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 = 2.304×10−13 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.

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 degrees) 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 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.

This study presents a simple and accurate method for estimating joint angles with one degree of freedom using two inertial measurement units. The approach leverages quaternion-based computations and eigenvalue decomposition to determine the rotation axes, requiring only a single calibration movement, and was validated through simulation and real-case experiments. In simulations, the proposed approach achieved a mean squared error of 1.1838 degrees, demonstrating robustness to sensor misalignment and varying noise levels. A real-world validation was conducted on a healthy subject performing elbow flexion-extension, using Xsens DOT units at 60 Hz and an OptiTrack motion capture system at 100 Hz, with sensor signals interpolated for direct comparison. The results showed strong agreement between both methods, with a root mean squared error of 4.8922 degrees, ranging from 2.6995 to 5.9759 degrees during resting phases. The method’s efficiency and minimal calibration requirements make it ideal for biomechanics, rehabilitation, and robotics. Its calibration-by-motion approach is particularly useful for applications such as cycling-based knee analysis, where the motion of interest inherently serves as calibration, although elbow joint studies may require a separate calibration step to avoid interference from shoulder movement.

This study evaluates the use of non-commercial, custom-built inertial measurement units (IMUs) for static orientation estimation, comparing them with a commercial solution. Three IMUs were analyzed: the custom-built IMU (s-III), Xsens DOT (s-I), and MATRIX IMU (s-II), using two orientation estimation methods: Proprietary Filter (PF) and Complementary Filter (CF). The results show that the custom-built IMU exhibited the lowest electronic noise, while the Xsens and MATRIX IMUs had higher noise levels due to aging and hardware characteristics. All IMUs presented noise below 3 mg for accelerometers and 0.2 degrees/s for gyroscopes. In terms of orientation accuracy, the S-C system (custom IMU with CF) had a mean error of 2.10%, comparable to the commercial solution, which achieved 0.61%. The S-A system (Xsens with CF) showed a mean error of 0.45%+/- 0.15%. The CF algorithm outperformed PF, particularly in static conditions, due to faster stabilization. However, the MATRIX IMU (S-B) had the highest error (2.88%) due to its higher noise levels. The findings suggest that non-commercial IMUs, combined with open-source algorithms, can offer accurate orientation estimates, providing a cost-effective alternative for biomedical and engineering applications.

The development of wearable devices capable of capturing high-frequency human motion has been a focus of research in recent years. In this work, we propose a new approach to improve the performance of a flexible PCB for IMU sensor mounting, by integrating a 0.3 mm steel stiffener in the PCB. A LDO circuit is used to externalize the battery management system (BMS) for 3V voltage regulation, reducing energy losses and overheating, making the design more efficient and reliable in the long term, while the internal microcontroller can handle battery monitoring and status control during operation. The STNS01PUR provides a more compact solution but at the cost of higher component density and potential thermal challenges, especially during charging. The first results show an improvement in all intended areas from previous reported designs.

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.

Over the last few years, the use of automatic performance measurement systems has become relatively common among professional kayak paddlers. Although these systems are not able to perform a complete evaluation of the athlete, they can extract relevant parameters about the training result. Thus, different devices have been developed to measure relevant information such as paddling rhythm. The high price of these devices limits their use for amateur athletes. To overcome this limitation, the validity of the measurements provided by different smartphones, obtained from the integrated inertial sensors, has been analyzed. This study has compared the performance of different phones versus professional Movella DOT inertial measurement devices. The article presents a comparison of the results obtained and proves that smartphones produce results equivalent to those of conventional sensors.

Ground contact time (GCT) is one of the most relevant factors when assessing running performance in sports practice. In recent years, inertial measurement units (IMUs) have been widely used to automatically evaluate GCT, since they can be used in field conditions and are friendly and easy to wear devices. In this paper we describe the results of a systematic search, using the Web of Science, to assess what reliable options are available to GCT estimation using inertial sensors. Our analysis reveals that estimation of GCT from the upper body (upper back and upper arm) has rarely been addressed. Proper estimation of GCT from these locations could permit an extension of the analysis of running performance to the public, where users, especially vocational runners, usually wear pockets that are ideal to hold sensing devices fitted with inertial sensors (or even using their own cell phones for that purpose). Therefore, in the second part of the paper, an experimental study is described. Six subjects, both amateur and semi-elite runners, were recruited for the experiments, and ran on a treadmill at different paces to estimate GCT from inertial sensors placed at the foot (for validation purposes), the upper arm, and upper back. Initial and final foot contact events were identified in these signals to estimate the GCT per step, and compared to times estimated from an optical MOCAP (Optitrack), used as the ground truth. We found an average error in GCT estimation of 0.01 s in absolute value using the foot and the upper back IMU, and of 0.05 s using the upper arm IMU. Limits of agreement (LoA, 1.96 times the standard deviation) were [-0.01 s, 0.04 s], [-0.04 s, 0.02 s], and [0.0 s, 0.1 s] using the sensors on the foot, the upper back, and the upper arm, respectively.

This paper presents a complete design and validation of a specific case of a Human-Robot collaborative workspace consisting of a robot arm and a human operator working in a pick & place task in close proximity. The human motion monitoring is made with IMU-based wearables and optical markers, and the robot motion planning method considers safety distance criteria as prescribed in ISO/TS 15066. This case study is meant to illustrate the design of collaborative environments by using the Model-based Design approach. The goal is to systematically address the problems of safety assurance and performance optimization, allowing the use of optimization and machine learning approaches.

The short-term prediction of a person’s trajectory during normal walking becomes necessary in many environments shared by humans and robots. Physics-based approaches based on Newton’s laws of motion seem best suited for short-term predictions, but the intrinsic properties of human walking conflict with the foundations of the basic kinematical models compromising their performance. In this paper, we propose a short-time prediction method based on gait biomechanics for real-time applications. This method relays on a single biomechanical variable, and it has a low computational burden, turning it into a feasible solution to implement in low-cost portable devices. We evaluate its performance from an experimental benchmark where several subjects walked steadily over straight and curved paths. With this approach, the results indicate a performance good enough to be applicable to a wide range of human–robot interaction applications.

The use of soft and flexible materials in rehabilitation support systems based on robotic exoskeletons plays a crucial role in ergonomics, reducing weight and complexity. However, the selection of these materials can have mechanical implications that affect the performance of the exoskeleton in the chosen application. This paper proposes a testing protocol to assess the mechanical and structural features of a prototype upper limb robotic exoskeleton using an optical motion capture system as the primary measurement instrument. In addition to the protocol, a literature review is presented, laying the ground for the theoretical development of the mechanical evaluation for prototypes. Also, the materials used concerning the evaluation technologies required for data collection and assessment of the devices are described. The development and future application of this protocol will enable the identification of the relevant motion parameters on the exoskeleton prototype, the changes that its structure may experience due to the use of the flexible materials, and the effectiveness of the mechanical support provided in the upper limb. Finally, the proposed protocol can be extended to other types of robotic exoskeletons built with similar materials, regardless of their final application, extending its applicability to other study cases.

In the context of human–robot collaborative shared environments, there has been an increase in the use of optical motion capture (OMC) systems for human motion tracking. The accuracy and precision of OMC technology need to be assessed in order to ensure safe human–robot interactions, but the accuracy specifications provided by manufacturers are easily influenced by various factors affecting the measurements. This article describes a new methodology for the metrological evaluation of a human–robot collaborative environment based on optical motion capture (OMC) systems. Inspired by the ASTM E3064 test guide, and taking advantage of an existing industrial robot in the production cell, the system is evaluated for mean error, error spread, and repeatability. A detailed statistical study of the error distribution across the capture area is carried out, supported by a Mann–Whitney U-test for median comparisons. Based on the results, optimal capture areas for the use of the capture system are suggested. The results of the proposed method show that the metrological characteristics obtained are compatible and comparable in quality to other methods that do not require the intervention of an industrial robot.

In a setting shared by humans and machines, the short-term prediction in real time of a person's position during a walking displacement becomes necessary from a real and perceived safety point of view. Classical kinematical models inspired by the Newton's laws based on the actual position and orientation of the subject have been proposed to estimate her future position. However, we have found that the natural alternation of the step during human gait forces fluctuations in the position and orientation of the walking subject that limit the predictive capabilities of the kinematical models. We check whether filtering approaches may be valid to remove the fluctuations in real time. As an alternative, we propose to exploit biomechanical factors of human gait, in particular to combine the sampled position and orientation of the subject with her hip orientation to correct these fluctuations thus permitting the models to be applied in a real-time prediction context.

This article describes a new methodology for the metrological evaluation of a human-robot collaborative environment based on optical motion capture (OMC) systems. By taking advantage of the existing industrial robot in the production cell, the workspace calibration procedure can be automatized, reducing the need of human intervention. The method is inspired on the ASTM E3064 test guide, and the results presented show that the metrological characteristics so obtained are compatible and comparable in quality to the ones with the manual procedure.

Prediction of walking turns allows to improve human factors such as comfort and perceived safety in human-robot interaction. The current state-of-the-art suggests that upper body kinematics can be used for that purpose and contains evidence about the reliability and the quantitative anticipation that can be expected from different variables. However, the experimental methodology has not been consistent throughout the different works and the related data has not always been given in an explicit form, with different studies containing partial, complementary or even contradictory results. In this paper, with the purpose of providing a uniform view of the topic that can trigger new developments in the field, we performed a systematic review of the relevant literature addressing three main questions: (i) Which upper body kinematic variables permit to anticipate a walking turn? (ii) How long in advance can we anticipate the turn from them? (iii) What is the expected contribution of walking turn prediction systems from upper body kinematics for human-robot interaction? We have found that head yaw was the most reliable kinematical variable from the upper body to predict walking turns about 200ms. Trunk roll anticipates walking turns by a similar amount of time, but with less reliability. Both approaches may benefit human-robot interaction in close proximity, helping the robot to exhibit appropriate proxemic behavior interacting at intimate, personal or social distances. From the point of view of safety, they have to be considered with caution. Trunk yaw is not valid to anticipate turns. Gaze Yaw seems to be the earliest predictor, although existing evidence is still inconclusive.

The present article describes the creation and operation of a device for biomechanical analysis, which is portable, precise, non-invasive, and which allows us to autonomously obtain the position, orientation and measurement of the articular amplitude of two body segments connected by a joint. The above is carried out with data obtained from a number of inertial and magnetic microsensors. The device, through the application of a series of methods, allows the recording of the movements made by a person while they go about their daily activities without the need of including a computing system that operates it. This can be done in real time or delayed-mode, and for long periods of time, depending on the battery charge of the device.

In human motion science, accelerometers are used as linear distance sensors by attaching them to moving body parts, with their measurement axes its measurement axis aligned in the direction of motion. When double integrating the raw sensor data, multiple error sources are also integrated integrated as well, producing inaccuracies in the final position estimation which increases fast with the integration time. In this paper, we make a systematic and experimental comparison of different methods for position estimation, with different sensors and in different motion conditions. The objective is to correlate practical factors that appear in real applications, such as motion mean velocity, path length, calibration method, or accelerometer noise level, with the quality of the estimation. The results confirm that it is possible to use accelerometers to estimate short linear displacements of the body with a typical error of around 4.5% in the general conditions tested in this study. However, they also show that the motion kinematic conditions can be a key factor in the performance of this estimation, as the dynamic response of the accelerometer can affect the final results. The study lays out the basis for a better design of distance estimations, which are useful in a wide range of ambulatory human motion monitoring applications.

Usual human motion capture systems are designed to work in controlled laboratory conditions. For occupational health, instruments that can measure during normal daily life are essential, as the evaluation of the workers' movements is a key factor to reduce employee injury- and illness-related costs. In this paper, we present a method for joint angle measurement, combining inertial sensors (accelerometers and gyroscopes) and magnetic sensors. This method estimates wrist flexion, wrist lateral deviation, elbow flexion, elbow pronation, shoulder flexion, shoulder abduction and shoulder internal rotation. The algorithms avoid numerical integration of the signals, which allows for long-time estimations without angle estimation drift. The system has been tested both under laboratory and field conditions. Controlled laboratory tests show mean estimation errors between 0.06° and of 1.05°, and standard deviation between 2.18° and 9.20°. Field tests seem to confirm these results when no ferromagnetic materials are close to the measurement system.