Dealing with sensor displacement in motion-based onbody activity recognition systems
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Recognizing Upper Body Postures using Textile Strain Sensors
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
Textile-Based Wearable Sensors for Assisting Sports Performance
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
Wearable therapist: sensing garments for supporting children improve posture
Proceedings of the 11th international conference on Ubiquitous computing
Modeling and simulation of sensor orientation errors in garments
BodyNets '09 Proceedings of the Fourth International Conference on Body Area Networks
Rapid prototyping of smart garments for activity-aware applications
Journal of Ambient Intelligence and Smart Environments
Detecting bends and fabric folds using stitched sensors
Proceedings of the 2013 International Symposium on Wearable Computers
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A fundamental challenge limiting information quality obtained from smart sensing garments is the influence of textile movement relative to limbs.We present and validate a comprehensive modeling and simulation framework to predict recognition performance in casual loose-fitting garments. A statistical posture and wrinkle-modeling approach is introduced to simulate sensor orientation errors pertained to local garment wrinkles. A metric was derived to assess fitting, the body-garment mobility. We validated our approach by analyzing simulations of shoulder and elbow rehabilitation postures with respect to experimental data using actual casual garments. Results confirmed congruent performance trends with estimation errors below 4% for all study participants. Our approach allows to estimate the impact of fitting before implementing a garment and performing evaluation studies with it. These simulations revealed critical design parameters for garment prototyping, related to performed body posture, utilized sensing modalities, and garment fitting. We concluded that our modeling approach can substantially expedite design and development of smart garments through early-stage performance analysis.