Bessler Jule, Prange-Lasonder Gerdienke B, Schaake Leendert, Saenz José F, Bidard Catherine, Fassi Irene, Valori Marcello, Lassen Aske Bach, Buurke Jaap H
Roessingh Research and Development, Enschede, Netherlands.
Department of Biomedical Signals and Systems, University of Twente, Enschede, Netherlands.
Front Robot AI. 2021 Mar 22;8:602878. doi: 10.3389/frobt.2021.602878. eCollection 2021.
The assessment of rehabilitation robot safety is a vital aspect of the development process, which is often experienced as difficult. There are gaps in best practices and knowledge to ensure safe usage of rehabilitation robots. Currently, safety is commonly assessed by monitoring adverse events occurrence. The aim of this article is to explore how safety of rehabilitation robots can be assessed early in the development phase, before they are used with patients. We are suggesting a uniform approach for safety validation of robots closely interacting with humans, based on safety skills and validation protocols. Safety skills are an abstract representation of the ability of a robot to reduce a specific risk or deal with a specific hazard. They can be implemented in various ways, depending on the application requirements, which enables the use of a single safety skill across a wide range of applications and domains. Safety validation protocols have been developed that correspond to these skills and consider domain-specific conditions. This gives robot users and developers concise testing procedures to prove the mechanical safety of their robotic system, even when the applications are in domains with a lack of standards and best practices such as the healthcare domain. Based on knowledge about adverse events occurring in rehabilitation robot use, we identified multi-directional excessive forces on the soft tissue level and musculoskeletal level as most relevant hazards for rehabilitation robots and related them to four safety skills, providing a concrete starting point for safety assessment of rehabilitation robots. We further identified a number of gaps which need to be addressed in the future to pave the way for more comprehensive guidelines for rehabilitation robot safety assessments. Predominantly, besides new developments of safety by design features, there is a strong need for reliable measurement methods as well as acceptable limit values for human-robot interaction forces both on skin and joint level.
康复机器人安全性评估是其开发过程中的一个重要方面,而这一过程通常颇具难度。在确保康复机器人安全使用的最佳实践和知识方面存在差距。目前,安全性通常通过监测不良事件的发生来评估。本文旨在探讨如何在康复机器人用于患者之前的开发阶段早期对其安全性进行评估。我们建议基于安全技能和验证协议,采用一种统一的方法对与人类密切交互的机器人进行安全验证。安全技能是机器人降低特定风险或应对特定危险能力的抽象表示。它们可以根据应用需求以各种方式实现,这使得单一安全技能能够应用于广泛的应用和领域。已经制定了与这些技能相对应并考虑特定领域条件的安全验证协议。这为机器人用户和开发者提供了简洁的测试程序,以证明其机器人系统的机械安全性,即使应用处于缺乏标准和最佳实践的领域,如医疗保健领域。基于对康复机器人使用中发生的不良事件的了解,我们确定软组织层面和肌肉骨骼层面的多方向过度力是康复机器人最相关的危险,并将其与四项安全技能相关联,为康复机器人的安全性评估提供了具体的起点。我们进一步确定了一些未来需要解决的差距,以便为更全面的康复机器人安全评估指南铺平道路。主要的是,除了通过设计特性实现新的安全发展外,还迫切需要可靠的测量方法以及皮肤和关节层面人机交互力的可接受极限值。