Enhanced Design and Characterization of a Wearable IMU for High-Frequency Motion Capture

This paper presents the third-generation design of Bimu, a compact wearable inertial measurement unit (IMU) tailored for advanced human motion tracking. Building on prior iterations, Bimu R2 focuses on enhancing thermal stability, data integrity, and energy efficiency by integrating onboard memory, redesigning the power management system, and optimizing the communication interfaces. A detailed performance evaluation-including noise, bias, scale factor, power consumption, and drift-demonstrates the device's reliability and readiness for deployment in real-world applications ranging from clinical gait analysis to high-speed motion capture. The improvements introduced offer valuable insights for researchers and engineers developing robust wearable sensing solutions.

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Publication Details

Journal
Sensors
Published
2025-10-08
DOI
https://doi.org/10.3390/s25196224
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
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article

Enhanced Design and Characterization of a Wearable IMU for High-Frequency Motion Capture

Diego Álvarez, Juan C. Álvarez, Antonio M. López, Diego Valdés Tirado et al.
Sensors
Context-Aware Activity Recognition Systems
article

Enhanced Design and Characterization of a Wearable IMU for High-Frequency Motion Capture

Diego Álvarez, Juan C. Álvarez, Antonio M. López, Diego Valdés Tirado, Gonzalo García Carro
article en

Abstract

This paper presents the third-generation design of Bimu, a compact wearable inertial measurement unit (IMU) tailored for advanced human motion tracking. Building on prior iterations, Bimu R2 focuses on enhancing thermal stability, data integrity, and energy efficiency by integrating onboard memory, redesigning the power management system, and optimizing the communication interfaces. A detailed performance evaluation-including noise, bias, scale factor, power consumption, and drift-demonstrates the device's reliability and readiness for deployment in real-world applications ranging from clinical gait analysis to high-speed motion capture. The improvements introduced offer valuable insights for researchers and engineers developing robust wearable sensing solutions.

SensorsVol. 25(19)
Universidad de Oviedo (ES)
Openalex Percentile: Top 72%
Context-Aware Activity Recognition Systems
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