Surgical robotics holds much promise for improving patient safety and
clinician experience in the Operating Room (OR). However, it also comes with
new challenges, requiring strong team coordination and effective OR management.
Automatic detection of surgical activities is a key requirement for developing
AI-based intelligent tools to tackle these challenges. The current
state-of-the-art surgical activity recognition methods however operate on
image-based representations and depend on large-scale labeled datasets whose
collection is time-consuming and resource-expensive. This work proposes a new
sample-efficient and object-based approach for surgical activity recognition in
the OR. Our method focuses on the geometric arrangements between clinicians and
surgical devices, thus utilizing the significant object interaction dynamics in
the OR. We conduct experiments in a low-data regime study for long video
activity recognition. We also benchmark our method againstother object-centric
approaches on clip-level action classification and show superior performance.

手术机器人在手术室中产生了很大的潜力，但也带来了新的挑战，需要强大的团队协调和有效的手术室管理。本文提出了一种新的样本有效和基于对象的手术活动识别方法，着重利用手术室中临床医生和手术设备之间的几何排列以及对象间的交互动力学。通过在低数据条件下进行长视频活动识别的实验，并与其他基于对象的方法在剪辑级别的动作分类上进行测试，展现了出色的性能。

ST (OR) 2：手术室活动识别的时空对象级推理

ST(OR)2: Spatio-Temporal Object Level Reasoning for Activity Recognition  in the Operating Room

We apply recurrent neural networks to the task of recognizing surgical
activities from robot kinematics. Prior work in this area focuses on
recognizing short, low-level activities, or gestures, and has been based on
variants of hidden Markov models and conditional random fields. In contrast, we
work on recognizing both gestures and longer, higher-level activites, or
maneuvers, and we model the mapping from kinematics to gestures/maneuvers with
recurrent neural networks. To our knowledge, we are the first to apply
recurrent neural networks to this task. Using a single model and a single set
of hyperparameters, we match state-of-the-art performance for gesture
recognition and advance state-of-the-art performance for maneuver recognition,
in terms of both accuracy and edit distance. Code is available at
this https URL .

使用循环神经网络识别机器人运动学中的外科手术活动，成果表明使用 RNN 可以有效识别手势和操作，并提高了准确性和编辑距离。