A Parametric Model of Shoulder Articulation for Virtual Assessment of Space Suit Fit

Kim, K. Han; Young, Karen; Bernal, Yaritza; Boppana, Abhishektha; Vu, Linh; Benson, Elizabeth; Jarvis, Sarah; Rajulu, Sudhakar

doi:10.15221/16.201

Cited by 3 publications

(6 citation statements)

References 9 publications

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“…For context, if all possible prediction error was accumulated to only affect length and width, it would be higher than the half-size step of the American shoe sizing system (Luximon and Luximon, 2013), but less than inter-brand variability of shoe length and shoe width (Wannop et al, 2019). Further, this error is lower than the RMSEs of other parametric SSMs that predicted static standing child body shape (mean=10.4mm) , dynamic shoulder deformation (mean=11.98mm) (Kim et al, 2016) and child torso shape (mean=9.5mm) (Park et al, 2017). Note though, that the presented model may have lower prediction errors due to the foot being a relatively smaller section of the body to model.…”

Section: Discussionmentioning

confidence: 92%

“…First scans were roughly aligned using a point-to-plane iterative-closest-point algorithm (Chen and Medioni, 1992), implemented in Open3D . Next, the radial-basis function fitting algorithm from the GIAS2 software package was run twice using a thin-plate spline to approximate the foot surface Kim et al, 2016). The mid-stance scan from each subject was registered first to the template, and then the registration process was run both forwards towards toe-off and backwards towards heel-strike, on a scan-by-scan basis, using the previously registered scan as a template for the next scan.…”

Section: Data Processingmentioning

confidence: 99%

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Dynamic foot morphology explained through 4D scanning and shape modeling

Boppana¹,

Anderson²

2020

Preprint

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A detailed understanding of foot morphology can enable the design of more comfortable and better fitting footwear. However, foot morphology varies widely within the population, and changes dynamically during the loading of stance phase. This study presents a parametric statistical shape model from 4D foot scans to capture both the inter-and intra-individual variability in foot morphology. Thirty subjects walked on a treadmill while 4D scans of their right foot were taken at 90 frames-per-second during stance phase. Each subject's height, weight, foot length, foot width, arch length, and sex were also recorded. The 4D scans were all registered to a common high-quality foot scan, and a principal component analysis was done on all processed 4D scans. Elastic-net linear regression models were built to predict the principal component scores, which were then inverse transformed into 4D scans. The best performing model was selected with leave-one-out cross-validation. The chosen model was predicts foot morphology across stance phase with a root-mean squared error of 5.2 ± 2.0 mm. This study shows that statistical shape modeling can be used to predict dynamic changes in foot morphology across the population. The model can be used to investigate and improve foot-footwear interaction, allowing for better fitting and more comfortable footwear.

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Section: Discussionmentioning

confidence: 92%

Section: Data Processingmentioning

confidence: 99%

Dynamic foot morphology explained through 4D scanning and shape modeling

Boppana¹,

Anderson²

2020

Preprint

Self Cite

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show abstract

“…Efforts have been made for developing morphologic and kinematic human and spacesuit models to predictively assess spacesuit fit and mobility (Kim et al, 2016(Kim et al, , 2019Davis et al, 2020). This framework for virtual testing of spacesuit fit is based on 3D body scanning and computational modeling techniques.…”

Section: D Virtual Fit Assessment and Modeling 301mentioning

confidence: 99%

“…Efforts have been made for developing morphologic and kinematic human and spacesuit models to predictively assess spacesuit fit and mobility (Kim et al. , 2016, 2019; Davis et al.…”

Section: Introductionmentioning

confidence: 99%

3D virtual fit assessment and modeling: liquid cooling and ventilation garment

Weiss¹,

Hernandez

Kim

et al. 2021

IJCST

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PurposeThe suboptimal fit of a spacesuit can interfere with a crewmember's performance and is regarded as a potential risk factor for injury. To quantify suit fit, a virtual fit assessment model was previously developed to identify suit-to-body contact and interference using 3D human body scans and suit CAD models. However, ancillary suit components and garments worn inside of the suit have not been incorporated.Design/methodology/approachThis study was conducted to predict a 3D model of the liquid cooling and ventilation garment (LCVG) from an arbitrary person's body scan. A total of 14 subjects were scanned in a scan wear and LCVG condition. A statistical model was generated using principal component analysis and random forest regression technique.FindingsThe model was able to predict the geometry of the LCVG layer at the accuracy of 5.3 cm maximum error and 1.7 cm root mean square error. The errors were more pronounced for the arms and lower torso, while the thighs and upper torso regions, which are critical for suit fit assessments, show more accurate predictions. A case study of suit fit with and without the LCVG model demonstrated that the new model can enhance the scope and accuracy of future spacesuit assessments.Originality/valueThe capabilities resulting from these modeling techniques would greatly expand the assessments of fit of the garment on various anthropometries. The results from this study can significantly improve the design process modeling and initial suit sizing efforts to optimize crew performance during extravehicular activity training and missions.

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“…Las dimensiones corporales en posturas forzadas o en movimiento son importantes en el diseño e innovación de prendas que requieren un ajuste funcional óptimo, caso particular de la indumentaria deportiva (Naglic et al, 2016) (Figura 35) o los equipos de protección individual (K. H. Kim et al, 2016;Loercher et al, 2018). Los últimos avances de la mano de escáneres 4D, han permitido analizar cómo evoluciona una medida corporal con ciertos movimientos (Uriel et al, 2022).…”

Section: Antropometría Digital 3dunclassified

Desarrollo de modelos estadísticos de predicción del ajuste y talla de prendas de ropa a partir de la percepción y características antropométricas del usuario

Alemany Mut

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A Parametric Model of Shoulder Articulation for Virtual Assessment of Space Suit Fit

Cited by 3 publications

References 9 publications

Dynamic foot morphology explained through 4D scanning and shape modeling

Dynamic foot morphology explained through 4D scanning and shape modeling

3D virtual fit assessment and modeling: liquid cooling and ventilation garment

Desarrollo de modelos estadísticos de predicción del ajuste y talla de prendas de ropa a partir de la percepción y características antropométricas del usuario

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