Abstract Most designs of wearable robots are based on human biomechanical statistics, engineering experience or individual experiments. Despite great successes, few of them consider the human–robot integration and individual differences between users. Additionally, the design periods, cost and safety also need to be further improved. Learning from the natural driving mechanism of human body, we propose a general human-in-the-loop (HIL) optimization designing approach for this kind of wearable robots. Firstly, the human–robot coupling model of the personalized wearable robot and the human musculoskeletal model are established. Then, the Computed Muscle Control (CMC) tool embedded in software OpenSim and the Bayesian optimization used in machine learning are combined to find the optimal design scheme for the personalized wearable robots to reduce the human metabolic energy cost in specific physical movement. The HIL approach could not only optimize the control parameters of wearable robots, but also optimize their geometry, material and any other design parameters flexibly and effectively. An application example for the HIL approach is also provided to help designers better understand and use the HIL method proposed in this paper.

中文翻译：

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可穿戴机器人的人在环优化，以降低人体在身体运动中的代谢能量成本

摘要 可穿戴机器人的设计大多基于人体生物力学统计、工程经验或个体实验。尽管取得了巨大的成功，但很少有人考虑人机集成和用户之间的个体差异。此外，设计周期、成本和安全性也有待进一步提高。从人体的自然驱动机制中学习，我们为这种可穿戴机器人提出了一种通用的人在环（HIL）优化设计方法。首先，建立了个性化可穿戴机器人的人机耦合模型和人体肌肉骨骼模型。然后，将嵌入在软件 OpenSim 中的 Computed Muscle Control (CMC) 工具和机器学习中使用的贝叶斯优化相结合，寻找个性化可穿戴机器人的最佳设计方案，以降低人体在特定身体运动中的代谢能量成本。HIL方法不仅可以优化可穿戴机器人的控制参数，还可以灵活有效地优化其几何、材料和任何其他设计参数。还提供了 HIL 方法的应用示例，以帮助设计人员更好地理解和使用本文提出的 HIL 方法。