Robotic lower-limb rehabilitation training is a better alternative for the physical training efforts of a therapist due to advantages, such as intensive repetitive motions, economical therapy, and quantitative assessment of the level of motor recovery through the measurement of force and movement patterns. However, in actual robotic rehabilitation training, emergency stops occur frequently to prevent injury to patients. However, frequent stopping is a waste of time and resources of both therapists and patients. Therefore, early detection of emergency stops in real-time is essential to take appropriate actions. In this paper, we propose a novel deep-learning-based technique for detecting emergency stops as early as possible. First, a bidirectional long short-term memory prediction model was trained using only the normal joint data collected from a real robotic training system. Next, a real-time threshold-based algorithm was developed with cumulative error. The experimental results revealed a precision of 0.94, recall of 0.93, and F1 score of 0.93. Additionally, it was observed that the prediction model was robust for variations in measurement noise.

中文翻译：

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基于深度学习的机器人下肢康复训练系统紧急停止预测

机器人下肢康复训练是治疗师身体训练工作的更好选择，因为它具有以下优点，例如密集的重复运动、经济的治疗以及通过测量力量和运动模式对运动恢复水平的定量评估。然而，在实际的机器人康复训练中，为了防止对患者造成伤害，经常会发生紧急停止。然而，频繁停止是对治疗师和患者双方的时间和资源的浪费。因此，实时及早检测紧急停止对于采取适当的行动至关重要。在本文中，我们提出了一种新的基于深度学习的技术，用于尽早检测紧急停止。第一的，双向长短期记忆预测模型仅使用从真实机器人训练系统收集的正常关节数据进行训练。接下来，开发了一种具有累积误差的实时基于阈值的算法。实验结果显示准确率为 0.94，召回率为 0.93，F1 得分为 0.93。此外，观察到预测模型对于测量噪声的变化是稳健的。