortex constitutes one of the highest levels of cortical hierarchy dedicated to the execution of adaptive behaviors. We have identified a specific local field potential (LFP) pattern generated in the PrL cortex and associated with cognition-related behaviors. We used this pattern to trigger the activation of a visual display on a touch screen as part of an operant conditioning task. Rats learned to increase the presentation rate of the selected θ to β-γ (θ/β-γ) transition pattern across training sessions...
June 14, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28483558/evaluation-of-the-activpal-accelerometer-for-physical-activity-and-energy-expenditure-estimation-in-a-semi-structured-setting
#18
Alexander H K Montoye, James M Pivarnik, Lanay M Mudd, Subir Biswas, Karin A Pfeiffer
OBJECTIVES: Evaluate accuracy of the activPAL and its proprietary software for prediction of time spent in physical activity (PA) intensities (sedentary, light, and moderate-to-vigorous) and energy expenditure (EE) and compare its accuracy to that of a machine learning model (ANN) developed from raw activPAL data. DESIGN: Semi-structured accelerometer validation in a laboratory setting. METHODS: Participants (n=41 [20 male]; age=22.0±4.2) completed a 90-min protocol performing 13 activities for 3-10min each and choosing activity order, duration, and intensity...
April 21, 2017: Journal of Science and Medicine in Sport
https://www.readbyqxmd.com/read/28419025/ensemble-methods-for-classification-of-physical-activities-from-wrist-accelerometry
#19
Alok Kumar Chowdhury, Dian Tjondronegoro, Vinod Chandran, Stewart G Trost
PURPOSE: To investigate whether the use of ensemble learning algorithms improve physical activity recognition accuracy compared to the single classifier algorithms, and to compare the classification accuracy achieved by three conventional ensemble machine learning methods (bagging, boosting, random forest) and a custom ensemble model comprising four algorithms commonly used for activity recognition (binary decision tree, k nearest neighbor, support vector machine, and neural network)...
September 2017: Medicine and Science in Sports and Exercise
https://www.readbyqxmd.com/read/28378815/prediction-of-oxygen-uptake-dynamics-by-machine-learning-analysis-of-wearable-sensors-during-activities-of-daily-living
#20
T Beltrame, R Amelard, A Wong, R L Hughson
Currently, oxygen uptake () is the most precise means of investigating aerobic fitness and level of physical activity; however, can only be directly measured in supervised conditions. With the advancement of new wearable sensor technologies and data processing approaches, it is possible to accurately infer work rate and predict during activities of daily living (ADL). The main objective of this study was to develop and verify the methods required to predict and investigate the dynamics during ADL. The variables derived from the wearable sensors were used to create a predictor based on a random forest method...
April 5, 2017: Scientific Reports
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