Institute of General Mechanics - RWTH Aachen University, Aachen, Germany.
Institute of General Mechanics - RWTH Aachen University, Aachen, Germany.
Gait Posture. 2020 Mar;77:75-82. doi: 10.1016/j.gaitpost.2020.01.021. Epub 2020 Jan 23.
Due to the high susceptivity of the walking pattern to be affected by several disorders, accurate analysis methods are necessary. Given the complexity and relevance of such assessment, the utilization of methods to facilitate it plays a significant role, provided that they do not compromise the outcomes.
This paper aimed at identifying the standards for the application of adaptive predictive systems to gait analysis, given the extensive research on this field. Furthermore, we also intended to check whether such methods can effectively support clinicians in determining the number of physiotherapy sessions necessary to recover gait-related dysfunctions.
Through a screening process of scientific databases, we considered studies encompassed from 1968 to April 2019. Within these 50 years, we found 24 papers that met our inclusion criteria. They were analyzed according to their data acquisition and processing methods via ad hoc questionnaires. Additionally, we examined quantitatively the adaptive approaches.
Concerning data acquisition, the included papers presented a mean score of 6.1 SD 1.0, most of them applying optoelectronic systems, and the ground reaction force (GRF) was the most used parameter. The AI quality assessment showed an above-average rate of 7.8 SD 1.0, and artificial neural networks (ANN) being the paradigm most frequently utilized. Our systematic review identified only one study that addressed therapeutics including a predictive method.
While much progress has been identified to predict assessment aspects, there is little effort to assist healthcare professionals in establishing the rehabilitation duration and prognostics. Therefore, future studies should focus on accomplishing the production of applications of predictive methods to therapeutics and prognosis, not lingering extremely on the analysis of gait features.
由于行走模式容易受到多种障碍的影响，因此需要准确的分析方法。鉴于这种评估的复杂性和相关性，利用方法来促进它具有重要作用，只要这些方法不会影响结果。
本文旨在确定应用自适应预测系统进行步态分析的标准，因为该领域已经进行了广泛的研究。此外，我们还想检查这些方法是否可以有效地帮助临床医生确定恢复与步态相关的功能障碍所需的物理治疗次数。
通过对科学数据库的筛选过程，我们考虑了涵盖 1968 年至 2019 年 4 月期间的研究。在这 50 年中，我们发现了 24 篇符合我们纳入标准的论文。我们根据他们的专门问卷分析了他们的数据采集和处理方法。此外，我们还对自适应方法进行了定量检查。
关于数据采集，所纳入的论文平均得分为 6.1 ± 1.0，其中大部分使用光电系统，地面反力（GRF）是最常用的参数。人工智能质量评估显示出平均以上的 7.8 ± 1.0 的评分，人工神经网络（ANN）是最常使用的范例。我们的系统综述只确定了一项涉及包括预测方法的治疗方法的研究。
虽然已经确定了许多进展来预测评估方面，但在帮助医疗保健专业人员确定康复持续时间和预后方面的努力很少。因此，未来的研究应该专注于实现预测方法在治疗和预后中的应用，而不是过分关注步态特征的分析。
