Mechanical Engineering Department, College of Engineering and Technology, Cairo Campus, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo 11757, Egypt.
Faculty of Engineering, German International University, Cairo, Egypt.
Sensors (Basel). 2022 Nov 3;22(21):8441. doi: 10.3390/s22218441.
Achieving a normal gait trajectory for an amputee's active prosthesis is challenging due to its kinematic complexity. Accordingly, lower limb gait trajectory kinematics and gait phase segmentation are essential parameters in controlling an active prosthesis. Recently, the most practiced algorithm in gait trajectory generation is the neural network. Deploying such a complex Artificial Neural Network (ANN) algorithm on an embedded system requires performing the calculations on an external computational device; however, this approach lacks mobility and reliability. In this paper, more simple and reliable ANNs are investigated to be deployed on a single low-cost Microcontroller (MC) and hence provide system mobility. Two neural network configurations were studied: Multi-Layered Perceptron (MLP) and Convolutional Neural Network (CNN); the models were trained on shank and foot IMU data. The data were collected from four subjects and tested on a fifth to predict the trajectory of 200 ms ahead. The prediction was made for two cases: with and without providing the current phase of the gait. Then, the models were deployed on a low-cost microcontroller (ESP32). It was found that with fewer data (excluding the current gait phase), CNN achieved a better correlation coefficient of 0.973 when compared to 0.945 for MLP; when including the current phase, both network configurations achieved better correlation coefficients of nearly 0.98. However, when comparing the execution time required for the prediction on the intended MC, MLP was much faster than CNN, with an execution time of 2.4 ms and 142 ms, respectively. In summary, it was found that when training data are scarce, CNN is more efficient within the acceptable execution time, while MLP achieves relative accuracy with low execution time with enough data.
由于其运动复杂性，为假肢实现正常的步态轨迹是具有挑战性的。因此，下肢步态轨迹运动学和步态阶段分割是控制主动假肢的重要参数。最近，在步态轨迹生成中应用最广泛的算法是神经网络。在嵌入式系统上部署如此复杂的人工神经网络（ANN）算法需要在外部计算设备上执行计算；然而，这种方法缺乏移动性和可靠性。在本文中，研究了更简单和可靠的 ANN，可以部署在单个低成本微控制器（MC）上，从而提供系统的移动性。研究了两种神经网络配置：多层感知器（MLP）和卷积神经网络（CNN）；模型在小腿和脚部 IMU 数据上进行了训练。数据由四名受试者收集，并在第五名受试者上进行了测试，以预测 200ms 内的轨迹。预测是在两种情况下进行的：提供和不提供步态的当前阶段。然后，将模型部署在低成本微控制器（ESP32）上。结果发现，在数据较少的情况下（不包括当前步态阶段），CNN 达到了更好的相关系数 0.973，而 MLP 为 0.945；当包括当前阶段时，两种网络配置都实现了接近 0.98 的更好的相关系数。然而，当比较在预期 MC 上进行预测所需的执行时间时，MLP 比 CNN 快得多，分别为 2.4ms 和 142ms。总之，研究发现，在训练数据稀缺的情况下，CNN 在可接受的执行时间内更有效，而 MLP 在有足够数据的情况下，以较低的执行时间实现相对精度。
