Wang Duojin, Gu Xiaoping, Yu Hongliu
Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China; Shanghai Engineering Research Center of Assistive Devices, 516 Jungong Road, Shanghai 200093, China.
Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China.
Med Eng Phys. 2023 Mar;113:103960. doi: 10.1016/j.medengphy.2023.103960. Epub 2023 Feb 17.
In recent years, lower limb exoskeletons (LLEs) have received much attention due to the potential to help people with paraplegia regain the ability of upright-legged locomotion. However, one major hindrance to converting prototypes into actual products is the lack of a balance recovery function. Locomotion intentions can be the first step for balance assistance. Therefore, its significance continues to grow. Many researchers focus on this topic, but there is a lack of a general discussion on the research phenomenon. Therefore, the purpose of this work is to systematize these data and benefit future research. This review is divided into two parts, the location of sensors/devices and the evaluation criteria of algorithms, which are the main components of locomotion intentions. We found that sensor/device placement is still concentrated in the lower limbs, but most researchers have found the importance of the chest. The peak power of the signal collected from the chest may be overestimated because it undergoes higher vertical velocity and acceleration during a rotation. However, despite the differences in peak power between the upper and lower back, high correlations were found for the tasks, especially from sitting to standing. Since peak power is based on vertical acceleration and velocity, it can be considered a metric that is more robust to changes in sensor location. Therefore, data acquisition from the chest is effective. In this paper, it is pointed out that sensors placed on the chest may have a tendency to change, as some researchers have realized in the field of locomotion intention recognition. In the evaluation criteria, we also found that deep learning algorithm (such as Back Propagation Artificial Neural Network (BPANN)) is outstanding, and Support Vector Machine (SVM) is the most cost-effective algorithm. In terms of accuracy, sensitivity, and specificity, BPANN achieved nearly 100%. SVM has different types; the best one achieves 98% accuracy, 100% sensitivity, and 100% specificity. But it also has 87.8% accuracy, which is not stable. Convolutional Neural Networks (CNN) can be used for image classification and have an accuracy of around 87%. Compared to the above two algorithms, CNN may have lower performance. Other algorithms also have higher accuracy, sensitivity, and specificity. These evaluation criteria, however, were not all ideal at the same time. Based on these results, we also point out the existing problems. In general, the application of these algorithms to LLE can contribute to its intention recognition, which can be helpful in balancing research. Finally, this can help make LLE more suitable for daily use.
近年来，下肢外骨骼（LLEs）因其有助于截瘫患者恢复直立行走能力的潜力而备受关注。然而，将原型转化为实际产品的一个主要障碍是缺乏平衡恢复功能。运动意图可能是平衡辅助的第一步。因此，其重要性不断增加。许多研究人员关注这一主题，但缺乏对研究现象的一般性讨论。因此，这项工作的目的是将这些数据系统化，以造福未来的研究。本综述分为两部分，传感器/设备的位置和算法的评估标准，它们是运动意图的主要组成部分。我们发现传感器/设备的放置仍集中在下肢，但大多数研究人员已经发现胸部的重要性。从胸部采集的信号的峰值功率可能被高估，因为它在旋转过程中经历了更高的垂直速度和加速度。然而，尽管上背部和下背部的峰值功率存在差异，但在任务中发现了高度相关性，尤其是从坐姿到站姿。由于峰值功率基于垂直加速度和速度，它可以被认为是一种对传感器位置变化更稳健的度量。因此，从胸部采集数据是有效的。本文指出，放置在胸部的传感器可能存在变化趋势，正如一些研究人员在运动意图识别领域所意识到的那样。在评估标准方面，我们还发现深度学习算法（如反向传播人工神经网络（BPANN））表现出色，支持向量机（SVM）是最具成本效益的算法。在准确性、敏感性和特异性方面，BPANN几乎达到了100%。SVM有不同类型；最好的一种达到了98%的准确率、100%的敏感性和100%的特异性。但它也有87.8%的准确率，不太稳定。卷积神经网络（CNN）可用于图像分类，准确率约为87%。与上述两种算法相比，CNN的性能可能较低。其他算法也有较高的准确性、敏感性和特异性。然而，这些评估标准并非同时都理想。基于这些结果，我们还指出了存在的问题。总体而言，这些算法在LLE中的应用有助于其意图识别，这对平衡研究可能有帮助。最后，这有助于使LLE更适合日常使用。
