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Updated: Jan 9, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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Computer Model Based on an Asynchronous BLE 5.0 IMU Sensor Network for Biomechanical Applications.

Juan Antonio Mora-Sánchez1, Luis Pastor Sánchez-Fernández1, Diana Lizet González-Baldovinos1

  • 1Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz S/N, Nueva Industrial Vallejo, Gustavo A. Madero, Mexico City 07738, Mexico.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new wireless sensor network using Inertial Measurement Units (IMUs) and Bluetooth Low Energy (BLE) 5.0 for accurate biomechanical data collection. The system enhances real-time motion capture and occupational health assessments.

Keywords:
Bluetooth Low Energy 5.0IMUsasynchronous data acquisitionfuzzy inference systemrapid upper limb assessment

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Area of Science:

  • Biomechanics
  • Sensor Networks
  • Human-Computer Interaction

Background:

  • Dynamic environments necessitate advanced sensor networks for biomechanical data acquisition.
  • Existing systems face limitations in range, speed, and stability.

Purpose of the Study:

  • Develop and validate a robust asynchronous network architecture using Inertial Measurement Units (IMUs).
  • Utilize Bluetooth Low Energy (BLE) 5.0 to overcome prior implementation limitations.
  • Enable real-time biomechanical signal acquisition for enhanced motion capture and health assessment.

Main Methods:

  • Implemented a network of six IMUs with a hybrid Python 3.10-LabVIEW 2022 Q3 framework.
  • Ensured concurrent, asynchronous data acquisition via virtual port emulation for stable sensor interconnection.
  • Validated system performance through 75 assessments involving 25 participants in postural experiments.

Main Results:

  • Achieved high acquisition efficiency (near 100%) with data loss below ±2%.
  • Demonstrated a maximum indoor range of 40 m and outdoor range of 105 m.
  • Successfully applied the system in a case study using a Fuzzy Inference System (FIS) for Rapid Upper Limb Assessment (RULA).

Conclusions:

  • The developed architecture offers superior scalability and robustness for motion capture applications.
  • The system provides objective, quantifiable metrics for occupational health, particularly for seated tasks.
  • Overcame limitations of observational methods in assessing injury risk and upper limb posture.