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Classification of Bones

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IMU-Based Pelvic Rotation Detection: A Novel Dataset, Benchmark Classifiers, and Sensor Placement Optimization.

Meixing Liao, Eva Libeert, Joeri Van Cauwelaert

IEEE Journal of Biomedical and Health Informatics

|February 17, 2026

Summary

This summary is machine-generated.

This study introduces a new dataset and machine learning approach to detect pelvic rotations (PRs), crucial for preventing low back pain (LBP) caused by sedentary behavior. A two-sensor system shows promising results for LBP management.

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Area of Science:

Biomedical Engineering
Rehabilitation Science
Wearable Technology

Background:

Prolonged sedentary behavior is a significant public health issue linked to low back pain (LBP), a leading cause of global disability.
Monitoring pelvic rotations (PRs) is vital for postural control, LBP prevention, and management, yet detection methods and datasets are limited.

Purpose of the Study:

To investigate the feasibility of using inertial measurement units (IMUs) and machine learning (ML) for recognizing pelvic rotations (PRs).
To introduce a novel, publicly available dataset for PR detection across various activities.
To optimize sensor placement for a practical, minimal-sensor solution for LBP management.

Main Methods:

Development of a new dataset with over 15 hours of high-resolution IMU data from 39 participants with sedentary lifestyles.
Benchmarking six classical ML classifiers for PR recognition, focusing on XGBoost.
Systematic optimization of sensor placement, evaluating configurations from full-body to minimal sensor setups.

Main Results:

XGBoost achieved high performance in PR recognition, with weighted F1 scores of 0.988 (2-class) and 0.895 (8-class).
A two-sensor configuration (pelvis and left upper leg) achieved performance comparable to a 17-sensor system.
The optimal two-sensor setup reached a weighted F1 score of 0.872 in an 8-class classification task.

Conclusions:

The study provides a valuable public dataset and a robust ML baseline for PR recognition.
A minimal two-sensor configuration offers a practical and effective solution for real-world wearable systems.
This research facilitates the development of technologies to reduce sedentary postures and improve LBP management.