Fine-grained activity recognition enables explainable analysis of procedures for skill assessment, autonomy, and error detection in robot-assisted surgery. However, existing recognition models suffer from the limited availability of annotated datasets with both kinematic and video data and an inability to generalize to unseen subjects and tasks. Kinematic data from the surgical robot is particularly critical for safety monitoring and autonomy, as it is unaffected by common camera issues such as occlusions and lens contamination. We leverage an aggregated dataset of six dry-lab surgical tasks from a total of 28 subjects to train activity recognition models at the gesture and motion primitive (MP) levels and for separate robotic arms using only kinematic data. The models are evaluated using the LOUO (Leave-One-User-Out) and our proposed LOTO (Leave-One-Task-Out) cross validation methods to assess their ability to generalize to unseen users and tasks respectively. Gesture recognition models achieve higher accuracies and edit scores than MP recognition models. But, using MPs enables the training of models that can generalize better to unseen tasks. Also, higher MP recognition accuracy can be achieved by training separate models for the left and right robot arms. For task-generalization, MP recognition models perform best if trained on similar tasks and/or tasks from the same dataset.

翻译：细粒度活动识别能够对机器人辅助手术中的技能评估、自主性和错误检测进行可解释的程序分析。然而，现有识别模型面临两个主要挑战：标注数据集有限（同时包含运动学与视频数据），以及无法泛化到未见过的受试者和任务。来自手术机器人的运动学数据对安全监控和自主性尤为关键，因为它不受常见相机问题（如遮挡和镜头污染）的影响。我们利用包含6个干实验室外科任务、共28名受试者的聚合数据集，仅使用运动学数据，在手势和原始动作（MP）级别训练活动识别模型，并针对不同机器人手臂分别建模。采用留一受试者（LOUO）及我们提出的留一任务（LOTO）交叉验证方法评估模型泛化能力，分别检验其对未见用户和未见任务的适应能力。手势识别模型在准确率和编辑分数上均优于MP识别模型；但基于MP的模型能更好地泛化到未见任务。此外，为左右机器人手臂分别训练模型可进一步提升MP识别准确率。在任务泛化方面，当训练数据包含相似任务或来自同一数据集的任务时，MP识别模型表现最佳。