Moving Statistical Body Shape Models Using Blender

Scataglini, Sofia; Danckaers, Femke; Haelterman, Rob; Huysmans, Toon; Sijbers, Jan

doi:10.1007/978-3-319-96077-7_4

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…Xsens requires the body placement of the wearable inertial sensors attached to the body by straps. This is after introducing the anthropometric measurements to the MVN Awinda software for representing and scaling the Digital Human Model (DHM) of the subject, where a successive calibration is required [ 60 , 61 , 62 , 63 ]. This is not practical when you need to acquire a patient in a clinical setting as you have limitation in time.…”

Section: Discussionmentioning

confidence: 99%

Measuring Spatiotemporal Parameters on Treadmill Walking Using Wearable Inertial System

Scataglini

Verwulgen

Roosens

et al. 2021

Sensors

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This study aims to measure and compare spatiotemporal gait parameters in nineteen subjects using a full wearable inertial mocap system Xsens (MVN Awinda, Netherlands) and a photoelectronic system one-meter OptoGaitTM (Microgait, Italy) on a treadmill imposing a walking speed of 5 km/h. A total of eleven steps were considered for each subject constituting a dataset of 209 samples from which spatiotemporal parameters (SPT) were calculated. The step length measurement was determined using two methods. The first one considers the calculation of step length based on the inverted pendulum model, while the second considers an anthropometric approach that correlates the stature with an anthropometric coefficient. Although the absolute agreement and consistency were found for the calculation of the stance phase, cadence and gait cycle, from our study, differences in SPT were found between the two systems. Mean square error (MSE) calculation of their speed (m/s) with respect to the imposed speed on a treadmill reveals a smaller error (MSE = 0.0008) using the OptoGaitTM. Overall, our results indicate that the accurate detection of heel strike and toe-off have an influence on phases and sub-phases for the entire acquisition. Future study in this domain should investigate how to design and integrate better products and algorithms aiming to solve the problematic issues already identified in this study without limiting the user’s need and performance in a different environment.

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Section: Discussionmentioning

confidence: 99%

Measuring Spatiotemporal Parameters on Treadmill Walking Using Wearable Inertial System

Scataglini

Verwulgen

Roosens

et al. 2021

Sensors

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“…Starting from the shape, it is possible to realize a statistical shape modeling (SSM) mapping the body shape variability of anthropometric databases of 3D body scan [ 35 , 36 ]. Using the SSM and a bio vision file (bvh) captured using a mocap system in older users, it is possible to replicate a real motion activity in SSM, creating a kinematic model.…”

Section: Methodsmentioning

confidence: 99%

“…It is possible to replicate a human activity (e.g., an older user who is wearing a garment) into a virtual environment constituting a kinematic model. Scataglini et al (2018, 2019) [ 31 , 32 , 35 ], described the workflow for realizing a kinematic model and the dressing up starting from SSM.…”

Section: Methodsmentioning

confidence: 99%

“…This can be imported as a BVH file (bio vision file) into Blender. Afterward, we use UV mapping with the body mesh and the rigging of the shirt or clothes ( Figure 3 ) with the kinematic model [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

“…Feedback derived from this design phase is fundamental for the realization and for the prototyping phases because they can substitute expensive and lengthy real prototyping realizations, whose fitting can be stressful and painful for users involved in the experimentation. DHM allows designers to save a significant amount of resources, giving a very precise and accurate level of simulation of the body of the person and guaranteeing an effective verification of ergonomic aspects involved in the project of the specific smart cloth [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

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A User Centered Methodology for the Design of Smart Apparel for Older Users

Imbesi

Scataglini

2021

Sensors

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Smart clothing plays a big role to foster innovation and to. boost health and well-being, improving the quality of the life of people, especially when addressed to niche users with particular needs related to their health. Designing smart apparel, in order to monitor physical and physiological functions in older users, is a crucial asset that user centered design is exploring, balancing needs expressed by the users with technological requirements related to the design process. In this paper, the authors describe a user centered methodology for the design of smart garments based on the evaluation of users’ acceptance of smart clothing. This comparison method can be considered as similar to a simplified version of the quality function deployment tool, and is used to evaluate the general response of each garment typology to different categories of requirements, determining the propensity of the older user to the utilization of the developed product. The suggested methodology aims at introducing in the design process a tool to evaluate and compare developed solutions, reducing complexity in design processes by providing a tool for the comparison of significant solutions, correlating quantitative and qualitative factors.

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