Lambercy Olivier, Lehner Rea, Chua Karen, Wee Seng Kwee, Rajeswaran Deshan Kumar, Kuah Christopher Wee Keong, Ang Wei Tech, Liang Phyllis, Campolo Domenico, Hussain Asif, Aguirre-Ollinger Gabriel, Guan Cuntai, Kanzler Christoph M, Wenderoth Nicole, Gassert Roger
Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Switzerland.
Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.
Front Robot AI. 2021 May 5;8:612415. doi: 10.3389/frobt.2021.612415. eCollection 2021.
Current neurorehabilitation models primarily rely on extended hospital stays and regular therapy sessions requiring close physical interactions between rehabilitation professionals and patients. The current COVID-19 pandemic has challenged this model, as strict physical distancing rules and a shift in the allocation of hospital resources resulted in many neurological patients not receiving essential therapy. Accordingly, a recent survey revealed that the majority of European healthcare professionals involved in stroke care are concerned that this lack of care will have a noticeable negative impact on functional outcomes. COVID-19 highlights an urgent need to rethink conventional neurorehabilitation and develop alternative approaches to provide high-quality therapy while minimizing hospital stays and visits. Technology-based solutions, such as, robotics bear high potential to enable such a paradigm shift. While robot-assisted therapy is already established in clinics, the future challenge is to enable physically assisted therapy and assessments in a minimally supervized and decentralized manner, ideally at the patient's home. Key enablers are new rehabilitation devices that are portable, scalable and equipped with clinical intelligence, remote monitoring and coaching capabilities. In this perspective article, we discuss clinical and technological requirements for the development and deployment of minimally supervized, robot-assisted neurorehabilitation technologies in patient's homes. We elaborate on key principles to ensure feasibility and acceptance, and on how artificial intelligence can be leveraged for embedding clinical knowledge for safe use and personalized therapy adaptation. Such new models are likely to impact neurorehabilitation beyond COVID-19, by providing broad access to sustained, high-quality and high-dose therapy maximizing long-term functional outcomes.
当前的神经康复模式主要依赖于延长住院时间和定期治疗课程,这需要康复专业人员与患者之间密切的身体互动。当前的新冠疫情对这种模式提出了挑战,因为严格的物理距离规则和医院资源分配的变化导致许多神经疾病患者无法接受必要的治疗。因此,最近的一项调查显示,大多数参与中风护理的欧洲医疗保健专业人员担心这种护理缺失会对功能预后产生明显的负面影响。新冠疫情凸显了迫切需要重新思考传统的神经康复模式,并开发替代方法,以在尽量减少住院时间和就诊次数的同时提供高质量的治疗。基于技术的解决方案,如机器人技术,具有实现这种范式转变的巨大潜力。虽然机器人辅助治疗已经在诊所中得到应用,但未来的挑战是能够以最少的监督和分散的方式进行物理辅助治疗和评估,理想情况是在患者家中进行。关键的推动因素是新型康复设备,这些设备便于携带、可扩展,并具备临床智能、远程监测和指导能力。在这篇观点文章中,我们讨论了在患者家中开发和部署最少监督的机器人辅助神经康复技术的临床和技术要求。我们阐述了确保可行性和可接受性的关键原则,以及如何利用人工智能嵌入临床知识以实现安全使用和个性化治疗调整。这种新模式可能会在新冠疫情之后对神经康复产生影响,通过提供广泛的机会获得持续、高质量和高剂量的治疗,从而最大化长期功能预后。