This paper builds on our previous work by exploiting Artificial Intelligence to predict individual grip force variability in manual robot control. Grip forces were recorded from various loci in the dominant and non dominant hands of individuals by means of wearable wireless sensor technology. Statistical analyses bring to the fore skill specific temporal variations in thousands of grip forces of a complete novice and a highly proficient expert in manual robot control. A brain inspired neural network model that uses the output metric of a Self Organizing Map with unsupervised winner take all learning was run on the sensor output from both hands of each user. The neural network metric expresses the difference between an input representation and its model representation at any given moment in time t and reliably captures the differences between novice and expert performance in terms of grip force variability.Functionally motivated spatiotemporal analysis of individual average grip forces, computed for time windows of constant size in the output of a restricted amount of task-relevant sensors in the dominant (preferred) hand, reveal finger-specific synergies reflecting robotic task skill. The analyses lead the way towards grip force monitoring in real time to permit tracking task skill evolution in trainees, or identify individual proficiency levels in human robot interaction in environmental contexts of high sensory uncertainty. Parsimonious Artificial Intelligence (AI) assistance will contribute to the outcome of new types of surgery, in particular single-port approaches such as NOTES (Natural Orifice Transluminal Endoscopic Surgery) and SILS (Single Incision Laparoscopic Surgery).

翻译：本文以先前工作为基础，利用人工智能预测手动机器人控制中个体抓握力变异。通过可穿戴无线传感器技术，从个体的优势手和非优势手多个位点记录抓握力。统计分析揭示了完全新手与高熟练专家在手动机器人控制中数千个抓握力的技能特异性时间变化。采用基于脑启发的神经网络模型，该模型使用自组织映射（Self Organizing Map）的输出度量，并结合无监督赢家通吃学习机制，对每位用户双手的传感器输出进行运算。神经网络度量表征了任意时刻t输入表示与其模型表示之间的差异，并可靠地捕捉到新手与专家在抓握力变异方面的差异。基于功能驱动的个体平均抓握力时空分析（针对优势手中有限数量的任务相关传感器输出的固定大小时间窗口计算）揭示出反映机器人操作技能的特定手指协同模式。该分析为实时监测抓握力铺平道路，可追踪受训者的技能演变，或在感官高度不确定的环境背景下识别人机交互中的个体熟练水平。简约型人工智能辅助将推动新型手术的发展，尤其是单孔入路手术如经自然腔道内镜手术和单切口腹腔镜手术。