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

The acquisition, processing, and monitoring of biomechanical variables in dynamic environments require sensor network architectures capable of handling high concurrency and large data volumes. This study aims to develop, validate, and deploy a robust asynchronous network architecture of Inertial Measurement Units (IMUs) utilizing Bluetooth Low Energy (BLE) 5.0 for real-time biomechanical signal acquisition, overcoming the range, speed, and stability limitations of prior implementations. A network of six IMUs was implemented, with communication managed by a hybrid Python 3.10-LabVIEW 2022 Q3 framework. This architecture ensures concurrent, asynchronous data acquisition while maintaining stable sensor interconnection through virtual port emulation. System evaluation demonstrated superior technical performance, exhibiting high acquisition efficiency (close to 100%) and data loss below ±2% across 75 assessments per sensor. These assessments were obtained by evaluating the posture of 25 participants during three postural experiments, with a maximum indoor range of 40 m and an outdoor range of 105 m, validating the system's scalability and robustness for motion capture. The approach was applied in a case study using a Fuzzy Inference System (FIS) to assess the upper limb via the Rapid Upper Limb Assessment (RULA) method. The system successfully quantified the temporal distribution of injury risk bilaterally, overcoming the limitations of observational methods and providing objective metrics crucial for occupational health in seated tasks.