Activity recognition in manufacturing: The roles of motion capture and sEMG+inertial wearables in detecting fine vs. gross motion
Activity recognition in manufacturing: The roles of motion capture and sEMG+inertial wearables in detecting fine vs. gross motion
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DOI：
10.1109/icra.2019.8793954
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
A. Kubota;T. Iqbal;J. Shah;L. Riek
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文献类型：
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作者：
A. Kubota;T. Iqbal;J. Shah;L. Riek
在安全关键的环境中，机器人需要可靠地识别人类的活动，才能成为有效和值得信任的合作伙伴。由于大多数人类活动识别(HAR)方法依赖于单峰传感器数据(例如，运动捕捉或可穿戴传感器)，传感器形态和活动的运动粒度(例如，粗略或精细)之间的关系如何影响分类精度尚不清楚。据我们所知，我们是第一个研究在制造环境中使用运动捕捉与可穿戴传感器数据来识别人体运动的有效性的人。我们介绍了加州大学圣迭戈分校-麻省理工学院的人体运动数据集，它由两个需要粗略或细粒度运动的装配任务组成。对于这两个任务，我们使用了三种广泛使用的HAR算法，比较了VICON运动捕捉系统和Myo臂章的准确性。我们发现，运动捕捉在粗略运动识别方面的准确率高于可穿戴传感器(高达36.95%)，而可穿戴传感器在细粒度运动识别方面的准确率更高(高达28.06%)。这些结果表明，这些传感器模式是互补的，机器人可能受益于利用多个模式同时但独立地检测粗略和细粒度运动的系统。我们的发现将有助于指导机器人学众多领域的研究人员，包括从演示和抓取中学习，以有效地选择最适合其应用的传感器模式。
In safety-critical environments, robots need to reliably recognize human activity to be effective and trust-worthy partners. Since most human activity recognition (HAR) approaches rely on unimodal sensor data (e.g. motion capture or wearable sensors), it is unclear how the relationship between the sensor modality and motion granularity (e.g. gross or fine) of the activities impacts classification accuracy. To our knowledge, we are the first to investigate the efficacy of using motion capture as compared to wearable sensor data for recognizing human motion in manufacturing settings. We introduce the UCSD-MIT Human Motion dataset, composed of two assembly tasks that entail either gross or fine-grained motion. For both tasks, we compared the accuracy of a Vicon motion capture system to a Myo armband using three widely used HAR algorithms. We found that motion capture yielded higher accuracy than the wearable sensor for gross motion recognition (up to 36.95%), while the wearable sensor yielded higher accuracy for fine-grained motion (up to 28.06%). These results suggest that these sensor modalities are complementary, and that robots may benefit from systems that utilize multiple modalities to simultaneously, but independently, detect gross and fine-grained motion. Our findings will help guide researchers in numerous fields of robotics including learning from demonstration and grasping to effectively choose sensor modalities that are most suitable for their applications.