Hi! My name is Annalisa Baronetto,
I am a researcher at the Chair of Digital Health.
Welcome to our presentation on
“Simulation of Garment-Embedded
Contact Sensor Performance under Motion Dynamics”.
Smart garments can support long-term
health monitoring in everyday life.
Contact sensors, like electrodes and microphones,
highly depend on correct positioning and garment fitting.
In this work, we present a novel simulation method
to estimate contact sensor performance during different motion sequences,
that may occur during Activities of Daily Living.
So far, the analysis has only been conducted in static body positions.
Our method comprises four steps:
3D human body model generation, automated smart garment design,
simulation of Activities of Daily Living
and dynamic sensor fitting and displacement evaluation.
The open-source SMPL model was used to generate rigged human body models.
SMPL is a human model whose body pose and shape was learned
from 3D real human scans, including the CAESAR dataset.
In total, we generated a dataset of 100 models of different body shapes and sex.
The obtained 3D human models were animated with motion capture data
from the AMASS and CAPE datasets.
6 motion sequences, including ADLs like walking and drinking, were simulated in our analysis.
The model TailorNet was used to dress the 3D human models during the simulation.
TailorNet is a cloth model reconstructed from images and 3D scans,
whose fabric deformation was learned from physical-based simulations.
The model provides different garment designs, including 24 different T-Shirts.
From TailorNet, we selected three different T-Shirt styles with different fitting.
All the used T-Shirts were made of stretchable fabric.
To evaluate sensor performance, we considered an ECG measurement application
and defined electrode locations on the cloth models according to the 12-lead ECG standard.
Body-sensing errors can be influenced by two factors:
1- The body-garment distance may vary during motion,
the garment may detach from the body and sensors may no longer be in contact
2- The garment may slide on the body surface due to body segments motion,
and the sensors may be displaced from the intended measurement positions.
We evaluated the garment distance and displacement for each cloth vertex.
Before the evaluation, a calibration was done to find
the point-to-point correspondence between the garment and the body models.
The results show that distance errors decrease with larger BMI for both sexes.
The used cloth model is indeed unisex and is not fully personalised to fit the different body shapes.
Sensors below the breast show higher distance errors,
mostly because of the larger body shape variability.
Males show higher displacement error variance than females.
Electrodes below the neck are on average less misaligned than the other sensors,
especially the electrodes on the lower abdomen.
The higher displacement could have been caused by arm lifting movements.
Overall the sensors below the neck showed the smallest distance and displacement,
whereas the sensors below the breast showed the highest distance values
and the sensors on the abdomen the highest displacement values.
Household works and lifting a box show the largest distance ranges,
probably because of the higher complexity of the movements.
Female models present smaller sensor displacement than males.
The female different body shape could have decreased a possible displacement of the garment.
We compared the results of our simulation with ECG shirt measurements found in literature.
According to previous studies, the optimal electrode position is below the breast.
In our analysis, although sensors below the neck and next to the sternum
show slightly lower displacement, they tend to lose contact
with the skin more easily than the sensors below the breast.
The results are therefore in agreement with literature.
In conclusion, we presented a novel method for simulation
and performance analysis of contact sensors embedded in smart garments.
We were able to show the performance changes
as a function of body shape, garment fit and movement type.
In further work we plan to extend the existing method to include fabric properties
and garment cuts as well as other sensor modalities,
such as orientation and strain sensors.
For more information on our work, check our conference paper or contact me.
Thank you for your attention, I wish you a great time at this year’s conference.