The clinical assessment technology such as remote monitoring of rehabilitation progress for lower limb related ailments rely on the automatic evaluation of movement performed along with an estimation of joint angle information. In this paper, we introduce a transfer-learning based Long-term Recurrent Convolution Network (LRCN) named as ‘MyoNet’ for the classification of lower limb movements, along with the prediction of the corresponding knee joint angle. The model consists of three blocks- (i) feature extractor block, (ii) joint angle prediction block, and (iii) movement classification block. Initially, the model is end-to-end trained for knee joint angle prediction followed by transferring the knowledge of a trained model to the movement classification through transfer-learning approach making a memory and computationally efficient design. The proposed MyoNet was evaluated on publicly available University of California (UC) Irvine machine learning repository dataset of the lower limb for 11 healthy subjects and 11 subjects with knee pathology for three movements type-walking, standing with knee flexion movements and sitting with knee extension movements. The average mean absolute error (MAE) resulted in the prediction of joint angle for healthy subjects and subjects with knee pathology are 8.1 % and 9.2 % respectively. Subsequently, an average classification accuracy of 98.1 % and 92.4 % were achieved for healthy subjects and subjects with knee pathology, respectively. Interestingly, the significance of this study in itself is promising with substantial improvement in the performance compared to state-of-the-art methodologies. The clinical significance of such surface electromyography signals (sEMG) based movement recognition and prediction of corresponding joint angle system could be beneficial for remote monitoring of rehabilitation progress by the physiotherapist using wearables.

中文翻译：

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MyoNet：基于迁移学习的 LRCN，用于下肢运动识别和膝关节角度预测，用于远程监测 sEMG 的康复进展

远程监控下肢相关疾病的康复进度等临床评估技术依赖于对运动进行的自动评估以及对关节角度信息的估计。在本文中，我们介绍了一种名为“MyoNet”的基于迁移学习的长期循环卷积网络 (LRCN)，用于下肢运动分类以及相应膝关节角度的预测。该模型由三个块组成 - (i) 特征提取器块，(ii) 关节角度预测块，和 (iii) 运动分类块。最初，该模型经过端到端的膝关节角度预测训练，然后通过转移学习方法将训练模型的知识转移到运动分类中，从而形成记忆和计算效率的设计。提议的 MyoNet 在公开可用的加州大学 (UC) 尔湾机器学习存储库数据集上对 11 名健康受试者和 11 名膝关节病理学受试者进行了评估，包括步行、膝关节屈曲站立和膝关节伸展坐姿三种运动类型运动。预测健康受试者和膝关节病变受试者的关节角度的平均平均绝对误差 (MAE) 分别为 8.1% 和 9.2%。随后，健康受试者和膝关节病变受试者的平均分类准确率分别达到 98.1% 和 92.4%。有趣的是，与最先进的方法相比，这项研究本身的意义在于其性能的显着提高。