Signal processing of anthropometric data

The Anthropometric Measurements Laboratory has accumulated a large body of data from a number of previous experiments. The data is very noisy, therefore it requires the application of some signal processing schemes. Moreover, it was not regarded as time series measurements but as positional information; hence, the data is stored as coordinate points as defined by the motion of the human body. The accumulated data defines two groups or classes. Some of the data was collected from an experiment designed to measure the flexibility of the limbs, referred to as radial movement. The remaining data was collected from experiments designed to determine the surface of the reach envelope. An interactive signal processing package was designed and implemented. Since the data does not include time this package does not include a time series element. Presently the results is restricted to processing data obtained from those experiments designed to measure flexibility

Novel method of capturing static and dynamic anthropometric data for home design

This paper presents a novel method for capturing and measuring both static and dynamic anthropometric data of people. These data can be then used for barrier-free home design based on a concept of ergonomic design with motion. This new approach utilized a 3D motion capture system as a tool to simultaneously obtain anthropometric information based on body motion analyses. This paper reports the experimental system design, data collection and analysis techniques on body motions

Four-year stability of anthropometric and cardio-metabolic parameters in a prospective cohort of older adults

Aim: To examine the medium-term stability of anthropometric and cardio-metabolic parameters in the general population. Materials & methods: Participants were 5160 men and women from the English Longitudinal Study of Ageing (age ≥50 years) assessed in 2004 and 2008. Anthropometric data included height, weight, BMI and waist circumference. Cardio-metabolic parameters included blood pressure, serum lipids (total cholesterol, HDL, LDL, triglycerides), hemoglobin, fasting glucose, fibrinogen and C-reactive protein. Results: Stability of anthropometric variables was high (all intraclass correlations >0.92), although mean values changed slightly (-0.01 kg weight, +1.33 cm waist). Cardio-metabolic parameters showed more variation: correlations ranged from 0.43 (glucose) to 0.81 (HDL). The majority of participants (71–97%) remained in the same grouping relative to established clinical cut-offs. Conclusion: Over a 4-year period, anthropometric and cardio-metabolic parameters showed good stability. These findings suggest that when no means to obtain more recent data exist, a one-time sample will give a reasonable approximation to average levels over the medium-term, although reliability is reduced

A literature review of the anthropometric studies of school students for ergonomics purposes: are accuracy, precision and reliability being considered?

BACKGROUND: Despite offering many benefits, direct manual anthropometric measurement method can be problematic due to their vulnerability to measurement errors. OBJECTIVE: The purpose of this literature review was to determine, whether or not the currently published anthropometric studies of school children, related to ergonomics, mentioned or evaluated the variables precision, reliability and/or accuracy in the direct manual measurement method. METHODS: Two bibliographic databases, and the bibliographic references of all the selected papers were used for finding relevant published papers in the fields considered in this study. RESULTS: Forty-six (46) studies met the criteria previously defined for this literature review. However, only ten (10) studies mentioned at least one of the analyzed variables, and none has evaluated all of them. Only reliability was assessed by three papers. Moreover, in what regards the factors that affect precision, reliability and accuracy, the reviewed papers presented large differences. This was particularly clear in the instruments used for the measurements, which were not consistent throughout the studies. Additionally, it was also clear that there was a lack of information regarding the evaluators’ training and procedures for anthropometric data collection, which are assumed to be the most important issues that affect precision, reliability and accuracy. CONCLUSIONS: Based on the results it was possible to conclude that the considered anthropometric studies had not focused their attention to the analysis of precision, reliability and accuracy of the manual measurement methods. Hence, and with the aim of avoiding measurement errors and misleading data, anthropometric studies should put more efforts and care on testing measurement error and defining the procedures used to collect anthropometric data

Malnourished and surviving in South Asia, better nourished and dying young in Africa: What can explain this puzzle?

This paper examines the factors explaining the very different relationship between anthropometric shortfall and child mortality in South Asia and Sub Saharan Africa. While in the former, very high rates of anthropometric shortfall coexist with comparatively lower child mortality rates, rates of anthropometric shortfall in Sub Saharan Africa are much lower, yet under five mortality is much higher than in South Asia. This puzzle is examined using a panel data set of undernutrition, mortality, and their correlates. The analysis suggests that the unusually high rates of anthropometric shortfall in South Asia are partially due to the use of a US¡based reference standard which appears to generate misleading international comparisons of undernutrition. The very high rates of under five mortality in Africa seem to be mostly due to very high fertility, high and rising HIV prevalence, and a possible multiplicative interaction of risk factors

The relationship between anthropometric parameters and bone mineral density in an Iranian referral population

Osteoporosis is a common health concern in both developed and developing countries. In this study the association between anthropometric measures and osteoporosis was investigated in 3630 males and females visiting BMD clinic of Shariati Hospital, Tehran, Iran, a teaching hospital and referral center for osteoporosis affiliated to the Tehran University of Medical Sciences. Anthropometric measurements obtained and also Bone Mineral Density (BMD) measurement was done using a Lunar DPXMD densitometer. Data were analyzed using SPSS with Chi-square and ANOVA with post-hoc tests. Results showed that the weight, BMI and age had the strongest correlation with the BMD values in the studied people. While age is negatively correlated with BMD in all the studied people, a positive association was noted between weight, height and BMI and BMD parameters (P<0.01). It was concluded that certain anthropometric parameters (BMI and weight) can considerably affect one's risk of developing osteoporosis. Further research on the effect of these variables on the association of weight and BMD is needed

Anthropometric discriminators of type 2 diabetes among White and Black American adults

BACKGROUND: The aim of the present study was to determine the best anthropometric discriminators of type 2 diabetes mellitus (T2DM) among White and Black males and females in a large US sample. METHODS: We used Atherosclerosis Risk in Communities study baseline data (1987–89) from 15 242 participants (1827 with T2DM) aged 45–65 years. Anthropometric measures included a body shape index (ABSI), body adiposity index (BAI), body mass index, waist circumference (WC), waist:height ratio (WHtR), and waist:hip ratio (WHR). All anthropometric measures were standardized to Z-scores. Using logistic regression, odds ratios for T2DM were adjusted for age, physical activity, and family history of T2DM. The Akaike information criterion and receiver operating characteristic C-statistic were used to select the best-fit models. RESULTS: Body mass index, WC, WHtR, and WHR were comparable discriminators of T2DM among White and Black males, and were superior to ABSI and BAI in predicting T2DM (P < 0.0001). Waist circumference, WHtR, and WHR were the best discriminators among White females, whereas WHR was the best discriminator among Black females. The ABSI was the poorest discriminator of T2DM for all race–gender groups except Black females. Anthropometric values distinguishing T2DM cases from non-cases were lower for Black than White adults. CONCLUSIONS: Anthropometric measures that included WC, either alone or relative to height (WHtR) or hip circumference (WHR), were the strongest discriminators of T2DM across race–gender groups. Body mass index was a comparable discriminator to WC, WHtR, and WHR among males, but not females

Seasonal changes in anthropometric and physical characteristics within English academy rugby league players.

Professional rugby league clubs implement training programmes for the development of anthropometric and physical characteristics within an academy programme. However, research that examines seasonal changes in these characteristics is limited. The purpose of the study was to evaluate the seasonal changes in anthropometric and physical characteristics of academy rugby league players by age category (i.e., under 14, 16, 18, 20). Data were collected on 75 players pre- and postseason over a 6-year period (resulting in a total of 195 assessments). Anthropometric (body mass, sum of 4 skinfolds) and physical (10- and 20-m sprint, vertical jump, Yo-Yo intermittent recovery test and 1 repetition maximum squat, bench press, and prone row) measures were collected. The under 14s and 16s showed greater seasonal improvements in body mass (e.g., under 14s = 7.4 ± 4.3% vs. under 20s = 1.2 ± 3.3%) and vertical jump performance than under 18s and under 20s. In contrast, under 18s and under 20s players showed greater seasonal improvements in Yo-Yo performance and 10-m sprint (e.g., under 14s = 1.3 ± 3.9% vs. under 20s = -1.9 ± 1.2%) in comparison to under 14s and under 16s. Seasonal strength improvements were greater for the under 18s compared with under 20s. This study provides comparative data for seasonal changes in anthropometric and physical characteristics within rugby league players aged 13-20 years. Coaches should be aware that seasonal improvements in speed may not exist within younger age categories, until changes in body mass stabilize and consider monitoring changes in other characteristics (e.g., momentum). Large interplayer variability suggests that player development should be considered on an individual and longitudinal basis

Anthropometrics without numbers!

(Anthropometrics without Numbers! An Investigation of Designers' Use and Preference of People Data By Nickpour F and Dong H) There is still missing knowledge to encourage and support designers in adoption and implementation of inclusive design. Some of this missing knowledge comes in the form of anthropometric data which provides accessible information on users' capabilities and limitations. Support and resources for designers on this type of data seems to be limited and exclusive. This study focuses on evaluating the existing use of anthropometric data by professional designers, aiming to explore means of presenting such data more effectively. Ten UK-based design consultancies were interviewed and completed questionnaires collecting information on designer’s current use of anthropometric data, their suggestions on presentation of that data and their preferences on data tools. It is concluded that the use of anthropometric data sources by designers is very limited and minimal; experienced designers tend to rely mainly on experimental methods such as physical prototyping and engagement with people. The results provide insights into designers' existing approaches to data collection and use. This study highlights the need for development of a highly visual, simple and intuitive data tool based on the interviewed designers’ preferences and suggestions. This has to be done by carefully adopting the designers’ existing approaches to data collection and use and by adapting existing data into that