ArticleProceedings of the Eighth Wireless of the Students, by the Students, and for the Students Workshop. Workshop on Wireless of the Students, by the Students, for the Students (8th : 2016 : New York, N.Y.)2016
Deep Neural Network for RFID-Based Activity Recognition.
Article in Proceedings of the Eighth Wireless of the Students, by the Students, and for the Students Workshop. Workshop on Wireless of the Students, by the Students, for the Students (8th : 2016 : New York, N.Y.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.Technology in cancer research & treatmentPooled it
- Feature Fusion of a Deep-Learning Algorithm into Wearable Sensor Devices for Human Activity Recognition.Sensors (Basel, Switzerland) · 2021Article
- New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition.Sensors (Basel, Switzerland) · 2021Article
- Deep Learning for RFID-Based Activity Recognition.Proceedings of the ... International Conference on Embedded Networked Sensor Systems. International Conference on Embedded Networked Sensor Systems · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
We propose a Deep Neural Network (DNN) structure for RFID-based activity recognition. RFID data collected from several reader antennas with overlapping coverage have potential spatiotemporal relationships that can be used for object tracking. We augmented the standard fully-connected DNN structure with additional pooling layers to extract the most representative features. For model training and testing, we used RFID data from 12 tagged objects collected during 25 actual trauma resuscitations. Our results showed 76% recognition micro-accuracy for 7 resuscitation activities and 85% average micro-accuracy for 5 resuscitation phases, which is similar to existing system that, however, require the user to wear an RFID antenna.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.