情境识别与行为识别学习资料与论文

近几年期刊/会议论文整理：

综述性文章：

1. Deep learning for sensor-based human activity recognition: overview, challenges and opportunities.

   Chen, Kaixuan, et al.

   arXiv preprint arXiv:2001.07416 (2020).

2. Deep learning for sensor-based activity recognition: A survey.

   Wang, Jindong, et al.

   Pattern Recognition Letters 119 (2019): 3-11.

   https://arxiv.org/abs/1707.03502

3. Multimodal deep learning for activity and context recognition.

   Radu, Valentin, et al.

   Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1.4 (2018): 1-27.

   https://www.repository.cam.ac.uk/bitstream/handle/1810/293497/main_no_copyright.pdf?sequence=3

Baseline 方法：

1. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition[J].

   Ordóñez F J, Roggen D

   Sensors, 2016, 16(1): 115.

2. **Recognizing detailed human context in the wild from smartphones and smartwatches.[J] **

   Vaizman, Yonatan, Katherine Ellis, and Gert Lanckriet.

   IEEE Pervasive Computing 16.4 (2017): 62-74.

情境识别与行为识别的挑战：

TBA

基于深度学习的情境/行为识别学习资料：

TBA

时间序列学习资料：

1 .Time series classification using multi-channels deep convolutional neural networks[C].

Zheng Y, Liu Q, Chen E, et al

International Conference on Web-Age Information Management. Springer International Publishing, 2014: 298-310.

国内外研究团队：

Andrew Campbell，Dartmouth教授，普适计算领域泰斗级人物，着重研究智能手机和传感器下的行为识别。Google Scholar引用超20000次。

Tanzeem Choudhury，MIT Media Lab毕业，现在Cornell副教授。MIT评为全球最年轻的35岁以下科学家，行为识别领域专家。Google Scholar引用超6000次。

Nic Lane，PhD in Dartmouth。Bell Labs，MSRA。主要研究方向为人群多样性条件下的行为识别。Google Scholar引用超5000次。