Hierarchical Deep Learning Model with Inertial and Physiological Sensors Fusion for Wearable-Based Human Activity Recognition
Author:
Affiliation:
1. University of Toronto,Electrical and Computer Engineering Dept.,ON,Canada
2. Huawei Technologies Canada,ON,Canada
3. University of Guelph,School of Engineering,ON,Canada
Funder
Huawei Technologies
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09747471.pdf?arnumber=9747471
Reference24 articles.
1. Attnsense: Multi-level attention mechanism for multimodal human activity recognition;ma;IJCAI,2019
2. An Autoencoder-based Approach for Recognizing Null Class in Activities of Daily Living In-the-wild via Wearable Motion Sensors
3. Hhar-net: Hierarchical human activity recognition using neural networks;fazli;IHCI,2020
4. Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks
5. Novel approaches to human activity recognition based on accelerometer data
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