Caldas Rafael, Mundt Marion, Potthast Wolfgang, Buarque de Lima Neto Fernando, Markert Bernd
Institute of General Mechanics, RWTH Aachen University, Germany.
Institute of General Mechanics, RWTH Aachen University, Germany.
Gait Posture. 2017 Sep;57:204-210. doi: 10.1016/j.gaitpost.2017.06.019. Epub 2017 Jun 24.
The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects.
评估人类步态的传统方法要么成本高昂，要么在临床实践中定期应用时较为复杂。为了降低成本并简化评估，分别采用了惯性传感器和自适应算法。本文旨在总结基于惯性传感器数据将自适应算法（也称为人工智能算法）应用于步态分析的研究，验证其是否能支持临床评估。通过检索主要数据库来识别文章，检索范围涵盖1968年至2016年10月。我们确定了22项符合纳入标准的研究。对纳入的论文根据其数据采集和处理方法以及特定问卷进行了分析。关于数据采集，平均得分为6.1±1.62，这意味着22篇论文中有13篇未报告相关结果。人工智能算法的质量评估呈现出高于平均水平的评分（8.2±1.84）。因此，人工智能算法似乎能够支持基于惯性传感器数据的步态分析。然而，由于大多数研究使用不同的方法来评估健康受试者，因此需要进一步研究以加强并规范在患者中的应用。
