Constrained Adaptive Distillation Based on Topological Persistence for Wearable Sensor Data-Reference-Cited by-同舟云学术

Constrained Adaptive Distillation Based on Topological Persistence for Wearable Sensor Data

Published:2023 Issue: Volume:72 Page:1-14
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Jeon Eun Som¹^ORCID,Choi Hongjun²^ORCID,Shukla Ankita¹^ORCID,Wang Yuan³^ORCID,Buman Matthew P.⁴^ORCID,Turaga Pavan¹^ORCID

Affiliation:

1. Geometric Media Laboratory, School of Arts, Media and Engineering, and the School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA

2. Lawrence Livermore National Laboratory, Livermore, CA, USA

3. Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA

4. College of Health Solutions, Arizona State University, Phoenix, AZ, USA

Funder

NIH

Joint Division of Mathematical Science (DMS)/National Institute of General Medical Sciences (NIGMS) Initiative to Support Research at the Interface of the Biological and Mathematical Sciences

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

https://ieeexplore.ieee.org/ielam/19/10012124/10308705-aam.pdf

Reference60 articles.

1. EEG-CLNet: Collaborative Learning for Simultaneous Measurement of Sleep Stages and OSA Events Based on Single EEG Signal

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4. An analysis of eating activities for automatic food type recognition;kim;Proc Asia Pacific Signal Inf Process Assoc Annu Summit Conf,2012

5. Knowledge Distillation: A Survey

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