Computer Model Based on an Asynchronous BLE 5.0 IMU Sensor Network for Biomechanical Applications

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.