The utilization of inertial measurement units as wearable sensors is proliferating across various domains, such as health care, sports, and rehabilitation. This expansion has produced a market of devices tailored to accommodate very specific ranges of operational demands. Simultaneously, this growth is creating opportunities for the development of a new class of devices more oriented towards general-purpose use and capable of capturing both high-frequency signals for short-term, event-driven motion analysis and low-frequency signals for extended monitoring. For such a design, which combines flexibility and low cost, a rigorous evaluation of the device in terms of deviation, noise levels, and precision is essential. This evaluation is crucial for identifying potential improvements and refining the design accordingly, yet it is rarely addressed in the literature. This paper presents the development process of such a device. The results of the design process demonstrate acceptable performance in optimizing energy consumption and storage capacity while highlighting the most critical optimizations needed to advance the device towards the goal of a smart, general-purpose unit for human motion monitoring.

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.

The utilization of inertial measurement units (IMUs) as wearable sensors is proliferating 1 across various domains such as healthcare, sports, and rehabilitation. This expansion is driven by 2 the continuous improvement of sensor technology to accommodate specific operational demands. 3 This paper introduces the development of a configurable wearable device capable of capturing both 4 high-frequency and low-frequency signals, tailored for a wide range of monitoring applications.Our 5 focus is on a comprehensive evaluation of the device in terms of deviation, noise levels, and precision. 6 We present initial findings that demonstrate the device’s robust performance, and put on the spotlight 7 the most critical optimizations needed to improve the device to reach the goal of a smart general-use 8 unit for human motion monitoring.

This study introduces a magnetometer-free, custom-built inertial measurement unit (IMU) combined with an open-source sensor fusion algorithm and provides a systematic experimental comparison against commercial IMUs that integrate proprietary hardware and closed-source filtering software. The main innovation lies in demonstrating that an open and fully reproducible hardware and software architecture can achieve orientation estimation accuracy comparable to that of state-of-the-art commercial solutions under both static and dynamic conditions. Experimental results show that the proposed system achieves a mean orientation error of 2.10% (0.11°) while consistently outperforming proprietary filtering approaches in static scenarios and exhibiting competitive variability during dynamic motion. Furthermore, the study presents a comprehensive benchmarking framework that analyzes the influence of sensor noise characteristics and filtering strategy on estimation performance across multiple hardware and algorithm configurations. These findings establish that custom-built IMUs paired with open-source sensor fusion algorithms constitute a reliable, cost-effective, and transparent alternative to commercial hardware and software solutions for biomedical and engineering applications.

This study systematically evaluates the impact of sensor configuration, body location, classification granularity, and model choice on inertial-based human activity recognition in a laboratory dataset aligned with the Spanish IMPaCT cohort design. Data were collected from 85 participants instrumented with thigh-, wrist-, and hip-mounted inertial measurement units over a structured protocol of 13 semi-structured daily activities, a resting phase and a structured activity. After manual correction of timestamp drift, signals were segmented into overlapping 10-s windows and analyzed using convolutional neural networks, Random Forest, and XGBoost classifiers.Two classification targets were defined: fine-grained recognition of 15 laboratory-controlled activities and coarse-grained classification into four MET-based intensity levels. Results showed that classification granularity is the primary determinant of performance ($F=224.85$, p-value = $2.304\times {10}^{-13}$ through the analysis of variance of the F1-score), with intensity-level classification substantially outperforming fine-grained activity recognition. Sensor configuration, model type, and body location also significantly influenced classification outcomes. Wrist-mounted sensors achieved the highest overall F1-scores. Incorporating gyroscope-derived features consistently improved performance across configurations, and feature importance analysis confirmed their substantial contribution. These findings, derived from models developed under controlled laboratory conditions, provide practical guidance for the design of wearable sensing protocols and modeling strategies in large-scale population-based studies, and support their extension to everyday physical activity, laying the foundation for future real-world applications.