Projektakademie Medizintechnik: Biosignal based prediction of joint movements for mechatronic support systems with adaptive multi domain models

项目学院医疗技术：基于生物信号的机电支持系统关节运动预测，具有自适应多域模型

• 批准号: 345871852
• 负责人: Professor Dr. Axel Schneider
• 金额: --
• 依托单位: Fachbereich Ingenieurwissenschaften und Mathematik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2017-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/345871852?language=en
• 关键词: Projektakademie; Medizintechnik; Biosignal; based; prediction

The proposed project is supposed to optimize the precision of joint-muscle-models by expanding the neurobionic and biomechanic model topology to reduce the phase-shift between the movement of the biological and technical system during operation of body support devices (e.g. orthosis). Especially during very slow (up to complete stop) and very fast movements, current models fail. This can be improved by the integration of tribological models (bristle, stiction, friction, hysteresis-effects etc.). Additionally, black-box approaches will be applied to identify yet unknown biological aspects and represent those as partial models. The developed models are mainly driven by emg-signals and will be fused with additional signal sources (mechanical pressure between device and biological system etc.). Learning and optimization methods are used to adapt the models online.

拟议的项目旨在通过扩展神经仿生和生物力学模型拓扑来优化关节肌肉模型的精度，以减少身体支撑装置（例如矫形器）操作期间生物系统和技术系统运动之间的相移。特别是在非常慢（直至完全停止）和非常快的运动期间，当前模型会失败。这可以通过摩擦学模型（鬃毛、静摩擦、摩擦、滞后效应等）的集成来改进。此外，黑盒方法将用于识别未知的生物学方面，并将其表示为部分模型。开发的模型主要由肌电信号驱动，并将与其他信号源（设备和生物系统之间的机械压力等）融合。使用学习和优化方法来在线调整模型。