论文标题
使用深度学习过程中准动力和动态肌肉收缩期间终点力的多模式估计
Multimodal Estimation of End Point Force During Quasi-dynamic and Dynamic Muscle Contractions Using Deep Learning
论文作者
论文摘要
准确的力量/扭矩估计对于诸如动力外骨骼，机器人技术和康复等应用至关重要。但是，由于关节角度，力水平，肌肉长度和运动速度的变化，动态条件下的力/扭矩估计是一个挑战。我们提出了一种新颖的方法，可以准确地对等渗，同动（准动态）和完全动态条件下的生成力进行建模。我们的解决方案使用深度多模式CNN从多模式EMG-IMU数据中学习，并估算肘部内部和受试者间方案的肘屈曲和扩展的生成力。提出的深层多模式CNN从EMG（时间域和频域）和IMU（在时域）提取了表示形式，并汇总它们以获得有效的嵌入以进行力估计。我们描述了一个包含EMG，IMU和输出力数据的新数据集，该数据集在许多不同的实验条件下收集，并使用此数据集评估我们所提出的方法。结果表明，在不同的实验设置和验证方案中，与其他基线方法以及文献中的其他基线方法相比，我们的方法的鲁棒性。 The obtained $R^2$ values are 0.91$\pm$0.034, 0.87$\pm$0.041, and 0.81$\pm$0.037 for the intra-subject and 0.81$\pm$0.048, 0.64$\pm$0.037, and 0.59$\pm$0.042 for the inter-subject scheme, during isotonic, isokinetic, and dynamic宫缩。此外，我们的结果表明，当包括运动学信息（IMU数据）时，力估计会大大提高。平均提高13.95 \％，118.18 \％和50.0 \％（内部受试者）和28.98 \％，41.18 \％和137.93 \％（Inter-sublyment）的同位素，isokinetic，Isokinetic和动态收缩分别实现。
Accurate force/torque estimation is essential for applications such as powered exoskeletons, robotics, and rehabilitation. However, force/torque estimation under dynamic conditions is a challenging due to changing joint angles, force levels, muscle lengths, and movement speeds. We propose a novel method to accurately model the generated force under isotonic, isokinetic (quasi-dynamic), and fully dynamic conditions. Our solution uses a deep multimodal CNN to learn from multimodal EMG-IMU data and estimate the generated force for elbow flexion and extension, for both intra- and inter-subject schemes. The proposed deep multimodal CNN extracts representations from EMG (in time and frequency domains) and IMU (in time domain) and aggregates them to obtain an effective embedding for force estimation. We describe a new dataset containing EMG, IMU, and output force data, collected under a number of different experimental conditions, and use this dataset to evaluate our proposed method. The results show the robustness of our approach in comparison to other baseline methods as well as those in the literature, in different experimental setups and validation schemes. The obtained $R^2$ values are 0.91$\pm$0.034, 0.87$\pm$0.041, and 0.81$\pm$0.037 for the intra-subject and 0.81$\pm$0.048, 0.64$\pm$0.037, and 0.59$\pm$0.042 for the inter-subject scheme, during isotonic, isokinetic, and dynamic contractions, respectively. Additionally, our results indicate that force estimation improves significantly when the kinematic information (IMU data) is included. Average improvements of 13.95\%, 118.18\%, and 50.0\% (intra-subject) and 28.98\%, 41.18\%, and 137.93\% (inter-subject) for isotonic, isokinetic, and dynamic contractions respectively are achieved.
