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Machine learning in physical activity

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https://www.readbyqxmd.com/read/29324298/calibration-of-raw-accelerometer-data-to-measure-physical-activity-a-systematic-review
#1
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...
December 30, 2017: Gait & Posture
https://www.readbyqxmd.com/read/29202343/interactive-effects-of-water-quality-physical-habitat-and-watershed-anthropogenic-activities-on-stream-ecosystem-health
#2
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...
November 30, 2017: 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
#3
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
#4
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...
November 21, 2017: Neuropsychologia
https://www.readbyqxmd.com/read/29174666/combining-ecological-momentary-assessment-with-objective-ambulatory-measures-of-behavior-and-physiology-in-substance-use-research
#5
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
https://www.readbyqxmd.com/read/29155839/performance-of-thigh-mounted-triaxial-accelerometer-algorithms-in-objective-quantification-of-sedentary-behaviour-and-physical-activity-in-older-adults
#6
Jorgen A Wullems, Sabine M P Verschueren, Hans Degens, Christopher I Morse, Gladys L Onambélé
Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers...
2017: PloS One
https://www.readbyqxmd.com/read/29149847/individuals-explanations-for-their-persistent-or-recurrent-low-back-pain-a-cross-sectional-survey
#7
Jenny Setchell, Nathalia Costa, Manuela Ferreira, Joanna Makovey, Mandy Nielsen, Paul W Hodges
BACKGROUND: Most people experience low back pain (LBP), and it is often ongoing or recurrent. Contemporary research knowledge indicates individual's pain beliefs have a strong effect on their pain experience and management. This study's primary aim was to determine the discourses (patterns of thinking) underlying people's beliefs about what causes their LBP to persist. The secondary aim was to investigate what they believed was the source of this thinking. METHODS: We used a primarily qualitative survey design: 130 participants answered questions about what caused their LBP to persist, and where they learned about these causes...
November 17, 2017: BMC Musculoskeletal Disorders
https://www.readbyqxmd.com/read/29082809/suitability-of-random-forest-analysis-for-epidemiological-research-exploring-sociodemographic-and-lifestyle-related-risk-factors-of-overweight-in-a-cross-sectional-design
#8
Noora Kanerva, Jukka Kontto, Maijaliisa Erkkola, Jaakko Nevalainen, Satu Männistö
AIMS: Factors that contribute to the development of overweight are numerous and form a complex structure with many unknown interactions and associations. We aimed to explore this structure (i.e. the mutual importance or hierarchy of sociodemographic and lifestyle-related risk factors of being overweight) using a machine-learning technique called random forest (RF). The results were compared with traditional logistic regression (LR) analysis. METHODS: The cross-sectional FINRISK 2007 Study included 4757 Finns (aged 25-74 years)...
October 1, 2017: Scandinavian Journal of Public Health
https://www.readbyqxmd.com/read/29060552/optimized-automatic-sleep-stage-classification-using-the-normalized-mutual-information-feature-selection-nmifs-method
#9
Dongrae Cho, Boreom Lee
Sleep is a very important physiological phenomenon for recovery of physical and mental fatigue. Recently, there has been a lot of interest in the quality of sleep and the research is actively under way. In particular, it is important to have a repetitive and regular sleep cycle for good sleep. However, it takes a lot of time to determine sleep stages using physiological signals by experts. In this study, we constructed an optimized classifier based on normalized mutual information feature selection (NMIFS) and kernel based extreme learning machine (K-ELM), and total 4 sleep stages (Awake, weak sleep (stage1+stage2), deep sleep(stage3+stage4) and rapid eye movement (REM)) were automatically classified...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28992270/neural-activity-during-affect-labeling-predicts-expressive-writing-effects-on-well-being-glm-and-svm-approaches
#10
Negar Memarian, Jared B Torre, Kate E Haltom, Annette L Stanton, Matthew D Lieberman
Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0...
September 1, 2017: Social Cognitive and Affective Neuroscience
https://www.readbyqxmd.com/read/28937298/probing-the-toxicity-of-nanoparticles-a-unified-in-silico-machine-learning-model-based-on-perturbation-theory
#11
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
#12
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
#13
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
#14
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
#15
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
#16
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
#17
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 Rehabilitation and Assistive Technologies
https://www.readbyqxmd.com/read/28751371/wearable-knee-health-system-employing-novel-physiological-biomarkers
#18
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
#19
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
#20
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 used to identify potential critical quality attributes (CQAs) of crofelemer, which may be useful in characterizing differently sourced and processed drug products. Crofelemer drug substance was extracted from tablets of one commercial 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...
November 2017: Journal of Pharmaceutical Sciences
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