Version 1
: Received: 16 October 2024 / Approved: 17 October 2024 / Online: 17 October 2024 (14:47:16 CEST)
How to cite:
Duda-Goławska, J.; Rogowski, A.; Laudańska, Z.; Żygierewicz, J.; Tomalski, P. Identifying Infant Body Position from Inertial Sensors with Machine Learning: Which Parameters Matter?. Preprints2024, 2024101387. https://doi.org/10.20944/preprints202410.1387.v1
Duda-Goławska, J.; Rogowski, A.; Laudańska, Z.; Żygierewicz, J.; Tomalski, P. Identifying Infant Body Position from Inertial Sensors with Machine Learning: Which Parameters Matter?. Preprints 2024, 2024101387. https://doi.org/10.20944/preprints202410.1387.v1
Duda-Goławska, J.; Rogowski, A.; Laudańska, Z.; Żygierewicz, J.; Tomalski, P. Identifying Infant Body Position from Inertial Sensors with Machine Learning: Which Parameters Matter?. Preprints2024, 2024101387. https://doi.org/10.20944/preprints202410.1387.v1
APA Style
Duda-Goławska, J., Rogowski, A., Laudańska, Z., Żygierewicz, J., & Tomalski, P. (2024). Identifying Infant Body Position from Inertial Sensors with Machine Learning: Which Parameters Matter?. Preprints. https://doi.org/10.20944/preprints202410.1387.v1
Chicago/Turabian Style
Duda-Goławska, J., Jarosław Żygierewicz and Przemysław Tomalski. 2024 "Identifying Infant Body Position from Inertial Sensors with Machine Learning: Which Parameters Matter?" Preprints. https://doi.org/10.20944/preprints202410.1387.v1
Abstract
Efficient classification of body position is crucial for monitoring infants’ motor development. It may fast-track the early detection of developmental issues related not only to the acquisition of motor milestones but also to postural stability and movement patterns. In turn, this may facilitate and enhance opportunities for early intervention that are crucial for promoting healthy growth and development. Manual classification of human body position based on video recordings is labour-intensive, leading to the adoption of Inertial Motion Unit (IMU) sensors. IMUs measure acceleration, angular velocity, and magnetic field intensity, enabling automated classification of body position. Many research teams are currently employing supervised machine learning classifiers that utilise hand-crafted features for data segment classification. In this study, we used a longitudinal dataset of IMU recordings made in the lab in three different play activities of infants aged 4-12 months. The classification was conducted based on manually annotated video recordings. We found superior performance of the CatBoost Classifier over the Random Forest Classifier in the task of classifying five positions based on IMU sensor data from infants, yielding excellent classification accuracy of Supine (97.7%), Sitting (93.5%) and Prone (89.9%) position. Moreover, using data ablation experiments and analysing the SHAP values, the study assessed the importance of various groups of features from both time and frequency domains. The results highlight that both accelerometer and magnetometer data, especially their statistical characteristics, are critical contributors to improving the accuracy of body position classification.
Keywords
inertial motion sensors; human activity recognition; explainable machine learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
