De Rossi Danilo, Veltink Peter
Faculty of Engineering, University of Pisa.
IEEE Eng Med Biol Mag. 2010 May-Jun;29(3):37-43. doi: 10.1109/MEMB.2010.936555.
The possibility of gathering reliable information about movement characteristics during activities of daily living holds particular appeal for researchers. Data such as this could be used to analyze the performance of individuals undergoing rehabilitation and to provide vital information on whether or not there is an improvement during a neurorehabilitation protocol. Wearable devices are particularly promising toward this aim, because they can be used in unstructured environments (e.g., at home). Recently, two different approaches in this area have become very popular and show promising performance: the use of inertial sensors together with advanced algorithms (e.g., Kalman filters) and the development of e-textile, in which the sensing technology is directly embroidered into the garment worn by the user.
收集日常生活活动中运动特征的可靠信息的可能性对研究人员具有特别的吸引力。这样的数据可用于分析正在接受康复治疗的个体的表现,并提供有关在神经康复方案期间是否有改善的重要信息。可穿戴设备在实现这一目标方面特别有前景,因为它们可以在非结构化环境(如家中)中使用。最近,该领域的两种不同方法变得非常流行且表现出良好的前景:将惯性传感器与先进算法(如卡尔曼滤波器)一起使用,以及开发电子织物,其中传感技术直接绣在用户穿着的衣服上。