Giorgino Toni, Tormene Paolo, Lorussi Federico, De Rossi Danilo, Quaglini Silvana
Laboratory for Biomedical Informatics, Dipartimento di Informatica e Sistemistica, University of Pavia, Pavia, Italy.
IEEE Trans Neural Syst Rehabil Eng. 2009 Aug;17(4):409-15. doi: 10.1109/TNSRE.2009.2019584. Epub 2009 Apr 10.
Conductive elastomers are a novel strain sensing technology which can be unobtrusively embedded into a garment's fabric, allowing a new type of sensorized cloths for motion analysis. A possible application for this technology is remote monitoring and control of motor rehabilitation exercises. The present work describes a sensorized shirt for upper limb posture recognition. Supervised learning techniques have been employed to compare classification models for the analysis of strains, simultaneously measured at multiple points of the shirt. The instantaneous position of the limb was classified into a finite set of predefined postures, and the movement was decomposed in an ordered sequence of discrete states. The amount of information given by the observation of each sensor during the execution of a specific exercise was quantitatively estimated by computing the information gain for each sensor, which in turn allows the data-driven optimization of the garment. Real-time feedback on exercise progress can also be provided by reconstructing the sequence of consecutive positions assumed by the limb.
导电弹性体是一种新型应变传感技术，它可以不显眼地嵌入到服装面料中，从而实现用于运动分析的新型传感衣物。该技术的一个可能应用是对运动康复锻炼进行远程监测和控制。目前的工作描述了一种用于上肢姿势识别的传感衬衫。已采用监督学习技术来比较用于分析在衬衫多个点同时测量的应变的分类模型。肢体的瞬时位置被分类为一组有限的预定义姿势，并且运动被分解为离散状态的有序序列。通过计算每个传感器的信息增益，定量估计了在执行特定锻炼期间每个传感器观测所提供的信息量，这反过来又允许对服装进行数据驱动的优化。通过重建肢体所假定的连续位置序列，还可以提供关于锻炼进展的实时反馈。
