CAR -一个用于并发活动识别的深度学习结构:海报摘要

Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks Pub Date : 2017-04-18 DOI:10.1145/3055031.3055058

Yanyi Zhang, Xinyu Li, Jianyu Zhang, Shuhong Chen, Moliang Zhou, Richard A. Farneth, I. Marsic, R. Burd

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引用次数: 11

摘要

我们介绍了并发活动识别器(CAR)——一种高效的深度学习结构，可以从多模态数据中识别复杂的并发团队活动。我们在一个具有挑战性的医疗环境中实施了这个系统，它使用Kinect深度视频和25种医疗对象上的被动RFID标签的数据来识别35种不同的活动。我们的初步结果表明，我们的系统达到了84%的平均准确率，F1-Score为0.20。

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CAR - a deep learning structure for concurrent activity recognition: poster abstract

We introduce the Concurrent Activity Recognizer (CAR) - an efficient deep learning structure that recognizes complex concurrent teamwork activities from multimodal data. We implemented the system in a challenging medical setting, where it recognizes 35 different activities using Kinect depth video and data from passive RFID tags on 25 types of medical objects. Our preliminary results showed our system achieved an 84% average accuracy with 0.20 F1-Score.

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Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks

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