Multi-Sensor Time-Series Classification for Activity Tracking Under Variable Length
Author:
Funder
National Science Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/8984669/08903306.pdf?arnumber=8903306
Cited by 6 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. State-of-the-art Activity Recognition and Prediction Techniques Applicable to the Home Energy Management System;2022 IEEE 7th International Energy Conference (ENERGYCON);2022-05-09
2. An Improved Self-Training Method for Positive Unlabeled Time Series Classification Using DTW Barycenter Averaging;Sensors;2021-11-08
3. Reducing Label Fragmentation During Time-series Data Annotation to Reduce Annotation Costs;Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers;2021-09-21
4. S-THAD: a framework for sensor-based temporal human activity detection from continuous data streams;Journal of Ambient Intelligence and Humanized Computing;2021-02-26
5. Multimodal data fusion framework enhanced robot-assisted minimally invasive surgery;Transactions of the Institute of Measurement and Control;2021-01-18
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