Optical motion capture is an essential tool for the study and analysis of human movement. Currently, most manufacturers of motion-capture systems provide software applications for reconstructing the movement in real time, thus allowing for on-the-fly visualization. The captured kinematics can be later used as input data for a further musculoskeletal analysis. However, in advanced biofeedback applications, the results of said analysis, such as joint torques, ground-reaction forces, muscle efforts, and joint-reaction forces, are also required in real time.In this work, an extended Kalman filter (EKF) previously developed by the authors for real-time, whole-body motion capture and reconstruction is augmented with inverse dynamics and muscle-efforts optimization, enabling the calculation and visualization of the latter, along with joint-reaction forces, while capturing the motion.A modified version of the existing motion-capture algorithm provides the positions, velocities, and accelerations at every time step. Then, the joint torques are calculated by solving the inverse-dynamics problem, using force-plate measurements along with previously estimated body-segment parameters. Once the joint torques are obtained, an optimization problem is solved, in order to obtain the muscle forces that provide said torques while minimizing an objective function. This is achieved by a very efficient quadratic programming algorithm, thoroughly tuned for this specific problem.With this procedure, it is possible to capture and label the optical markers, reconstruct the motion of the model, solve the inverse dynamics, and estimate the individual muscle forces, all while providing real-time visualization of the results.

The worldwide popularisation of running as a sport and recreational practice has led to a high rate of musculoskeletal injuries, usually caused by a lack of knowledge about the most suitable running technique for each runner. This running technique is determined by a runner’s anthropometric body characteristics, dexterity and skill. Therefore, this study aims to develop a motion capture-based running analysis test on a treadmill called KeepRunning to obtain running patterns rapidly, which will aid coaches and clinicians in assessing changes in running technique considering changes in the study variables. Therefore, a review and proposal of the most representative events and variables of analysis in running was conducted to develop the KeepRunning test. Likewise, the minimal detectable change (MDC) in these variables was obtained using test–retest reliability to demonstrate the reproducibility and viability of the test, as well as the use of MDC as a threshold for future assessments. The test–retest consisted of 32 healthy volunteer athletes with a running training routine of at least 15 km per week repeating the test twice. In each test, clusters of markers were placed on the runners’ body segments using elastic bands and the volunteers’ movements were captured while running on a treadmill. In this study, reproducibility was defined by the intraclass correlation coefficient (ICC) and MDC, obtaining a mean value of ICC = 0.94 ± 0.05 for all variables and MDC = 2.73 ± 1.16° for the angular kinematic variables. The results obtained in the test–retest reveal that the reproducibility of the test was similar or better than that found in the literature. KeepRunning is a running analysis test that provides data from the involved body segments rapidly and easily interpretable. This data allows clinicians and coaches to objectively provide indications for runners to improve their running technique and avoid possible injury. The proposed test can be used in the future with inertial motion capture and other wearable technologies.

Currently, optical motion capture remains the gold standard for human motion analysis. This technique estimates the movement of a subject by tracking a set of markers attached to their skin, then using the captured trajectories to reconstruct the movement of an underlying rigid–body model. However, since the markers are not rigidly fixed to the skeleton, their motion relative to the bones can induce significant estimation errors, especially when using the computed accelerations to calculate the joint torques by solving the inverse dynamics. This paper presents an extended Kalman filter aimed at reducing such errors, through the incorporation of a complete multibody model of the human body. The method builds upon an existing Kalman filter based on a purely kinematic plant model, which uses only optical markers as sensors. The proposed observer adds the motor efforts and external reactions to the system states, and incorporates force plates into the set of sensors. This allows the measured ground reactions to directly affect the dynamics, rather than remaining an input to a subsequent inverse dynamics. The dynamic model can be added to the Kalman filter with multiple options and simplifications, which are here examined to find the combination with the best balance between efficiency and accuracy. The performance of the selected implementation is then compared with the existing kinematics–based observer. The results show that the proposed method improves the estimation of joint torques and reduces the influence of marker bounces on them, in exchange for a higher computational cost and an increased estimation delay.

Companies increasingly implement exoskeletons in their production lines to reduce musculoskeletal disorders. Studies have been conducted on the general ergonomic effects of exoskeletons in production environments; however, it remains challenging to predict the biomechanical effects these devices may have in specific jobs. This article proposes the parametric modeling of an active lumbar exoskeleton using the Forces ergonomic method, which calculates the ergonomic risk using motion capture in the workplace, considering the internal joint forces. The exoskeleton was studied to model it in the Forces method using a four-phase approach based on experimental observations (Phase 1) and objective data collection via motion capture with inertial sensors and load cells for lifting load movements. From the experimentation the angles of each body segment, the effort perceived by the user, and the activation conditions were obtained (Phase 2). After modeling development (Phase 3), the experimental results regarding the force and risk were evaluated obtaining differences between model and experimental data of 0.971 ± 0.171 kg in chest force and 1.983 ± 0.678% in lumbar risk (Phase 4). This approach provides a tool to evaluate the biomechanical effects of this device in a work task, offering a parametric and direct approximation of the effects prior to implementation.