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Characterization of Reverberation Chamber
Published in Kamal Kumar Sharma, Akhil Gupta, Bandana Sharma, Suman Lata Tripathi, Intelligent Communication and Automation Systems, 2021
Abhishek Kadri, Devendra Chandra Pande, Abhilasha Mishra
In semi-reverberant environments such as passenger transit vehicles like planes, trains, buses and cars, elevators, factory halls, cargo-container workplaces, etc. people present the most common loading that influences the electromagnetic field (EMF) characteristics within observed volume. Damir Senic studied the human body absorption characteristics in terms of absorption effectiveness and absorption cross section (ACS) in a RC [11]. The ACS has been further used to calculate the body mass index (BMI), body surface area (BSA) and body fat percentage (BFP) of human subjects [12]. Abdou Khadir Fall et al. have used the RC for conducting experimental dosimetric measurements in the 60 GHz frequency band [13].
Applications of Machine Learning in Industrial Sectors
Published in Pedro Larrañaga, David Atienza, Javier Diaz-Rozo, Alberto Ogbechie, Carlos Puerto-Santana, Concha Bielza, Industrial Applications of Machine Learning, 2019
Pedro Larrañaga, David Atienza, Javier Diaz-Rozo, Alberto Ogbechie, Carlos Puerto-Santana, Concha Bielza
Nowadays obesity researchers have access to a wealth of data. Sensor and smartphone app data, electronic medical records, large insurance databases and publicly available national health data provide input that machine learning algorithms can transform into mathematical models. DeGregory et al. (2006) show an empirical comparison of logistic regression, artificial neural networks and classification trees to predict high levels of body fat percentage from anthropometric predictor variables taken from a sample of more than 25,000 patient records extracted from the National Health and Nutrition Examination Survey (NHANES) dataset.
Implantable Electronics
Published in Aboul Ella Hassanien, Nilanjan Dey, Surekha Borra, Medical Big Data and Internet of Medical Things, 2018
Vinay Chowdary, Vivek Kaundal, Paawan Sharma, Amit Kumar Mondal
Figure 3.16 shows the relationship between body fat percentage and body mass index for various observations. Two cases were considered: one where outliers were taken into the measurement, and the other where the outliers were removed.
Alterations in kinematics and muscle activation patterns with the addition of a kipping action during a pull-up activity
Published in Sports Biomechanics, 2019
Christopher Dinunzio, Nathaniel Porter, John Van Scoy, Derrick Cordice, Ryan S. McCulloch
Height and weight were recorded using a Stadiometer Detecto beam scale and height rod. Body fat percentage (%) was measured using a bioelectrical impedance body fat analyser (HBF-306 BL, Omron; Kyoto, Japan). Kinematic data were collected with a high-speed camera-(Exilim EX-FH20, Casio; Tokyo, Japan) oriented orthogonal to the plane of motion (PoM), in-line with the pull-up bar at a distance of 5 m, so that movement in the sagittal plane could be recorded at a frequency of 210 Hz. Spherical reflective markers for 2D analysis were placed on the right side of the participant’s body: 8th rib, greater trochanter of the femur, knee (centred over the lateral epicondyle of the femur) and lateral malleolus of the fibula (Youdas et al., 2010).
Validity of equations for estimating aerobic fitness in Mexican youth
Published in Journal of Sports Sciences, 2019
César Iván Ayala-Guzmán, Luis Ortiz-Hernández
Three adiposity indicators were assessed: BMI, waist circumference and body fat percentage. Weight, height and waist circumference (midpoint between the iliac crest and last rib) were measured following standardized techniques (Lohmann, Roche, & Martorell, 1988). Prior to fieldwork, observers were trained and low measurement error of each observer was achieved using the standardization procedure described by Habicht (1974). Body fat percentage was assessed using a bioelectrical impedance analyser (InBody, Inc., model 720, CA, USA). The Z-scores for height-for-age and BMI-for-age were calculated using the World Health Organization reference (de Onis et al., 2007).
A multidimensional approach to identifying the physical qualities of male English regional academy rugby union players; considerations of position, chronological age, relative age and maturation
Published in European Journal of Sport Science, 2023
Cameron Owen, Kevin Till, Padraic Phibbs, Dale J. Read, Jonathon Weakley, Mark Atkinson, Matt Cross, Simon Kemp, Thomas Sawczuk, Keith Stokes, Sean Williams, Ben Jones
Standing and sitting height were measured to the nearest 0.1 cm using a portable stadiometer (Seca 213, Hamburg, Germany). Body mass was collected, wearing minimal clothing (e.g. shorts and t-shirt) using calibrated analogue scales (Seca, Hamburg, Germany) to the nearest 0.1 kg. Bioelectrical impedance analysis (Tanita BF-350, Tokyo, Japan) was used to quantify body fat percentage.