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

Abstract

Highlights: What are the main findings? Inertial Measurement Unit network using a hybrid asynchronous architecture with real-time data acquisition. BLE 5.0 communication with a net throughput of 1.4 Mbps and a range of 105 m. Fuzzy inference models using the RULA method, converting biomechanical data into a risk of injury score. Models that quantify the temporal distribution of injury risk. What is the implication of the main finding? The validated BLE 5.0 asynchronous network provides the robustness and scalability needed to replace restrictive, high-cost systems, enabling reliable data acquisition for complex motion analysis. The fuzzy RULA model addresses subjectivity and discretization issues in the traditional approach, offering an objective, higher-resolution metric suitable for real-time monitoring and informed decision-making. Quantifying exposure time provides a key metric for occupational health, enabling the deployment of personalized interventions and data-driven preventive strategies. 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.