Harms Holger, Amft Oliver, Tr Ster Gerhard
Wearable Computing Laboratory, Eidgenössische Technische Hochschule Zürich, CH-8092 Zürich, Switzerland.
IEEE Trans Inf Technol Biomed. 2010 Nov;14(6):1436-45. doi: 10.1109/TITB.2010.2076822. Epub 2010 Sep 27.
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.
限制从智能传感服装获取信息质量的一个根本挑战是纺织品相对于肢体运动的影响。我们提出并验证了一个全面的建模和仿真框架，以预测休闲宽松服装中的识别性能。引入了一种统计姿势和皱纹建模方法，以模拟与局部服装皱纹相关的传感器方向误差。推导了一个指标来评估贴合度，即身体与服装的移动性。我们通过使用实际休闲服装，针对实验数据分析肩部和肘部康复姿势的模拟，验证了我们的方法。结果证实，所有研究参与者的性能趋势一致，估计误差低于4%。我们的方法允许在制作服装并对其进行评估研究之前，估计贴合度的影响。这些模拟揭示了与执行的身体姿势、使用的传感方式和服装贴合度相关的服装原型设计关键参数。我们得出结论，我们的建模方法可以通过早期性能分析大幅加快智能服装的设计和开发。
