CNN-based sensor fusion techniques for multimodal human activity recognition-Reference-Cited by-同舟云学术

CNN-based sensor fusion techniques for multimodal human activity recognition

Published:2017-09-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2017 ACM International Symposium on Wearable Computers
language:
Short-container-title:

Author:

Münzner Sebastian¹,Schmidt Philip²,Reiss Attila²,Hanselmann Michael²,Stiefelhagen Rainer¹,Dürichen Robert²

Affiliation:

1. Karlsruhe Institute of Technology, Germany

2. Robert Bosch GmbH, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3123021.3123046

Reference24 articles.

1. Ba J. and Kingma D. Adam: A method for stochastic optimization 2015. Ba J. and Kingma D. Adam: A method for stochastic optimization 2015.

2. A benchmark dataset to evaluate sensor displacement in activity recognition

3. Dieleman S. Schlter J. Raffel C. etal Lasagne: First release. Aug. 2015. Dieleman S. Schlter J. Raffel C. et al. Lasagne: First release. Aug. 2015.

Cited by 153 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Beyond Grids: Scaling Up Continuous Kernels via Adaptive Point Representations for Sensor-Based Human Activity Recognition;IEEE Sensors Journal;2024-08-01

2. Multi-teacher cross-modal distillation with cooperative deep supervision fusion learning for unimodal segmentation;Knowledge-Based Systems;2024-08

3. Complex Human Activity Recognition Based on Spatial LSTM and Deep Residual Convolutional Network Using Wearable Motion Sensors;IEEE Sensors Journal;2024-07-15

4. An Explore–Exploit Workload-Bounded Strategy for Rare Event Detection in Massive Energy Sensor Time Series;ACM Transactions on Intelligent Systems and Technology;2024-06-28

5. AutoAugHAR: Automated Data Augmentation for Sensor-based Human Activity Recognition;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2024-05-13