The ability to compute a quality index for manipulation tasks, in different configurations, has been widely used in robotics. However, it is poorly explored in human manipulation and physical human-robot collaboration (pHRC). Existing works that evaluate efficiency of human manipulation often focus only on heurisitic-based, biomechanics or ergonomics methods/tasks. Complementarity between these performance features allows for a better evaluation and more general criteria, applicable across tasks. This letter addressess this gap by generating a new metric that combines offline pre-computation of biomechanics, ergonomics, muscle assessment and joint constraints, and reducing the online time complexity, enhancing the response query time. The proposed solution allow us to build a quality distribution in the human's workspace which can be quickly tailored to specific tasks and filtered for design purposes. This method simplifies human manipulability assessment for both general and task-specific applications and, in contrast to existing works, is suitable for real-time and/or resource-limited applications. Numerical evidence shows the proposed analysis greatly outperforms previous results in terms of computing time without compromising performance.

中文翻译：

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人体舒适度：在人机协作过程中整合人体工程学和肌肉相关指标以进行可操作性分析

计算不同配置下操作任务的质量指数的能力已广泛应用于机器人技术。然而，它在人类操纵和物理人机协作（pHRC）方面的探索很少。评估人类操作效率的现有工作通常只关注基于启发式、生物力学或人体工程学的方法/任务。这些性能特征之间的互补性允许更好的评估和更通用的标准，适用于跨任务。这封信通过生成一个新的指标来弥补这一差距，该指标结合了生物力学、人体工程学、肌肉评估和关节约束的离线预计算，并降低了在线时间复杂度，提高了响应查询时间。提议的解决方案使我们能够在人类中建立质量分布 s 工作区，可以根据特定任务快速定制并为设计目的进行过滤。这种方法简化了一般和特定任务应用程序的人类可操作性评估，并且与现有工作相比，适用于实时和/或资源有限的应用程序。数值证据表明，在不影响性能的情况下，所提出的分析在计算时间方面大大优于以前的结果。