Bibliografie tematiche / Accelerometry

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Letteratura scientifica selezionata sul tema "Accelerometry"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 5 marzo 2023

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Articoli di riviste sul tema "Accelerometry"

1

Roth, Marilyn A., e Jennifer S. Mindell. "Who Provides Accelerometry Data? Correlates of Adherence to Wearing an Accelerometry Motion Sensor: The 2008 Health Survey for England". Journal of Physical Activity and Health 10, n. 1 (gennaio 2013): 70–78. http://dx.doi.org/10.1123/jpah.10.1.70.

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Abstract (sommario):
Background:Use of objective physical activity measures is rising. We investigated the representativeness of survey participants who wore an accelerometer.Methods:4273 adults aged 16+ from a cross-sectional survey of a random, nationally representative general population sample in England in 2008 were categorized as 1) provided sufficient accelerometry data [4−7 valid days (10+ hrs/d), n = 1724], 2) less than that (n = 237), or 3) declined (n = 302). Multinomial logistic regression identified demographic, socioeconomic, health, lifestyle, and biological correlates of participants in these latter 2 groups, compared with those who provided sufficient accelerometry data (4+ valid days).Results:Those in the random subsample offered the accelerometer were older and more likely to be retired and to report having a longstanding limiting illness than the rest of the adult Health Survey for England participants. Compared with those providing sufficient accelerometery data, those wearing the accelerometer less were younger, less likely to be in paid employment, and more likely to be a current smoker. Those who declined to wear an accelerometer did not differ significantly from those who wore it for sufficient time.Conclusions:We found response bias in wearing the accelerometers for sufficient time, but refusers did not differ from those providing sufficient data. Differences should be acknowledged by data users.
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2

Evenson, Kelly R., Elissa Scherer, Kennedy M. Peter, Carmen C. Cuthbertson e Stephanie Eckman. "Historical development of accelerometry measures and methods for physical activity and sedentary behavior research worldwide: A scoping review of observational studies of adults". PLOS ONE 17, n. 11 (21 novembre 2022): e0276890. http://dx.doi.org/10.1371/journal.pone.0276890.

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Abstract (sommario):
This scoping review identified observational studies of adults that utilized accelerometry to assess physical activity and sedentary behavior. Key elements on accelerometry data collection were abstracted to describe current practices and completeness of reporting. We searched three databases (PubMed, Web of Science, and SPORTDiscus) on June 1, 2021 for articles published up to that date. We included studies of non-institutionalized adults with an analytic sample size of at least 500. The search returned 5686 unique records. After reviewing 1027 full-text publications, we identified and abstracted accelerometry characteristics on 155 unique observational studies (154 cross-sectional/cohort studies and 1 case control study). The countries with the highest number of studies included the United States, the United Kingdom, and Japan. Fewer studies were identified from the continent of Africa. Five of these studies were distributed donor studies, where participants connected their devices to an application and voluntarily shared data with researchers. Data collection occurred between 1999 to 2019. Most studies used one accelerometer (94.2%), but 8 studies (5.2%) used 2 accelerometers and 1 study (0.6%) used 4 accelerometers. Accelerometers were more commonly worn on the hip (48.4%) as compared to the wrist (22.3%), thigh (5.4%), other locations (14.9%), or not reported (9.0%). Overall, 12.7% of the accelerometers collected raw accelerations and 44.6% were worn for 24 hours/day throughout the collection period. The review identified 155 observational studies of adults that collected accelerometry, utilizing a wide range of accelerometer data processing methods. Researchers inconsistently reported key aspects of the process from collection to analysis, which needs addressing to support accurate comparisons across studies.
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3

Bolton, Samantha, Nick Cave, Naomi Cogger e G. R. Colborne. "Use of a Collar-Mounted Triaxial Accelerometer to Predict Speed and Gait in Dogs". Animals 11, n. 5 (27 aprile 2021): 1262. http://dx.doi.org/10.3390/ani11051262.

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Abstract (sommario):
Accelerometry has been used to measure treatment efficacy in dogs with osteoarthritis, although interpretation is difficult. Simplification of the output into speed or gait categories could simplify interpretation. We aimed to determine whether collar-mounted accelerometry could estimate the speed and categorise dogs’ gait on a treadmill. Eight Huntaway dogs were fitted with a triaxial accelerometer and then recorded using high-speed video on a treadmill at a slow and fast walk, trot, and canter. The accelerometer data (delta-G) was aligned with the video data and records of the treadmill speed and gait. Mixed linear and logistic regression models that included delta-G and a term accounting for the dogs’ skeletal sizes were used to predict speed and gait, respectively, from the accelerometer signal. Gait could be categorised (pseudo-R2 = 0.87) into binary categories of walking and faster (trot or canter), but not into the separate faster gaits. The estimation of speed above 3 m/s was inaccurate, though it is not clear whether that inaccuracy was due to the sampling frequency of the particular device, or whether that is an inherent limitation of collar-mounted accelerometers in dogs. Thus, collar-mounted accelerometry can reliably categorise dogs’ gaits into two categories, but finer gait descriptions or speed estimates require individual dog modelling and validation. Nonetheless, this accelerometry method could improve the use of accelerometry to detect treatment effects in osteoarthritis by allowing the selection of periods of activity that are most affected by treatment.
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4

Gewolb, Ira H., e Frank L. Vice. "Use of a non-invasive accelerometric method for diagnosing gastroesophageal reflux in premature infants". Journal of Perinatology 41, n. 8 (23 marzo 2021): 1879–85. http://dx.doi.org/10.1038/s41372-021-01034-5.

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Abstract (sommario):
Abstract Objective To evaluate the clinical usefulness of a non-invasive accelerometric device to diagnose GER in preterm babies. Study design An accelerometer was taped over the sub-xiphoid process in 110 preterm (GA 29.6 ± 3.3 wk) infants (133 studies). Low frequency, sub-audible signals were captured via digital recording (sampling rate 200 Hz), then re-sampled (rate = 60 Hz) to create a spectrogram (focused range 0–30 Hz). Mean amplitude in the focused range was calculated. Results Of 85 studies with simultaneous pH-metry and accelerometry, 18 had concurrent positive and 23 had concurrent negative scores, 42 had negative pH scores when accelerometry was positive (≥1 µV), consistent with non-acid reflux. Eleven infants at high risk of aspiration received surgical interventions. All but 1 had negative pH scores while 10/11 had positive accelerometry. Conclusions The non-invasiveness of this accelerometric technique allows for GER screening and for repeated testing to assess efficacy of interventions.
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5

Sjöros, Tanja, Henri Vähä-Ypyä, Saara Laine, Taru Garthwaite, Eliisa Löyttyniemi, Harri Sievänen, Kari K. Kalliokoski, Juhani Knuuti, Tommi Vasankari e Ilkka H. A. Heinonen. "Influence of the Duration and Timing of Data Collection on Accelerometer-Measured Physical Activity, Sedentary Time and Associated Insulin Resistance". International Journal of Environmental Research and Public Health 18, n. 9 (6 maggio 2021): 4950. http://dx.doi.org/10.3390/ijerph18094950.

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Abstract (sommario):
Accelerometry is a commonly used method to determine physical activity in clinical studies, but the duration and timing of measurement have seldom been addressed. We aimed to evaluate possible changes in the measured outcomes and associations with insulin resistance during four weeks of accelerometry data collection. This study included 143 participants (median age of 59 (IQR9) years; mean BMI of 30.7 (SD4) kg/m2; 41 men). Sedentary and standing time, breaks in sedentary time, and different intensities of physical activity were measured with hip-worn accelerometers. Differences in the accelerometer-based results between weeks 1, 2, 3 and 4 were analyzed by mixed models, differences during winter and summer by two-way ANOVA, and the associations between insulin resistance and cumulative means of accelerometer results during weeks 1 to 4 by linear models. Mean accelerometry duration was 24 (SD3) days. Sedentary time decreased after three weeks of measurement. More physical activity was measured during summer compared to winter. The associations between insulin resistance and sedentary behavior and light physical activity were non-significant after the first week of measurement, but the associations turned significant in two to three weeks. If the purpose of data collection is to reveal associations between accelerometer-measured outcomes and tenuous health outcomes, such as insulin sensitivity, data collection for at least three weeks may be needed.
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6

Wainberg, Michael, Samuel E. Jones, Lindsay Melhuish Beaupre, Sean L. Hill, Daniel Felsky, Manuel A. Rivas, Andrew S. P. Lim, Hanna M. Ollila e Shreejoy J. Tripathy. "Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: A cross-sectional study of 89,205 participants from the UK Biobank". PLOS Medicine 18, n. 10 (12 ottobre 2021): e1003782. http://dx.doi.org/10.1371/journal.pmed.1003782.

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Abstract (sommario):
Background Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort. Methods and findings In this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures—bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration—were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was β = −0.11 (95% confidence interval −0.13 to −0.10, p = 3 × 10−56, FDR = 6 × 10−55) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry. Conclusions In this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals.
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7

Kwon, Soyang, Patricia Zavos, Katherine Nickele, Albert Sugianto e Mark V. Albert. "Hip and Wrist-Worn Accelerometer Data Analysis for Toddler Activities". International Journal of Environmental Research and Public Health 16, n. 14 (21 luglio 2019): 2598. http://dx.doi.org/10.3390/ijerph16142598.

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Abstract (sommario):
Although accelerometry data are widely utilized to estimate physical activity and sedentary behavior among children age 3 years or older, for toddlers age 1 and 2 year(s), accelerometry data recorded during such behaviors have been far less examined. In particular, toddler’s unique behaviors, such as riding in a stroller or being carried by an adult, have not yet been examined. The objective of this study was to describe accelerometry signal outputs recorded during participation in nine types of behaviors (i.e., running, walking, climbing up/down, crawling, riding a ride-on toy, standing, sitting, riding in a stroller/wagon, and being carried by an adult) among toddlers. Twenty-four toddlers aged 13 to 35 months (50% girls) performed various prescribed behaviors during free play in a commercial indoor playroom while wearing ActiGraph wGT3X-BT accelerometers on a hip and a wrist. Participants’ performances were video-recorded. Based on the video data, accelerometer data were annotated with behavior labels to examine accelerometry signal outputs while performing the nine types of behaviors. Accelerometer data collected during 664 behavior assessments from the 21 participants were used for analysis. Hip vertical axis counts for walking were low (median = 49 counts/5 s). They were significantly lower than those recorded while a toddler was “carried” by an adult (median = 144 counts/5 s; p < 0.01). While standing, sitting, and riding in a stroller, very low hip vertical axis counts were registered (median ≤ 5 counts/5 s). Although wrist vertical axis and vector magnitude counts for “carried” were not higher than those for walking, they were higher than the cut-points for sedentary behaviors. Using various accelerometry signal features, machine learning techniques showed 89% accuracy to differentiate the “carried” behavior from ambulatory movements such as running, walking, crawling, and climbing. In conclusion, hip vertical axis counts alone may be unable to capture walking as physical activity and “carried” as sedentary behavior among toddlers. Machine learning techniques that utilize additional accelerometry signal features could help to recognize behavior types, especially to differentiate being “carried” from ambulatory movements.
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8

Oliver, Melody, Hannah Badland, Suzanne Mavoa, Mitch J. Duncan e Scott Duncan. "Combining GPS, GIS, and Accelerometry: Methodological Issues in the Assessment of Location and Intensity of Travel Behaviors". Journal of Physical Activity and Health 7, n. 1 (gennaio 2010): 102–8. http://dx.doi.org/10.1123/jpah.7.1.102.

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Abstract (sommario):
Background:Global positioning systems (GPS), geographic information systems (GIS), and accelerometers are powerful tools to explain activity within a built environment, yet little integration of these tools has taken place. This study aimed to assess the feasibility of combining GPS, GIS, and accelerometry to understand transport-related physical activity (TPA) in adults.Methods:Forty adults wore an accelerometer and portable GPS unit over 7 consecutive days and completed a demographics questionnaire and 7-day travel log. Accelerometer and GPS data were extracted for commutes to/from workplace and integrated into a GIS database. GIS maps were generated to visually explore physical activity intensity, GPS speeds and routes traveled.Results:GPS, accelerometer, and survey data were collected for 37 participants. Loss of GPS data was substantial due to a range of methodological issues, such as low battery life, signal drop out, and participant noncompliance. Nonetheless, greater travel distances and significantly higher speeds were observed for motorized trips when compared with TPA.Conclusions:Pragmatic issues of using GPS monitoring to understand TPA behaviors and methodological recommendations for future research were identified. Although methodologically challenging, the combination of GPS monitoring, accelerometry and GIS technologies holds promise for understanding TPA within the built environment.
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9

Nedergaard, Niels J., Mark A. Robinson, Elena Eusterwiemann, Barry Drust, Paulo J. Lisboa e Jos Vanrenterghem. "The Relationship Between Whole-Body External Loading and Body-Worn Accelerometry During Team-Sport Movements". International Journal of Sports Physiology and Performance 12, n. 1 (gennaio 2017): 18–26. http://dx.doi.org/10.1123/ijspp.2015-0712.

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Abstract (sommario):
Purpose:To investigate the relationship between whole-body accelerations and body-worn accelerometry during team-sport movements.Methods:Twenty male team-sport players performed forward running and anticipated 45° and 90° side-cuts at approach speeds of 2, 3, 4, and 5 m/s. Whole-body center-of-mass (CoM) accelerations were determined from ground-reaction forces collected from 1 foot–ground contact, and segmental accelerations were measured from a commercial GPS accelerometer unit on the upper trunk. Three higher-specification accelerometers were also positioned on the GPS unit, the dorsal aspect of the pelvis, and the shaft of the tibia. Associations between mechanical load variables (peak acceleration, loading rate, and impulse) calculated from both CoM accelerations and segmental accelerations were explored using regression analysis. In addition, 1-dimensional statistical parametric mapping (SPM) was used to explore the relationships between peak segmental accelerations and CoM-acceleration profiles during the whole foot–ground contact.Results:A weak relationship was observed for the investigated mechanical load variables regardless of accelerometer location and task (R2 values across accelerometer locations and tasks: peak acceleration .08–.55, loading rate .27–.59, and impulse .02–.59). Segmental accelerations generally overestimated whole-body mechanical load. SPM analysis showed that peak segmental accelerations were mostly related to CoM accelerations during the first 40–50% of contact phase.Conclusions:While body-worn accelerometry correlates to whole-body loading in team-sport movements and can reveal useful estimates concerning loading, these correlations are not strong. Body-worn accelerometry should therefore be used with caution to monitor whole-body mechanical loading in the field.
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Schrack, Jennifer, e Amal Wanigatunga. "MOVING, THINKING, AND SLEEPING: NOVEL INSIGHTS INTO PHYSICAL AND COGNITIVE HEALTH FROM ACCELEROMETRY DATA". Innovation in Aging 6, Supplement_1 (1 novembre 2022): 330. http://dx.doi.org/10.1093/geroni/igac059.1303.

Testo completo
Abstract (sommario):
Abstract Physical activity and sleep are well-established predictors of health and longevity with aging. Wrist accelerometers, that produce high-frequency time series data, capture multiple aspects of daily physical activity and sleep 24-hours/day. Historically, the majority of accelerometry-based activity research has employed summary metrics to understand the associations of total daily physical activity and sleep with physical and cognitive health. Although these measures are important for understanding conformity with physical activity and sleep recommendations, they underutilize the potential of these data. Further, the summary metrics may differ by accelerometer type/brand, making it difficult to translate results across device types and studies. This symposium will examine the associations between accelerometry-derived physical activity and various aging-related health outcomes, and compare the measurement properties of two commonly used accelerometers for measuring sleep. Ms. Marino will discuss the association of physical activity volume and fragmentation with the presence of the Apolipoprotein-ε4 genotype in the Baltimore Longitudinal Study of Aging (BLSA), overall and by time of day. Dr. Wanigatunga will present evidence on the association of physical activity patterns with beta amyloid plaques in the BLSA. Dr. Schrack will present the association of physical activity fragmentation and diurnal patterns with peripheral artery disease in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Finally, Ms. Liu will compare measurement of sleep variables derived from two commonly used accelerometers. Collectively, these presentations highlight ways to utilize the richness of accelerometry data to illuminate more sensitive associations between movement and health outcomes to advance prevention science and promote health aging.
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Più fonti

Tesi sul tema "Accelerometry"

1

Nilsson, Andreas. "Physical activity assessed by accelerometry in children". Doctoral thesis, Örebro : Örebro University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-1739.

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2

Machado, Inês Prata. "Human activity data discovery based on accelerometry". Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/10992.

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3

Stoltz, Victor, e Manne Godhe. "Validity of accelerometry in high-intensity complex movements". Thesis, Gymnastik- och idrottshögskolan, GIH, Institutionen för idrotts- och hälsovetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:gih:diva-3268.

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Abstract (sommario):
Abstract Aim The aim of the study was to examine the capability of accelerometers to estimate energy expenditure during high-intensity complex physical activity patterns. Also, to investigate whether placing the monitor on the hip or wrist influenced its prediction ability. Furthermore, the purpose was also to evaluate if there was a significant difference in the aforementioned estimations using data from one axis compared to all three axis combined.     Method A total of 14 subjects, eight men and six women, mean (SD) age of 26, 4 (5,5) years were recruited for the study. The participants performed standardised aerobic exercise while accelerometer data and oxygen uptake was measured simultaneously. Two triaxial accelerometers (Actigraph GT3X) were worn on the hip and wrist during the experiment. Indirect calorimetry, using Oxycon mobile, was chosen as the criterion measure. Validity was determined by comparing accelerometer counts with estimated energy expenditure (EE) in kcal/min, derived from measured oxygen consumption, using bivariate Pearson correlation, linear regression and stepwise regression analyses. Equations were calculated using each participant’s individual regression analyses.   Results The experiment reveals that GT3x presents a moderate correlation (r= 0, 47) for estimating EE from aerobics when worn on the hip and a weak correlation (r = 0.34) when worn on the wrist. However, when combined with the body mass variable, a strong correlation was found between accelerometer data for the hip and EE (r= 0.73). At both positions the vector magnitude (r = 0.47 for the hip and r = 0.34 for the wrist) yielded stronger correlations compared to just using the Y-axis (r = 0.15 for the hip and r= 0.08 for the wrist).     Conclusions In conclusion, this study found that GT3x was not particularly valid for assessing energy expenditure in high intensity complex activities. Wearing the accelerometer on the hip yielded higher correlations compared to wearing it on the wrist. When using the accelerometer for estimations of EE the Vector magnitude is to prefer before the Y-axis solely.
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4

Gerrard-Longworth, S. P. "Measuring physical activity in obese populations using accelerometry". Thesis, University of Salford, 2015. http://usir.salford.ac.uk/34502/.

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Abstract (sommario):
The thesis is concerned with objectively measuring human physical activity through accelerometry, and compares the effectiveness of algorithms between obese and non-obese groups. The thesis comprises three studies: Classification of Aerobic and Gym-based Exercises from Accelerometer Output. This study investigated whether accurate classification could be achieved from hip- or ankle-mounted accelerometers for a programme of aerobic exercises and free-living activities. It also examined whether accuracy was affected by obesity, and whether a single classifier could be applied across BMI groups. The study achieved high classification accuracies (85% for hip and 94% for ankle) for both obese and normal BMI groups using the same approach across groups. Walking Speed Estimation Using Accelerometry. This study aimed to develop a speed estimation model that was applicable across BMI groups, and which utilised a hip-mounted accelerometer. To achieve this, multiple accelerometer signal features were evaluated for use in a linear speed estimation model, and performance was compared between obese and normal BMI groups. The speed estimation algorithm achieved overall RMSE of 0.08ms-1 for a mixed BMI group, which is comparable with previous research using homogeneous groups. Prediction of Energy Expenditure from Accelerometer Output. This study aimed to identify physiological and anthropometric parameters for use in an improved energy expenditure estimation model. Model performance was tested on a mixed BMI group. The energy expenditure prediction model incorporating subject attributes showed around 20% improvement over the standard model. This research found that current approaches to activity classification using accelerometry are equally applicable to obese groups and normal BMI groups. Walking speed prediction was shown to be possible from a hip-mounted accelerometer for both obese and normal BMI groups. Energy expenditure estimation is improved by including subject-specific parameters in the prediction model. Accelerometry is, therefore, a suitable tool for measuring different aspects of physical activity for obese and mixed BMI groups.
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5

Wilhelm, Spencer Christian. "Prediction of Non-Resting Energy Expenditure using Accelerometry". Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/91463.

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Abstract (sommario):
The accurate measurement of total energy expenditure is a cornerstone of metabolic research. However, there is a lack of measurement methods that are valid, objective, inexpensive, and easy to use. Accelerometry, along with validated prediction equations for resting energy requirements, may provide an opportunity to fill this void. Twenty weight stable adults (12 female, 8 male) who recently participated in a controlled feeding study comprised the study sample. Total energy requirements were assessed from the controlled feeding period in which weight stability was achieved using the intake-balance method. Resting energy expenditure was assessed using the Mifflin-St. Jeor equation. Participants wore accelerometers to objectively assess habitual physical activity. The accelerometer data obtained along with subjects' demographic and biometric data were used to predict non-resting energy expenditure (NREE) using step-wise linear regression in JMP. Bland-Altman plots and Spearman's Rho correlations were used to determine the validity of the total energy requirements obtained from the sum of the predicted non-resting energy expenditure. Estimated resting energy expenditure was compared with the total energy requirements assessed using the intake-balance method from the controlled feeding period. The resulting prediction equation is as follows: 480.93 – 180.69(sex) + 0.21(Accelerometer kcals) + 617.98(BF%) = AEE. The sex was coded as 1 for females and 0 for males. This prediction model has a coefficient of determination of 0.74 (0.70 adjusted). On average, the model overestimates AEE by 76 kcals. This new model could be the key to accurately, inexpensively and objectively measuring total energy requirements.
Master of Science
Accurate measurement of the total amount of energy (i.e. calories) utilized by the body throughout the day, also known as total energy expenditure, is a vital component of metabolic research. However, there is a lack of measurement methods that are valid, objective, inexpensive, and easy to use. Accelerometers combined with equations designed to predict total energy expenditure may be able to fill this gap. Accelerometers are devices worn on the body that measure accelerative forces from physical activity. Twenty weight stable adults (12 female, 8 male), who recently participated in a study in which all dietary intake and exercise were closely monitored (controlled feeding study), comprised the study sample. The amount of energy needed to maintain weight (total energy requirements) was assessed from the controlled feeding period in which weight stability was achieved. Resting energy expenditure, the energy burned while the body is at rest, was assessed using an equation often used to estimate energy expenditure, the Mifflin-St. Jeor equation. Participants wore accelerometers to objectively assess habitual physical activity. The accelerometer data obtained along with subjects’ demographic (age, sex) and biometric (height, weight, BMI, etc.) data were used to predict non-resting energy expenditure (resting energy expenditure subtracted from total energy expenditure). Multiple statistical tests were used to determine the validity of the total energy requirements obtained from the sum of the predicted non-resting energy expenditure (NREE) and resting energy expenditure. Estimated resting energy expenditure was compared with the total energy requirements assessed using the intake-balance method from the controlled feeding period. The resulting prediction equation is as follows: 480.93 – 180.69(sex) + 0.21(Accelerometer kcals) + 617.98(BF%) = NREE. The sex was coded as 1 for females and 0 for males. This prediction model has a coefficient of determination of 0.74 (0.70 adjusted), which means 70% of the variation in non-resting energy expenditure was explained by changes in the variables in the equation. On average, the model overestimates NREE by 76 Calories per day. This new model could be the key to accurately, inexpensively and objectively measuring total energy requirements.
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São, Marcos Ana Jorge Romão. "Physical activity measurements in adolescents: accelerometry vs PAI". Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13732.

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Abstract (sommario):
Mestrado em Fisioterapia
Background: Practicing physical activity (PA) has shown to present health benefits at all ages, namely in paediatrics. There are several methods to evaluate PA, however the most frequently used are the self-report questionnaires and accelerometry. The latter is an objective measuring tool, however it requires the use of relatively expensive devices. Questionnaires are easy and quick to apply, and therefore a useful tool to evaluate PA. Despite the existence of some questionnaires to evaluate PA in adolescents, there is still none validated against accelerometry for the Portuguese population. This validation is important, since questionnaires present an error associated to inaccuracies in recall activity. Aims: To validate the Physical Activity Index (PAI), by comparing it with accelerometry in adolescents according to gender and to explore if adolescents follow the physical activity recommendations established for their age. Methods: In this cross-sectional study, adolescents were recruited from 3 basketball teams, 2 classrooms from a school in Aveiro, and an orchestra band classroom. Socio-demographic, anthropometric data and spirometry were collected from the adolescents who participated in this study. Physical Activity (PA) was assessed with accelerometers (Actigraph model - GT3XPlus, Actigraph MTI, Manufacturing Technology Inc., Pensacola, FL, USA) worn during 7 days and the PAI. Pearson correlation coefficients (rs) were calculated to explore the correlations of moderate-to-vigorous PA (MVPA) (min.day-1) and steps per day vs. the PAI. To analyse participants’ ability to follow the recommendations of PA levels, 60 minutes of MVPA and 10,000 to 11,700 steps per day were considered. Chi-square (χ2) tests were used to explore differences between male and female’s ability to reach international recommendations of physical activity levels. Results: Forty nine adolescents (57.14% female; mean age 14.43 ±0.96 years old) participated in this study. Female and male presented similar PA levels measured with a subjective or an objective measure. Correlations between objective and subjective measures were significant and positive only for male (MVPA: r=.514, p=.017; Steps per day: r=.460, p=.041). Most participants were sedentary when analysing the objective data and considering the PA recommendations. Only 1 female (3.57%) and 3 males (14.29%) surpassed the 60 min.day-1 mark and, in terms of steps per day, only 13 females (46.43%) and 13 males (61.90%) registered over 10000 steps per day. Conclusion: When compared with accelerometry the PAI presented as a valid measuring tool only for male adolescents. Adolescents of both genders presented similar levels of PA with both measuring tools, and accelerometry results showed that the majority of adolescents were sedentary. Thus, it is necessary to investigate further in the future about the correlation between accelerometry and the PAI, as well as about the sedentary habits of adolescents.
Enquadramento: A prática de atividade física (AF) tem vindo a apresentar benefícios para a saúde em todas as idades, nomeadamente na pediatria. Existem vários métodos para avaliar a AF, no entanto os mais utilizados são os questionários e a acelerometria. Esta última é uma medida objetiva que, no entanto, requer o uso de instrumentos relativamente dispendiosos. Já os questionários são instrumentos rápidos e fáceis de aplicar, sendo, assim úteis na avaliação da AF. Apesar de existirem alguns instrumentos para avaliar AF em adolescentes, ainda, não existe nenhum validado contra a acelerometria para a população portuguesa. Esta validação é necessária, uma vez que os questionários, por serem medidas subjetivas apresentam um erro associado a imprecisões na capacidade de relato de atividades passadas. Objetivos: Validar o Índice de Atividade Física (IAF) comparando-o com a acelerometria de acordo com o género e explorar se os adolescentes seguem as recomendações de AF estabelecidas para as suas idades. Métodos: Neste estudo transversal, os adolescentes foram recrutados de 3 equipas de basquetebol, 2 turmas de uma escola de Aveiro e 1 turma de uma banda de música de orquestra. Dados sociodemográficos, antropométricos e de espirometria foram recolhidos aos participantes. AF foi medida com acelerómetros (Actigraph modelo - GT3XPlus, Actigraph MTI, Manufacturing Technology Inc., Pensacola, FL, USA), usados durante 7 dias e com o IAF. O coeficiente de correlação de Pearson (rs) foi calculado para explorar as correlações entre os minutos de AF moderada a vigorosa (AFMV) (min.dia-1) e os passos por dia vs. o IAF. Para analisar a capacidade dos participantes seguirem as recomendações dos níveis de AF, foram considerados 60 minutos.dia-1 de AFMV e os 10,000 a 11,700 passos por dia. Testes Quiquadrado (χ2) foram usados para explorar diferenças na capacidade de atingir os níveis de AF estabelecidos em orientações internacionais entre rapazes e raparigas. Resultados: Quarenta e nove adolescentes (57.14% raparigas; idade média 14.43 ±0.96 anos) participaram no estudo. Raparigas e rapazes apresentaram níveis de AF semelhantes, medidos com medidas subjetivas ou objetivas. As correlações entre as medidas subjetiva e objetiva foram significativas e positivas apenas para os rapazes (AFMV: r=.514, p=.017; Passos por dia: r=.460, p=.041). A maioria dos adolescentes mostraram-se sedentários na análise dos dados objetivos e das recomendações de AF. Apenas 1 rapariga (3.57%) e 3 rapazes (14.29%) ultrapassaram a marca dos 60 minutos.dia-1 de AFMV e em relação aos passos por dia, apenas 13 raparigas (46.43%) e 13 rapazes (61.90%) registaram valores acima dos 10,000 passos por dia. Conclusão: O IAF mostrou-se uma ferramenta válida na medição de AF em comparação com a acelerometria, apenas para os rapazes. Adolescentes de ambos os sexos apresentaram níveis de AF semelhantes, em ambos os instrumentos de medida, tendo-se a maioria revelado como sedentários através da análise dos resultados obtidos pela acelerometria Desta forma, é necessário investigar-se mais, no futuro, sobre a correlação entre a acelerometria e o IAF, bem como sobre os hábitos sedentários dos adolescentes.
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van, Hees Vincent Theodoor. "Implementation of raw accelerometry in physical activity epidemiology". Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610767.

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Sato, Kimitake, William A. Sands e Michael H. Stone. "The Reliability of Accelerometry to Measure Weightlifting Performance". Digital Commons @ East Tennessee State University, 2012. https://dc.etsu.edu/etsu-works/4616.

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The purposes of the study were to track weightlifters' barbell acceleration with a portable accelerometer over three training sessions to examine test–retest reliability and to compare peak barbell acceleration at different training intensities. Twelve nationally ranked weightlifters volunteered for this study. The portable accelerometer was attached to the right side of the barbell to measure barbell resultant acceleration during the snatch lift at a sampling frequency of 100 Hz. The data were collected over three training sessions at intensity levels of 80%, 85%, and 90% of one repetition maximum. The data were analyzed using intra-class correlation coefficients (ICCs) for the three training sessions and one-way repeated measure ANOVA to compare the difference in peak barbell acceleration at three intensities. Results showed that the device was highly reliable with an ICC of 0.88 and 95% confidence interval of 0.81–0.93. There were significant differences in peak barbell acceleration at various lifting intensities, indicating a decline of the acceleration as the mass of the barbell became heavier. The portable accelerometer seems useful in measuring barbell acceleration data, which can be analyzed in future studies to monitor a weightlifter's performance in a practical setting instead of testing at a laboratory.
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Siebert, Christopher Michael. "Heart Rate and Accelerometry during Footbag Net Singles Play". Portland State University, 2013.

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Kotru, Krish. "Timekeeping and accelerometry with robust light pulse atom interferometers". Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98681.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 165-173).
Light pulse atom interferometry (LPAI) is a powerful technique for precision measurements of inertial forces and time. Laboratory LPAI systems currently achieve state-ofthe- art acceleration sensitivity and establish the international atomic time standard. However, the realization of practical LPAI in dynamic environments (e.g., rapidly accelerating or rotating platforms) has been limited in part by atom optics-the analogues to optical beamsplitters and mirrors. Atom optics in traditional LPAIs are composed of resonant laser pulses that are susceptible to variations in optical detuning and intensity expected in sensors designed for dynamic environments. This thesis investigates atom optics that use frequency- and intensity-modulated laser pulses to suppress sensitivity to these inhomogeneities. For atomic timekeeping applications, a Ramsey LPAI sequence based on stimulated Raman transitions and frequency-swept adiabatic rapid passage (ARP) was developed. Raman ARP drives coherent transfer in an effective two-level atomic system by sweeping the Raman detuning through the two-photon resonance. In experiments with ¹³³Cs atoms, Raman ARP reduced the sensitivity of Ramsey sequences to differential AC Stark shifts by about two orders of magnitude, relative to standard Raman transitions. Raman ARP also preserved fringe contrast despite substantial intensity inhomogeneity. The fractional frequency uncertainty of the ARP Ramsey sequence was limited by second-order Zeeman shifts to ~3.5 x 10-¹² after about 2500 s of averaging. For accelerometry applications, Raman ARP provided efficient, large momentum transfer (LMT) atom optics in an acceleration-sensitive LPAI. These atom optics produced momentum splittings of up to 30 photon recoil momenta between interfering wavepackets-the largest to date for Raman atom optics. This splitting, in principle, enables up to a factor-of-15 improvement in sensitivity over the nominal interferometer. By forgoing cooling methods that reduce atom number, this LMT method reduces the measurement uncertainty due to atom shot-noise and enables large area atom interferometry at higher data-rates. These features could prove useful for fielded inertial sensors based on atom interferometry.
by Krish Kotru.
Ph. D.
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Libri sul tema "Accelerometry"

1

Shephard, Roy J., e Catrine Tudor-Locke, a cura di. The Objective Monitoring of Physical Activity: Contributions of Accelerometry to Epidemiology, Exercise Science and Rehabilitation. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29577-0.

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Dauderstädt, Ulrike Anna. A thermal accelerometer. Delft: Delft University Press, 1999.

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Olcott, Joanne E. Fiber-optic flexural disk accelerometer. Monterey, Calif: Naval Postgraduate School, 1991.

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Levinzon, Felix. Piezoelectric Accelerometers with Integral Electronics. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08078-9.

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Varum, Humberto, e Sérgio de Brito André. Accelerometers: Principles, structure and applications. Hauppauge, New York: Nova Science Publishers, Inc., 2011.

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B, Rogers Melissa J., e United States. National Aeronautics and Space Administration., a cura di. Accelerometer data analysis and presentation techniques. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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A, Rogers John, e Geological Survey (U.S.), a cura di. Relative performance of several inexpensive accelerometers. [Reston, Va.]: U.S. Dept. of the Interior, U.S. Geological Survey, 1995.

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C, Blanchard Robert, Larman K. T e Langley Research Center, a cura di. Improved HIRAP flight calibration technique. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

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Xu, Yong Ping. MEMS Silicon Oscillating Accelerometers and Readout Circuits. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338826.

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John, Lekki, e NASA Glenn Research Center, a cura di. A self-diagnostic system for the M6 accelerometer. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Capitoli di libri sul tema "Accelerometry"

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Abrams, David B., J. Rick Turner, Linda C. Baumann, Alyssa Karel, Susan E. Collins, Katie Witkiewitz, Terry Fulmer et al. "Accelerometry". In Encyclopedia of Behavioral Medicine, 12. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_100008.

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Schwintzer, Peter, Z. Kang e F. Perosanz. "Accelerometry Aboard CHAMP". In International Association of Geodesy Symposia, 197–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59745-9_39.

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Connes, Pierre. "Absolute Astronomical Accelerometry". In Seismology of the Sun and the Distant Stars, 403–4. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4608-8_43.

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Connes, Pierre. "Development of Absolute Accelerometry". In Planetary Systems: Formation, Evolution, and Detection, 357–67. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1154-6_37.

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Innerd, Paul. "The technology of accelerometry". In Physical Activity Assessment, 141–57. New York : Routledge, 2020.: Routledge, 2019. http://dx.doi.org/10.4324/9781315163260-8.

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Sanso, Fernando, A. Albertella, G. Bianco, A. Della Torre, M. Fermi, V. Iafolla, A. Lenti, F. Migliaccio, A. Milani e A. Rossi. "SAGE: An Italian Project of Satellite Accelerometry". In International Association of Geodesy Symposia, 193–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59745-9_38.

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Veltink, P. H., e H. B. K. Boom. "3D Movement Analysis Using Accelerometry — Theoretical Concepts". In Neuroprosthetics: from Basic Research to Clinical Applications, 317–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-80211-9_39.

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Amor, J. D., e C. J. James. "Personalized Ambient Monitoring: Accelerometry for Activity Level Classification". In IFMBE Proceedings, 866–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89208-3_207.

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Aminian, Kamiar, Eduardo De Andres, Karen Rezakhanlou, Carlo Fritsch, Y. Schutz, Michèle Depairon, Pierre-François Leyvraz e Philippe Robert. "Motion Analysis in Clinical Practice Using Ambulatory Accelerometry". In Modelling and Motion Capture Techniques for Virtual Environments, 1–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-49384-0_1.

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Martinikorena, Ion, Alicia Martínez-Ramírez, Pablo Lecumberri, Nora Millor, Marisol Gómez e Mikel Izquierdo. "Frailty Assessment Based on Trunk Accelerometry during Walking". In Biosystems & Biorobotics, 537–42. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08072-7_79.

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Atti di convegni sul tema "Accelerometry"

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Agrawal, Aman R., Mitul Dey Chowdhury, Christian M. Pluchar e Dalziel Wilson. "Membrane-based optomechanical accelerometry". In CLEO: Applications and Technology. Washington, D.C.: OSA, 2021. http://dx.doi.org/10.1364/cleo_at.2021.jtu2i.3.

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Zhu, Ruoxi, Zifan Zhou, Jason Bonacum e Selim Shahriar. "Slow Light Enhanced Accelerometry". In Frontiers in Optics. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/fio.2022.jtu4b.8.

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We show how to realize a slow-light enhanced accelerometer using a Mach-Zehnder Interferometer. A proof-of-principle experiment carried out with Rb vapor is found to produce results in close agreement with the theoretical model.
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Zahnd, Etienne, Faezeh Movahedi, James L. Coyle, Ervin Sejdić e Prahlad G. Menon. "Correlating Tri-Accelerometer Swallowing Vibrations and Hyoid Bone Movement in Patients With Dysphagia". In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-66133.

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Swallowing accelerometry has been recently investigated as a potential non-invasive tool for dysphagia screening. This method is based on the translation of vibrations recorded from the upper aerodigestive tract structure during swallowing into a voltage signal. Some studies hypothesize the hyoid bone movement during swallowing as the source of swallow vibrations, as it is an essential component of swallowing function that contributes to protection of the airway during the swallow. However, there is still an open question about the physiological source of swallowing vibrations. In this paper, we investigate the correlation between the swallowing vibrations recorded by the tri-axial accelerometer and hyoid bone kinetics observed in video-fluoroscopic swallow imaging studies. Further, this is a first of its kind study investigating this correlation with vibration signals measured in the medial-lateral plane of accelerometry. Our hypothesis is that there exists a correlation between the recorded swallowing vibrations in three axes and hyoid bone kinetics in videofluoroscopic images.
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Garnotel, M., C. Simon e S. Bonnet. "Physical activity estimation from accelerometry". In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8856957.

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Xu, Min, Albert Goldfain, Atanu Roy Chowdhury e Jim DelloStritto. "Towards accelerometry based static posture identification". In 2011 IEEE Consumer Communications and Networking Conference (CCNC). IEEE, 2011. http://dx.doi.org/10.1109/ccnc.2011.5766477.

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"MECHANOMYOGRAPHIC SENSOR - A Triaxial Accelerometry Approach". In International Conference on Biomedical Electronics and Devices. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001054601760179.

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Vasilyev, Vladimir, Vasilii Borisov e Alexey Syskov. "Accelerometry for Human Activity Recognition: an Overview". In 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB). IEEE, 2021. http://dx.doi.org/10.1109/csgb53040.2021.9496042.

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Kusmakar, Shitanshu, Chandan K. Karmakar, Bernard Yan, Terence J. O'Brien, Ramanathan Muthuganapathy e Marimuthu Palaniswami. "Onset Detection of Epileptic Seizures From Accelerometry Signal". In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8513669.

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Pandia, Keya, Sourabh Ravindran, Gregory T. A. Kovacs, Laurent Giovangrandi e Randy Cole. "Chest-accelerometry for hemodynamic trending during valsalva-recovery". In 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010). IEEE, 2010. http://dx.doi.org/10.1109/isabel.2010.5702877.

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Russell, James, Dana Zive e Mohamud Daya. "Effect of Chest Compression Leaning on Accelerometry Waveforms". In 2016 Computing in Cardiology Conference. Computing in Cardiology, 2016. http://dx.doi.org/10.22489/cinc.2016.295-322.

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Rapporti di organizzazioni sul tema "Accelerometry"

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Siebert, Christopher. Heart Rate and Accelerometry during Singles Footbag Net Play. Portland State University Library, gennaio 2000. http://dx.doi.org/10.15760/etd.650.

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Butler, Michelle A., Brandon K. Doan, Michael Hanna, Gina A. Adam, Al Wile, Brian Self, Kristin J. Heaton, Teresa Brininger e Elizabeth Kryskow. An Investigation of Head Accelerometry, Cognitive Function, and Brain Blood Flow During Intercollegiate Boxing and its Impact Regarding Head Injury Assessment In Combat. Fort Belvoir, VA: Defense Technical Information Center, settembre 2010. http://dx.doi.org/10.21236/ada564443.

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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor e Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), dicembre 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.
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Warne, Larry Kevin, Carrie Frances Schmidt, Kenneth Allen Peterson, Stanley H. Kravitz, Rosemarie A. Renn, Frank J. Peter, Ragon D. Kinney e Jeffrey C. Gilkey. Levitated micro-accelerometer. Office of Scientific and Technical Information (OSTI), giugno 2004. http://dx.doi.org/10.2172/919151.

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Koehler, D. R., S. H. Kravitz e P. T. Vianco. Ultraminiature resonator accelerometer. Office of Scientific and Technical Information (OSTI), aprile 1996. http://dx.doi.org/10.2172/231652.

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Porterfield, Malcolm Kenneth. Accelerometer Drift Study. Office of Scientific and Technical Information (OSTI), febbraio 2020. http://dx.doi.org/10.2172/1601376.

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Balls, J. D. Neurological Diagnostic Accelerometer. Office of Scientific and Technical Information (OSTI), maggio 2000. http://dx.doi.org/10.2172/755833.

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Ammerman, D. J., M. M. Madsen, W. L. Uncapher, D. R. Stenberg e D. R. Bronowski. Accelerometer and strain gage evaluation. Office of Scientific and Technical Information (OSTI), giugno 1991. http://dx.doi.org/10.2172/5213009.

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MATERIALS SYSTEMS INC LITTLETON MA. 1-3 Composite Accelerometer Array. Fort Belvoir, VA: Defense Technical Information Center, settembre 1994. http://dx.doi.org/10.21236/ada299622.

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Soh, Daniel, Jongmin Lee e Peter Schwindt. Modeling of Atom Interferometer Accelerometer. Office of Scientific and Technical Information (OSTI), settembre 2020. http://dx.doi.org/10.2172/1670252.

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