ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2018
Children Activity Recognition: Challenges and Strategies.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Using an Adult-Designed Wearable for Pediatric Monitoring: Practical Tutorial and Application in School-Aged Children With Obesity.Journal of medical Internet research · 2026Article
- ByteTrack: a deep learning approach for bite count and bite rate detection using meal videos in children.Frontiers in nutrition · 2025Article
- Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network.Sensors (Basel, Switzerland) · 2021Article
- Emotion-Aware and Intelligent Internet of Medical Things Toward Emotion Recognition During COVID-19 Pandemic.IEEE internet of things journal · 2021Article
- Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors' Data.JMIR mHealth and uHealth · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
In this paper, we study the problem of children activity recognition using smartwatch devices. We introduce the need for a robust children activity model and challenges involved. To address the problem, we employ two deep neural network models, specifically, Bi-Directional LSTM model and a fully connected deep network and compare the results to commonly used models in the area. We demonstrate that our proposed deep models can significantly improve results compared to baseline models. We further show benefits of activity intensity level detection in health monitoring and verify high performance of our proposed models in this task.
Indexed as
Identifiers
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.