Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Temperature Measurement Sites

Temperature Measurement Sites

A thermometer measures body temperature. The common sites for measuring body temperature are the oral cavity, axillary region, temporal artery, and skin surface, such as the forehead, abdomen, and axilla. True core body temperature is assessed in the rectum, tympanic membrane, pulmonary artery, esophagus, and urinary bladder.
Oral: When assessing oral temperature, the thermometer tip should be placed under the tongue in the posterior sublingual pocket. It offers accurate readings and can be...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Toward Understanding Free-Flowing Manual Object Contact: Real-Time Interaction Between Body Position and Object Type in 9-Month-Olds.

Infancy : the official journal of the International Society on Infant Studies·2026

Same author

The role of attention to the mouth of talking faces for vocabulary skills during toddlerhood: Does language familiarity still matter?

Child development·2026

Same author

Vocalisations are coupled with movement of all limbs throughout infancy.

Scientific reports·2025

Same author

Context Shapes (Proto)Conversations in the First Year of Life.

Developmental science·2025

Same author

The effect of face orientation on audiovisual speech integration in infancy: An electrophysiological study.

Developmental psychobiology·2023

Same author

The Development of Cortical Responses to the Integration of Audiovisual Speech in Infancy.

Brain topography·2023

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 20, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

Identifying Infant Body Position from Inertial Sensors with Machine Learning: Which Parameters Matter?

Joanna Duda-Goławska¹, Aleksander Rogowski², Zuzanna Laudańska¹

¹Neurocognitive Development Lab, Institute of Psychology, Polish Academy of Sciences, ul. Jaracza 1, 00-378 Warsaw, Poland.

Sensors (Basel, Switzerland)

|December 17, 2024

Summary

This summary is machine-generated.

This study demonstrates that the CatBoost machine learning model accurately classifies infant body positions using Inertial Motion Unit (IMU) sensor data. Accelerometer and magnetometer data are key for reliable infant motor development monitoring.

Keywords:

explainable machine learning human activity recognition inertial motion sensors

More Related Videos

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

Related Experiment Videos

Last Updated: Jun 20, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

Area of Science:

Biomedical Engineering
Infant Motor Development
Machine Learning Applications

Background:

Accurate infant body position classification is vital for monitoring motor development and enabling early intervention.
Manual video analysis is labor-intensive, prompting the use of Inertial Motion Unit (IMU) sensors for automated classification.
Supervised machine learning with hand-crafted features is a common approach for IMU data classification.

Purpose of the Study:

To compare the performance of CatBoost Classifier against Random Forest Classifier for classifying infant body positions using IMU data.
To identify the most important sensor data features for accurate body position classification in infants aged 4-12 months.

Main Methods:

Utilized a longitudinal dataset of IMU recordings from infants (4-12 months) during three play activities.
Employed supervised machine learning, specifically CatBoost and Random Forest classifiers.
Conducted data ablation experiments and SHAP value analysis to assess feature importance.

Main Results:

The CatBoost Classifier significantly outperformed the Random Forest Classifier.
Achieved high classification accuracies: Supine (97.7%), Sitting (93.5%), and Prone (89.9%).
Accelerometer and magnetometer data, particularly their statistical features, were identified as critical for classification accuracy.

Conclusions:

The CatBoost model offers a superior, automated method for classifying infant body positions from IMU data.
This approach can aid in the early detection of developmental issues and facilitate timely interventions.
Feature importance analysis highlights the value of specific sensor data characteristics for robust classification.