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ASTM F3794-25

Standard Practice for Multivariate Fit and Accommodation for Exoskeleton Manufacturers and Designers
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ASTM F3794-25

Standard Practice for Multivariate Fit and Accommodation for Exoskeleton Manufacturers and Designers

PUBLISH DATE 2025
PAGES 6
ASTM F3794-25

1.1 The design and engineering of an exoskeleton is expensive. Therefore, designs typically fit more than one person; think one-size-fits-most or an exoskeleton provided in multiple sizes (for example, small, medium, and large).

However, to accurately fit and accommodate a specific design range of anthropometries, the exoskeleton manufacturer leverages adjustment points or sizes, or both, on the exoskeleton. This practice expands the analysis from univariate analysis (as discussed in Guide F3661 – 24) to multivariate analysis.

The advantage of multivariate analysis is that manufacturers and designers can improve exoskeleton fit by comprehensively integrating multiple dimensions rather than just one dimension in isolation, to determine multiple adjustment points on the exoskeleton. Multivariate analysis allows the exoskeleton to accommodate a specific design range of anthropometries in a mixed sex population.

1.2 Units—The values stated in SI units are to be regarded as standard. The values given in parentheses after SI units are provided for information only and are not considered standard.

1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.

1.4 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

5.1 The U.S. exoskeleton marketplace currently lacks a consistent sizing structure and clear criteria for selecting sizes. This inconsistency likely stems from the absence of comprehensive national anthropometric data tailored to exoskeleton design applications.

The significant variations in sizing structures among different exoskeleton manufacturers, coupled with considerable disparities in torso dimensions across users, create challenges in selecting and fitting exoskeletons appropriately. Additionally, the absence of a national standard or guideline for exoskeleton sizing and the adjustment range of components further complicates the issue.

Exoskeleton sizing is a multifaceted matter, involving a combination of various body dimensions, and thus requires a multivariate approach.

5.1.1 In many equipment design applications, multiple parameters are used because various body measurements are relevant to the function of the products (1).4 The greater the number of dimensions involved, the more complex the product design process becomes. For upper-extremity exoskeleton development, key dimensions may include shoulder-elbow length, upper arm circumference, back breadth, chest girth, front lateral length, waist breadth, and other relevant measurements, all of which require a comprehensive assessment.

The population to consider may be the general public or workers from a specific sector for which the exoskeletons are designed. The percentage of the population to be accommodated might be 95 % or 90 % of the target group. This practice focuses on demonstrating the process of selecting the essential dimensions and applying multivariate anthropometric procedures for exoskeleton fit and accommodation.

5.2 Not every element of this practice may be applicable to all exoskeletons, nor are the recommendations in this practice intended to be prescriptive (that is, manufacturers may already provide a variable level of adjustability inherent in the exoskeleton design, which may be described in their guidance). Additionally, this practice does not address musculoskeletal sex differences in human anatomy. The tools and methodology leveraged in this practice to perform a multivariate fit necessitates that the practitioner has a foundation in anthropometry and statistical analysis.

5.3 Comfort and Discomfort:

5.3.1 Test Method F3585 – 24 provides a method to measure an exoskeleton’s cognitive fit, perceived safety, and acceptance. From a comfort and discomfort viewpoint, the criteria delineated within Test Method F3585 affords a subjective way to test that the exoskeleton and user are in accord from an individual perspective.

Leveraging the ordinal data from the Likert Scale questionnaire in Test Method F3585 – 24 , in conjunction with this practice, will provide a more meaningful way to address comfort and discomfort beyond the individual with an eye on the population writ large.

5.4 Exoskeleton Multivariate Procedures—Two common techniques in multivariate analysis are principal components analysis (PCA) and cluster analysis (CA). PCA is a dimensionality reduction tool that takes large datasets and reduces them into smaller datasets while preserving variance. CA on the other hand, groups similar observations into clusters based on observed values from several variables of each individual.

CA is not the same as classification analysis where the number of groups is known and the need is to reassign observations into a distinct group. The distinction about CA is that the number of groups and observations are unknown before starting the grouping process. Furthermore, the groups are determined by the similarity of each observation, clustering those with similar features together.

PCA and CA are complementary to each other and both are widely used in medicine, artificial intelligence/machine learning, and engineering. PCA is often applied before CA to reduce noise and enhance effectiveness, while CA can then group observations within the reduced space for clearer separation. PCA and CA will be described in general terms in 5.5 and 5.6. For detailed instructions on how to conduct these analyses, refer to Refs. (2, 3).

5.4.1 Identifying Relevant Body Parameter Dimensions—Exoskeleton manufacturers and designers often select body parameters relevant to back and shoulder exoskeleton design, based on the adjustable components of the exoskeleton, such as “upper body length to adjust the length of the fasteners on back exoskeleton models” or “upper arm circumference for the attachment of shoulder exoskeletons” (4).

Other parameters, such as shoulder breadth and hip breadth, may also be considered in the design process or product configuration. For example, a study by the Federal Institute of Occupational Safety and Health in Germany used seven parameters for shoulder exoskeletons (shoulder breadth, chest breadth, upper arm circumference, shoulder-elbow length, hip breadth, upper body length, and chest depth) and six parameters for back exoskeletons (shoulder breadth, high waist circumference, hip breadth, thigh length, thigh circumference, and shoulder height) (4).

In more complex equipment or workspace design applications, such as farm tractor cab control compartments, multiple parameters (sometimes more than ten dimensions) are often considered (5). To minimize potential redundancy (such as multiple variables contributing to an aggregate variable), bivariate correlations between measurements can be analyzed to refine the list of parameters.

For example, a high correlation (r = 0.9 or above) may suggest eliminating one of the two correlated parameters, particularly the one more prone to measurement errors (5). The principle is applicable to exoskeleton design applications where a significant number of body dimensions are relevant to the exoskeleton configuration or adjustment.

Moreover, in protective equipment design, including exoskeletons, comfort and fit are crucial factors influencing usability and user acceptance. Correlating body measurements with fit and comfort ratings via surveys can help objectively identify the key parameters for effective multivariate analyses (6).

5.5 Principal Component Analysis (PCA) for Developing Anthropometric Body Models for Exoskeleton Accommodation Testing—The ultimate goal of principal component analysis (PCA) is to use a small number of principal components to explain the anthropometric variations within a product user population.

The measurements relevant to exoskeleton accommodation can first be stratified into male and female categories, then standardized with respect to their weighted mean and standard deviation. PCA can then be applied separately to these standardized values using statistical software such as Statistical Analysis System (SAS),5 R, or Statistica.6 This process can reduce the overall dimensions to two or three principal components (PCs) that define body models.

5.5.1 The number of principal components to retain can be determined using a scree plot, where eigenvalues for the PCs are greater than one (Ref. (7)). These PCs would be orthogonal to each other and can be described as approximating a circle for two PCs or a spheroid for three PCs, with enclosed data points that account for a desired percentage (such as 95 % or 90 %) of the anthropometric variance of exoskeleton users.

5.5.2 For example, with three principal components, the transformed data in eigen-coordinates can be described as approximating a spheroid. Each of the three principal components is a method to score the most relevant variable based on measurements of the original dataset in a single plot.

The Bonferroni method can be applied with a radius value (r) as the 95 % data enclosure criterion, achieving a 95 % confidence level for each group’s sex (
SDO ASTM: ASTM International
Document Number F3794
Publication Date Nov. 1, 2025
Language en - English
Page Count 6
Revision Level 25
Supercedes
Committee F48.02

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