Collaborative robots are increasingly present in industry to support human activities. However, to make the human-robot collaborative process more effective, there are several challenges to be addressed. Collaborative robotic systems need to be aware of the human activities to (1) anticipate collaborative/assistive actions, (2) learn by demonstration, and (3) activate safety procedures in shared workspace. This study proposes an action classification system to recognize primitive assembly tasks from human motion events data captured by a Dynamic and Active-pixel Vision Sensor (DAVIS). Several filters are compared and combined to remove event data noise. Task patterns are classified from a continuous stream of event data using advanced deep learning and recurrent networks to classify spatial and temporal features. Experiments were conducted on a novel dataset, the dataset of manufacturing tasks (DMT22), featuring 5 classes of representative manufacturing primitives (PickUp, Place, Screw, Hold, Idle) from 5 participants. Results show that the proposed filters remove about 65\% of all events (noise) per recording, conducting to a classification accuracy up to 99,37\% for subjects that trained the system and 97.08\% for new subjects. Data from a left-handed subject were successfully classified using only right-handed training data. These results are object independent.

翻译：协作机器人在工业中日益普及，用于支持人类活动。然而，为使人类-机器人协作过程更高效，仍需应对若干挑战。协作机器人系统需感知人类活动，以（1）预判协作/辅助动作，（2）通过演示学习，以及（3）在共享工作空间中激活安全程序。本研究提出一种动作分类系统，用于从动态有源像素视觉传感器（DAVIS）捕获的人体运动事件数据中识别原始装配任务。研究比较并组合了多种滤波器以去除事件数据噪声。通过使用先进的深度学习与循环网络对空间和时间特征进行分类，从连续事件数据流中识别任务模式。实验基于新型数据集——制造任务数据集（DMT22），该数据集包含5名参与者的5类代表性制造原始动作（拾取、放置、拧紧、保持、空闲）。结果表明，所提滤波器可移除每段记录中约65%的事件（噪声），使训练过系统的受试者分类准确率达99.37%，新受试者达97.08%。仅使用右手训练数据即可成功分类左手受试者的数据。这些结果与物体无关。