Read by QxMD icon Read

Machine learning in physical activity

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 12, 2018: Proceedings of the National Academy of Sciences of the United States of America
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
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
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
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
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...
January 18, 2018: Proceedings of the National Academy of Sciences of the United States of America
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"