Lai Daniel T H, Begg Rezaul K, Palaniswami Marimuthu
Biomechanics Unit, Victoria University, Melbourne, Vic. 8001, Australia.
IEEE Trans Inf Technol Biomed. 2009 Sep;13(5):687-702. doi: 10.1109/TITB.2009.2022913. Epub 2009 May 12.
Our mobility is an important daily requirement so much so that any disruption to it severely degrades our perceived quality of life. Studies in gait and human movement sciences, therefore, play a significant role in maintaining the well-being of our mobility. Current gait analysis involves numerous interdependent gait parameters that are difficult to adequately interpret due to the large volume of recorded data and lengthy assessment times in gait laboratories. A proposed solution to these problems is computational intelligence (CI), which is an emerging paradigm in biomedical engineering most notably in pathology detection and prosthesis design. The integration of CI technology in gait systems facilitates studies in disorders caused by lower limb defects, cerebral disorders, and aging effects by learning data relationships through a combination of signal processing and machine learning techniques. Learning paradigms, such as supervised learning, unsupervised learning, and fuzzy and evolutionary algorithms, provide advanced modeling capabilities for biomechanical systems that in the past have relied heavily on statistical analysis. CI offers the ability to investigate nonlinear data relationships, enhance data interpretation, design more efficient diagnostic methods, and extrapolate model functionality. These are envisioned to result in more cost-effective, efficient, and easy-to-use systems, which would address global shortages in medical personnel and rising medical costs. This paper surveys current signal processing and CI methodologies followed by gait applications ranging from normal gait studies and disorder detection to artificial gait simulation. We review recent systems focusing on the existing challenges and issues involved in making them successful. We also examine new research in sensor technologies for gait that could be combined with these intelligent systems to develop more effective healthcare solutions.
我们的行动能力是日常生活中的一项重要需求，以至于行动能力的任何中断都会严重降低我们所感知到的生活质量。因此，步态与人体运动科学的研究对于维持我们行动能力的健康状态起着重要作用。当前的步态分析涉及众多相互依存的步态参数，由于步态实验室中记录的数据量庞大且评估时间漫长，这些参数难以得到充分解读。针对这些问题，一种提议的解决方案是计算智能（CI），它是生物医学工程领域中一种新兴的范式，在病理学检测和假肢设计方面尤为显著。将CI技术集成到步态系统中，通过信号处理和机器学习技术相结合来学习数据关系，有助于研究由下肢缺陷、脑部疾病和衰老效应引起的病症。诸如监督学习、无监督学习以及模糊和进化算法等学习范式，为过去严重依赖统计分析的生物力学系统提供了先进的建模能力。CI能够研究非线性数据关系、增强数据解读、设计更高效的诊断方法以及拓展模型功能。预计这些将带来更具成本效益、高效且易于使用的系统，从而解决全球医疗人员短缺和医疗成本不断上升的问题。本文综述了当前的信号处理和CI方法，以及从正常步态研究、病症检测到人工步态模拟等步态应用。我们回顾了近期的系统，重点关注使其成功所涉及的现有挑战和问题。我们还研究了步态传感器技术的新研究，这些技术可与这些智能系统相结合，以开发更有效的医疗保健解决方案。
