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Maximum Dorsiflexion Detection Based on an On-Board Adaptive Algorithm for Transtibial Amputees With Robotic Prostheses

机译:基于机器人假肢的谐波术语的最大背裂检测

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Maximum dorsiflexion (MDF) is an important gait event corresponding to the maximum ankle dorsiflexion angle in each gait cycle. MDF timing plays an important role in the control of robotic prosthesis. This article puts forward an on-board adaptive algorithm to detect MDF timing of robotic transtibial prosthesis in different walking conditions (at different speeds and on different ramps) and for different users. Based on the adaptive algorithm, we can get a time-variant detection model. The framework of the adaptive algorithm is composed of: 1) training data collecting and labeling; 2) model training and real-time detection; and 3) model updating according to the detection results. Based on the adaptive algorithm, we conducted speed and ramp experiments to detect MDF timings at slow, normal, and fast speeds, and on ramps with different inclination angles (10 degrees, 5 degrees, 0 degrees, -5 degrees, and -10 degrees). Three transtibial amputee participated in the experiments. The model training/updating time ranges from 3.6 to 4.1 s and the detection time ranges from 0.95 to 1.17 ms for different speeds and ramps. In real-time detection, there is false detection (1.67%) at normal walking speed. In addition, all MDF timings are detected correctly (accuracy: 100%) based on the adaptive algorithm. The mean detection delays are 7.23, 18.27, and 7.5 ms corresponding to slow, normal and fast speeds and 10.60, 10.30, 18.27, 10.27, and 15.63 ms for ramps of different inclination angles (10 degrees, 5 degrees, 0 degrees, -5 degrees, and -10 degrees). Compared with the proposed adaptive algorithm, both the nonadaptive and adaptive threshold decision methods cause more false detections. The results show that the proposed approach for MDF timing detection has adaptations to different walking conditions (speeds and ramps) and prosthesis users, which indicates that the adaptive algorithm is effective and shows the potential in robotic prosthesis control in the future. Note to Practitioners-This article proposes an on-board adaptive algorithm to detect the maximum dorsiflexion (MDF) timing based on inertial measurement unit (IMU) and ankle angle sensor for robotic transtibial prosthesis users in each gait cycle. IMU and angle sensor are integrated in the prosthesis, and the adaptive algorithm is embedded in the control circuit of prosthesis. The adaptive algorithm can realize the model updating continuously for real-time MDF timing detection with collected and labeled training data. The proposed adaptive algorithm shows satisfactory adaptation for MDF timing detection in different walking speed and ramp conditions. In addition, the adaptive algorithm also shows some generalizations for prosthesis users, which are useful to improve prosthesis control.
机译:最大背屈(MDF)是与每个步态周期中的最大脚踝背屈角相对应的重要步态事件。 MDF定时在控制机器人假体的控制中起重要作用。本文提出了一个板载自适应算法,以检测不同步行条件(以不同的速度和不同斜坡上)和不同用户的机器人串易假体的MDF定时。基于自适应算法,我们可以获得时变检测模型。自适应算法的框架由以下组成:1)培训数据收集和标签; 2)模型培训和实时检测; 3)根据检测结果模型更新。基于自适应算法,我们进行了速度和斜坡实验,以检测缓慢,正常和快速速度的MDF定时,以及不同倾斜角度的斜坡(10度,5度,0度,-5度和-10度)。三个打架截肢者参与了实验。模型训练/更新时间范围为3.6至4.1秒,检测时间为不同速度和斜坡的0.95至1.17 ms。在实时检测中,在正常的步行速度下存在假检测(1.67%)。此外,基于自适应算法,正确检测到所有MDF定时(精确度:100%)。平均检测延迟为7.23,18.27和7.5ms,对应于慢速,正常和快速速度,10.60,10.30,18.27,10.27和15.63ms,用于不同倾角的斜角(10度,5度,0度,-5度和-10度)。与所提出的自适应算法相比,非接受和自适应阈值决策方法都导致更虚假的检测。结果表明,所提出的MDF定时检测方法具有对不同的步行条件(速度和斜坡)和假体用户的适应,这表明自适应算法是有效的,并显示了未来机器人假体控制的潜力。请注意从业者 - 本文提出了一种用于检测基于惯性测量单元(IMU)和脚踝角度传感器的惯性自适应算法,以及每个步态周期中的机器人打开假体用户。 IMU和角度传感器集成在假体中,自适应算法嵌入在假体的控制电路中。自适应算法可以使用收集和标记的训练数据来实现模型更新,以进行实时MDF定时检测。所提出的自适应算法显示了在不同步行速度和斜坡条件下的MDF定时检测令人满意的适应性。此外,自适应算法还显示出对假体用户的一些概括,这对于改善假体控制是有用的。

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