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Furthermore, the scattering of CoM estimations based on XSENS Fig. Bland—Altman plots of the CoM estimations against the CoP readings for subjects belonging to the Obese group. Furthermore, the scattering of the data points in Fig. Given the importance of having a reliable CoM estimation technique that can overcome the limitations of the regularly used methods, whether it is being restricted by insufficient space as in the case of using a force platform, or being vulnerable to different body types and weights like the segmental method, SESC is theoretically a strong candidate for CoM estimation.
In this study, we conduct an evaluation of the SESC method, in comparison to the segmental analysis method, covering their accuracy, precision, and the ability to maintain a consistent estimation when applied to subjects with significant body differences via evaluating their estimations for Fit and Obese subjects.
Considering the SESC RMSE results, the values of the mean errors shown in Tables 1 and 2 denote a good CoM estimation error for SESC when applied to both Fit and Obese groups. The mean RMSE value along the AP-axis was On the other hand, the mean RMSE value along the ML-axis was higher by 6.
By comparing our SESC RMSE values for both groups to the results presented by González et al. According to that study, the average SESC-CoM RMSE values ranged from Regarding the linear correlation between the estimated CoM and the CoP measurements, Table 3 shows that there exists a strong correlation between the SESC estimations and ground truth CoP for both groups in both AP and ML axes.
On the other hand, the XSENS estimations show a strong correlation along the AP-axis, and a moderate correlation along the ML-axis.
As a result of the strong correlation between the estimation techniques and the ground truth, we conduct a deeper evaluation of those techniques in terms of accuracy and precision. Using the Bland—Altman method, we further evaluated the agreement between the estimations of each method with the ground truth in both AP and ML axes.
According to the plots shown in Fig. Furthermore, both plots Fig. In addition, these plots also show that the measurements are spread close to each other and the LoA are close to the bias line, thus implying that this technique is precise as well On the other hand, Fig. According to Table 4 , the fixed biases Regarding the Bland-Altman analysis of the CoM estimations for the Obese group, the SESC estimations Fig.
Hence, the general evaluation of the SESC estimations strongly denotes that this method can consistently provide an accurate and precise CoM estimation for humans with different body mass distributions. Nevertheless, we should point out that a slight trend was detected in the plot shown in Fig.
This requires further investigations with a larger number of subjects and more variety in BMI especially for the Obese group. As for the segmental method, similar to its results on the Fit group, it still showed an absence of agreement between its estimations and the ground truth values Fig. Although the findings of this study give a valuable evaluation of SESC, several limitations should be taken into consideration.
First, in this study, we only investigated the CoM estimation along the AP and ML axes without referring to the vertical CoM estimation, as there is still no gold standard method available in the literature, agreed upon by the researchers, that can be used as reference.
Additionally, although the number of subjects used in this study was reasonably acceptable, an increase in the number of participants will give a more robust interpretation of the results.
Lastly, our future studies will focus on evaluating the CoM estimation using both SESC and the segmental analysis method over several time periods in order to investigate their test re-test reliability.
As for the segmental method, adopted by XSENS software, the results showed significantly larger errors than SESC and a lack of accuracy and precision in CoM estimation for both groups along the AP and ML axes.
Further investigations could be carried out to deal with the biases, and offer a better calibration equation to fix the estimation errors. The authors declare that the data supporting the findings of this study are available upon reasonable request from the corresponding author and in accordance with applicable regulations and data usage agreements.
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Kumar, D. BMI is a measurement of a person's leanness or corpulence based on their height and weight, and is intended to quantify tissue mass. It is widely used as a general indicator of whether a person has a healthy body weight for their height. Specifically, the value obtained from the calculation of BMI is used to categorize whether a person is underweight, normal weight, overweight, or obese depending on what range the value falls between.
These ranges of BMI vary based on factors such as region and age, and are sometimes further divided into subcategories such as severely underweight or very severely obese. Being overweight or underweight can have significant health effects, so while BMI is an imperfect measure of healthy body weight, it is a useful indicator of whether any additional testing or action is required.
Refer to the table below to see the different categories based on BMI that are used by the calculator. This is the World Health Organization's WHO recommended body weight based on BMI values for adults. It is used for both men and women, age 20 or older.
This is a graph of BMI categories based on the World Health Organization data. The dashed lines represent subdivisions within a major categorization. The Centers for Disease Control and Prevention CDC recommends BMI categorization for children and teens between age 2 and Being overweight increases the risk of a number of serious diseases and health conditions.
Below is a list of said risks, according to the Centers for Disease Control and Prevention CDC :. As can be seen from the list above, there are numerous negative, in some cases fatal, outcomes that may result from being overweight.
In some cases, being underweight can be a sign of some underlying condition or disease such as anorexia nervosa, which has its own risks.
Consult your doctor if you think you or someone you know is underweight, particularly if the reason for being underweight does not seem obvious. Although BMI is a widely used and useful indicator of healthy body weight, it does have its limitations. Waist-to-hip circumference ratio has also been used, but has been found to be no better than waist circumference alone, and more complicated to measure.
A related indicator is waist circumference divided by height. The values indicating increased risk are: greater than 0. The Surface-based Body Shape Index SBSI is far more rigorous and is based upon four key measurements: the body surface area BSA , vertical trunk circumference VTC , waist circumference WC and height H.
Data on 11, subjects from the National Health and Human Nutrition Examination Surveys NHANES —, showed that SBSI outperformed BMI, waist circumference, and A Body Shape Index ABSI , an alternative to BMI. Within some medical contexts, such as familial amyloid polyneuropathy , serum albumin is factored in to produce a modified body mass index mBMI.
The mBMI can be obtained by multiplying the BMI by serum albumin , in grams per litre. Contents move to sidebar hide. Article Talk. Read Edit View history. Tools Tools. What links here Related changes Upload file Special pages Permanent link Page information Cite this page Get shortened URL Download QR code Wikidata item.
Download as PDF Printable version. In other projects. Wikimedia Commons. This is the latest accepted revision , reviewed on 24 January Relative weight based on mass and height. Chart showing body mass index BMI for a range of heights and weights in both metric and imperial.
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One century ago Harris and Body mass estimation published a short report Body mass estimation examining the relations between body estkmation, body estimatikn, age, ,ass basal etimation rate. At the time, basal metabolic Body mass estimation was masx Body mass estimation measurement in diagnosing diseases such as Body composition ratios. Their conclusions and basal metabolic rate prediction formulas still resonate today. Using the Harris-Benedict approach as a template, we systematically examined the relations between body size, body shape, age, and skeletal muscle mass SMthe main anatomic feature of sarcopenia. The sample consisted of 12, non-Hispanic NH white and NH black participants in the US National Health and Nutrition Survey who had complete weight, height, waist circumference, age, and dual-energy X-ray DXA absorptiometry data. A conversion formula was used to derive SM from DXA-measured appendicular lean soft tissue mass. The Body Fat Calculator can be Gluten-free performance foods to estimate your total body fat estimattion on specific measurements. Use the eestimation Units" Body mass estimation if you Body mass estimation esti,ation comfortable with the International System of Units SI. This calculation is based on the U. Navy methodbut also includes the calculation of body fat percentage using the BMI method both of which are outlined below. Related BMI Calculator Calorie Calculator Ideal Weight Calculator. The scientific term for body fat is "adipose tissue.
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