ArticleProceedings of the ... International Conference on Embedded Networked Sensor Systems. International Conference on Embedded Networked Sensor Systems2016
Deep Learning for RFID-Based Activity Recognition.
Article in Proceedings of the ... International Conference on Embedded Networked Sensor Systems. International Conference on Embedded Networked Sensor Systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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
20 citing papers in PubMed.
- Automatic Detection of Physiological Attributes from Verbal Communication During Time-Critical Medical Events.ACM transactions on computing for healthcare · 2026Article
- Tag-free indoor fall detection using transformer network encoder and data fusion.Scientific reports · 2024Article
- Focusing on What Matters: Fine-grained Medical Activity Recognition for Trauma Resuscitation via Actor Tracking.Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops · 2024Article
- Daily Living Activity Recognition with Frequency-Shift WiFi Backscatter Tags.Sensors (Basel, Switzerland) · 2024Article
- Displacement Estimation via 3D-Printed RFID Sensors for Structural Health Monitoring: Leveraging Machine Learning and Photoluminescence to Overcome Data Gaps.Sensors (Basel, Switzerland) · 2024Article
- The Lifespan of Human Activity Recognition Systems for Smart Homes.Sensors (Basel, Switzerland) · 2023Article
- Real-time Context-Aware Multimodal Network for Activity and Activity-Stage Recognition from Team Communication in Dynamic Clinical Settings.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2023Article
- Multi-dimensional task recognition for human-robot teaming: literature review.Frontiers in robotics and AI · 2023Review
- Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus Context.Sensors (Basel, Switzerland) · 2022Article
- State-of-the-art of situation recognition systems for intraoperative procedures.Medical & biological engineering & computing · 2022Review
- Human activity recognition in artificial intelligence framework: a narrative review.Artificial intelligence review · 2022Article
- A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning.Sensors (Basel, Switzerland) · 2021Review
- Deep artificial neural network based on environmental sound data for the generation of a children activity classification model.PeerJ. Computer science · 2020Article
- Multimodal Attention Network for Trauma Activity Recognition from Spoken Language and Environmental Sound.Proceedings. IEEE International Conference on Healthcare Informatics · 2019Article
- An approach to automatic process deviation detection in a time-critical clinical process.Journal of biomedical informatics · 2018Article
- An RFID Indoor Positioning Algorithm Based on Support Vector Regression.Sensors (Basel, Switzerland) · 2018Article
- Progress Estimation and Phase Detection for Sequential Processes.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2017Article
- Language-Based Process Phase Detection in the Trauma Resuscitation.Proceedings. IEEE International Conference on Healthcare Informatics · 2017Article
- Speech-Based Activity Recognition for Trauma Resuscitation.Proceedings. IEEE International Conference on Healthcare InformaticsArticle
- Video-based Concurrent Activity Recognition for Trauma Resuscitation.Proceedings. IEEE International Conference on Healthcare InformaticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
We present a system for activity recognition from passive RFID data using a deep convolutional neural network. We directly feed the RFID data into a deep convolutional neural network for activity recognition instead of selecting features and using a cascade structure that first detects object use from RFID data followed by predicting the activity. Because our system treats activity recognition as a multi-class classification problem, it is scalable for applications with large number of activity classes. We tested our system using RFID data collected in a trauma room, including 14 hours of RFID data from 16 actual trauma resuscitations. Our system outperformed existing systems developed for activity recognition and achieved similar performance with process-phase detection as systems that require wearable sensors or manually-generated input. We also analyzed the strengths and limitations of our current deep learning architecture for activity recognition from RFID data.
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What OpenQuestion holds
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.