ArticleSensors (Basel, Switzerland)2020
Unsupervised End-to-End Deep Model for Newborn and Infant Activity Recognition.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
What it found
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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
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 citations in OpenAlex.
- Objective Measurement of Posture and Movement in Young Children Using Wearable Sensors and Customised Mathematical Approaches: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Systematic review of accelerometer-based methods for 24-h physical behavior assessment in young children (0-5 years old).The international journal of behavioral nutrition and physical activity · 2022Pooled it
- NAPping PAnts (NAPPA): An open wearable solution for monitoring Infant's sleeping rhythms, respiration and posture.Heliyon · 2024Article
- Accelerometer Thresholds for Estimating Physical Activity Intensity Levels in Infants: A Preliminary Study.Sensors (Basel, Switzerland) · 2024Article
- Automatic assessment of infant carrying and holding using at-home wearable recordings.Scientific reports · 2024Article
- Towards an AI-driven soft toy for automatically detecting and classifying infant-toy interactions using optical force sensors.Frontiers in robotics and AI · 2024Article
- Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances.Sensors (Basel, Switzerland) · 2022Review
- Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants.Communications medicine · 2022Article
- Unsupervised Learning for Product Use Activity Recognition: An Exploratory Study of a "Chatty Device".Sensors (Basel, Switzerland) · 2021Article
- An Efficient Classification of Neonates Cry Using Extreme Gradient Boosting-Assisted Grouped-Support-Vector Network.Journal of healthcare engineering · 2021Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Human activity recognition (HAR) works have mostly focused on the activities of adults. However, HAR is typically beneficial to the safety and wellness of newborn or infants because they have difficulties in verbal communication. The activities of infants are different from those of adults in terms of its types and intensity. Hence, it is necessary to study the behavior of infants separately. We study newborn and infant activity recognition by analyzing accelerometer data from the sensors attached to body. We aim to classify four types of activities: sleeping, moving in agony, moving in normal condition, and movement by external force. For this work, we collected 11 h videos and corresponding sensor data from 10 infant subjects. For recognition, we propose an end-to-end deep model using autoencoder and k-means clustering, which is trained in an unsupervised way. From a set of performance tests, our model can achieve 0.96 in balanced accuracy and F-1 score of 0.95.
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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.