INSERM, UMR1093-CAPS, Université de Bourgogne Franche Comté, UFR des Sciences du Sport, F-21000 Dijon, France.
PROTEOR, 6 rue de la Redoute, CS 37833, CEDEX 21078 Dijon, France.
Sensors (Basel). 2020 Nov 6;20(21):6345. doi: 10.3390/s20216345.
Locomotion assistive devices equipped with a microprocessor can potentially automatically adapt their behavior when the user is transitioning from one locomotion mode to another. Many developments in the field have come from machine learning driven controllers on locomotion assistive devices that recognize/predict the current locomotion mode or the upcoming one. This review synthesizes the machine learning algorithms designed to recognize or to predict a locomotion mode in order to automatically adapt the behavior of a locomotion assistive device. A systematic review was conducted on the Web of Science and MEDLINE databases (as well as in the retrieved papers) to identify articles published between 1 January 2000 to 31 July 2020. This systematic review is reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and is registered on Prospero (CRD42020149352). Study characteristics, sensors and algorithms used, accuracy and robustness were also summarized. In total, 1343 records were identified and 58 studies were included in this review. The experimental condition which was most often investigated was level ground walking along with stair and ramp ascent/descent activities. The machine learning algorithms implemented in the included studies reached global mean accuracies of around 90%. However, the robustness of those algorithms seems to be more broadly evaluated, notably, in everyday life. We also propose some guidelines for homogenizing future reports.
配备微处理器的助行器可以在用户从一种运动模式过渡到另一种运动模式时，自动调整其行为。该领域的许多进展来自于助行器上基于机器学习的控制器，这些控制器可以识别/预测当前的运动模式或即将到来的运动模式。本综述综合了旨在识别或预测运动模式的机器学习算法，以自动调整助行器的行为。我们在 Web of Science 和 MEDLINE 数据库（以及检索到的论文中）进行了系统回顾，以确定 2000 年 1 月 1 日至 2020 年 7 月 31 日期间发表的文章。本系统综述按照系统评价和荟萃分析的首选报告项目（PRISMA）指南进行报告，并在 Prospero（CRD42020149352）上注册。还总结了研究特点、使用的传感器和算法、准确性和稳健性。总共确定了 1343 条记录，其中 58 项研究被纳入本综述。研究中最常调查的实验条件是平地行走以及楼梯和斜坡的上升/下降活动。纳入研究中实施的机器学习算法的全局平均准确率约为 90%。然而，这些算法的稳健性似乎在更广泛的范围内得到了评估，特别是在日常生活中。我们还提出了一些统一未来报告的指导方针。
