Basteris Angelo, Nijenhuis Sharon M, Stienen Arno H A, Buurke Jaap H, Prange Gerdienke B, Amirabdollahian Farshid
Adaptive Systems Research Group, School of Computer Science, University of Hertfordshire, College Lane, AL95HX Hatfield, United Kingdom.
J Neuroeng Rehabil. 2014 Jul 10;11:111. doi: 10.1186/1743-0003-11-111.
Robot-mediated post-stroke therapy for the upper-extremity dates back to the 1990s. Since then, a number of robotic devices have become commercially available. There is clear evidence that robotic interventions improve upper limb motor scores and strength, but these improvements are often not transferred to performance of activities of daily living. We wish to better understand why. Our systematic review of 74 papers focuses on the targeted stage of recovery, the part of the limb trained, the different modalities used, and the effectiveness of each. The review shows that most of the studies so far focus on training of the proximal arm for chronic stroke patients. About the training modalities, studies typically refer to active, active-assisted and passive interaction. Robot-therapy in active assisted mode was associated with consistent improvements in arm function. More specifically, the use of HRI features stressing active contribution by the patient, such as EMG-modulated forces or a pushing force in combination with spring-damper guidance, may be beneficial.Our work also highlights that current literature frequently lacks information regarding the mechanism about the physical human-robot interaction (HRI). It is often unclear how the different modalities are implemented by different research groups (using different robots and platforms). In order to have a better and more reliable evidence of usefulness for these technologies, it is recommended that the HRI is better described and documented so that work of various teams can be considered in the same group and categories, allowing to infer for more suitable approaches. We propose a framework for categorisation of HRI modalities and features that will allow comparing their therapeutic benefits.
机器人介导的中风后上肢治疗可追溯到20世纪90年代。从那时起,一些机器人设备已投入商业使用。有明确证据表明,机器人干预可提高上肢运动评分和力量,但这些改善往往无法转化为日常生活活动能力。我们希望更好地理解其中原因。我们对74篇论文的系统综述聚焦于恢复的目标阶段、训练的肢体部位、使用的不同模式以及每种模式的有效性。综述表明,迄今为止,大多数研究聚焦于慢性中风患者近端手臂的训练。关于训练模式,研究通常涉及主动、主动辅助和被动交互。主动辅助模式下的机器人治疗与手臂功能的持续改善相关。更具体地说,使用强调患者主动贡献的人机交互(HRI)特征,如肌电图调制力或与弹簧阻尼引导相结合的推力,可能会有帮助。我们的工作还强调,当前文献常常缺乏关于物理人机交互(HRI)机制的信息。不同研究小组(使用不同的机器人和平台)如何实施不同模式往往并不明确。为了更有力、更可靠地证明这些技术的有效性,建议更好地描述和记录人机交互,以便将各团队的工作纳入同一组和类别进行考量,从而推断出更合适的方法。我们提出了一个人机交互模式和特征分类框架,这将有助于比较它们的治疗效果。