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Concept: Motion capture


Background Current inertial motion capture systems are rarely used in biomedical applications. The attachment and connection of the sensors with cables is often a complex and time consuming task. Moreover, it is prone to errors, because each sensor has to be attached to a predefined body segment. By using wireless inertial sensors and automatic identification of their positions on the human body, the complexity of the set-up can be reduced and incorrect attachments are avoided.We present a novel method for the automatic identification of inertial sensors on human body segments during walking. This method allows the user to place (wireless) inertial sensors on arbitrary body segments. Next, the user walks for just a few seconds and the segment to which each sensor is attached is identified automatically.MethodsWalking data was recorded from ten healthy subjects using an Xsens MVN Biomech system with full-body configuration (17 inertial sensors). Subjects were asked to walk for about 6 seconds at normal walking speed (about 5 km/h). After rotating the sensor data to a global coordinate frame with x-axis in walking direction, y-axis pointing left and z-axis vertical, RMS, mean, and correlation coefficient features were extracted from x-, y- and z-components and magnitudes of the accelerations, angular velocities and angular accelerations. As a classifier, a decision tree based on the C4.5 algorithm was developed using Weka (Waikato Environment for Knowledge Analysis).Results and conclusions After testing the algorithm with 10-fold cross-validation using 31 walkingtrials (involving 527 sensors), 514 sensors were correctly classified (97.5%). When a decision tree for alower body plus trunk configuration (8 inertial sensors) was trained andtested using 10-fold cross-validation, 100% of the sensors were correctly identified. This decision tree wasalso tested on walking trials of 7 patients (17 walking trials) after anterior cruciate ligamentreconstruction, which also resulted in 100% correct identification, thus illustrating the robustness of themethod.

Concepts: Human body, Classical mechanics, Walking, Motion capture, Decision tree learning


BACKGROUND: In the last years, several methods and devices have been proposed to record the human mandibular movements, since they provide quantitative parameters that support the diagnosis and treatment of temporomandibular disorders. The techniques currently employed suffer from a number of drawbacks including high price, unnatural to use, lack of support for real-time analysis and mandibular movements recording as a pure rotation. In this paper, we propose a specialized optical motion capture system, which causes a minimum obstruction and can support 3D mandibular movement analysis in real-time. METHODS: We used three infrared cameras together with nine reflective markers that were placed at key points of the face. Some classical techniques are suggested to conduct the camera calibration and three-dimensional reconstruction and we propose some specialized algorithms to automatically recognize our set of markers and track them along a motion capture session. RESULTS: To test the system, we developed a prototype software and performed a clinical experiment in a group of 22 subjects. They were instructed to execute several movements for the functional evaluation of the mandible while the system was employed to record them. The acquired parameters and the reconstructed trajectories were used to confirm the typical function of temporomandibular joint in some subjects and to highlight its abnormal behavior in others. CONCLUSIONS: The proposed system is an alternative to the existing optical, mechanical, electromagnetic and ultrasonic-based methods, and intends to address some drawbacks of currently available solutions. Its main goal is to assist specialists in diagnostic and treatment of temporomandibular disorders, since simple visual inspection may not be sufficient for a precise assessment of temporomandibular joint and associated muscles.

Concepts: Mandible, Joint, Motion capture, Temporomandibular joint, Temporomandibular joint disorder, Camera, Masseteric nerve


The paper presents a multifunctional joint sensor with measurement adaptability for biological engineering applications, such as gait analysis, gesture recognition, etc. The adaptability is embodied in both static and dynamic environment measurements, both of body pose and in motion capture. Its multifunctional capabilities lay in its ability of simultaneous measurement of multiple degrees of freedom (MDOF) with a single sensor to reduce system complexity. The basic working mode enables 2DOF spatial angle measurement over big ranges and stands out for its applications on different joints of different individuals without recalibration. The optional advanced working mode enables an additional DOF measurement for various applications. By employing corrugated tube as the main body, the sensor is also characterized as flexible and wearable with less restraints. MDOF variations are converted to linear displacements of the sensing elements. The simple reconstruction algorithm and small outputs volume are capable of providing real-time angles and long-term monitoring. The performance assessment of the built prototype is promising enough to indicate the feasibility of the sensor.

Concepts: Measurement, Joint, Classical mechanics, Angle, Motion capture, User interface, Reconstruction algorithm, Gait analysis


Inertial measurement of motion with Attitude and Heading Reference Systems (AHRS) is emerging as an alternative to 3D motion capture systems in biomechanics. The objectives of this study are: 1) to describe the absolute and relative accuracy of multiple units of commercially available AHRS under various types of motion; and 2) to evaluate the effect of motion velocity on the accuracy of these measurements.

Concepts: Medical statistics, Absolute, Measurement, Psychometrics, Units of measurement, Motion capture, Special relativity, God


Citizen science enables volunteers to contribute to scientific projects, where massive data collection and analysis are often required. Volunteers participate in citizen science activities online from their homes or in the field and are motivated by both intrinsic and extrinsic factors. Here, we investigated the possibility of integrating citizen science tasks within physical exercises envisaged as part of a potential rehabilitation therapy session. The citizen science activity entailed environmental mapping of a polluted body of water using a miniature instrumented boat, which was remotely controlled by the participants through their physical gesture tracked by a low-cost markerless motion capture system. Our findings demonstrate that the natural user interface offers an engaging and effective means for performing environmental monitoring tasks. At the same time, the citizen science activity increases the commitment of the participants, leading to a better motion performance, quantified through an array of objective indices. The study constitutes a first and necessary step toward rehabilitative treatments of the upper limb through citizen science and low-cost markerless optical systems.

Concepts: Obesity, Physical exercise, Exercise, Motivation, Rhabdomyolysis, Motion capture, User interface, Physical fitness


Motion capture experiment results are often used as a means of validation for digital human simulations. Motion capture results are marker positions and joint centers in Cartesian space. However, joint angles are more intuitive and easy to understand compared to marker or joint center positions. Posture reconstruction algorithms are used to map Cartesian space to joint space by re-creating experimental postures with simulation models. This allows for direct comparison between the experimental results and digital human simulations. Besides the inherent experimental errors from motion capture system, one source of simulation error is the determination of the link lengths to be used in the simulation model. The link length errors can propagate through all simulation results. Therefore, it is critical to eliminate the link length errors. The objective of this study is to determine the best method of determining link lengths for the simulation model to best match the model to the experiment results containing errors. Specifically, the way that the link lengths are calculated in the posture reconstruction process from motion capture data has a significant effect on the recreated posture for the simulation model. Three link length calculation methods (experimental-average method, trial-specific method, and T-pose method) are developed and compared to a benchmark method (frame-specific method) for calculating link lengths. The results indicate that using the trial-specific method is the most accurate method when referring to calculating frame-specific link lengths.

Concepts: Algorithm, Mathematics, Dimension, Experiment, Computer graphics, Computer simulation, Motion capture, Calculation


The current study aimed to comparatively examine the effects of minimalist, maximalist and conventional footwear on the loads experienced by the patellofemoral joint during running. Twenty male participants ran over a force platform at 4.0 m.s-1. Lower limb kinematics were collected using an 8 camera motion capture system allowing patellofemoral kinetics to be quantified using a musculoskeletal modelling approach. Differences in patellofemoral kinetic parameters were examined using one-way repeated measures ANOVA. The results showed the peak patellofemoral force and pressure were significantly larger in conventional (4.70 ± 0.91 BW & 13.34 ± 2.43 MPa) and maximalist (4.74 ± 0.88 BW & 13.59 ± 2.63 MPa) compared to minimalist footwear (3.87 ± 1.00 BW & 11.59 ± 2.63 MPa). It was also revealed that patellofemoral force per mile was significantly larger in conventional (246.81 ± 53.21 BW) and maximalist (251.94 ± 59.17 BW) as compared to minimalist (227.77 ± 58.60 BW) footwear. As excessive loading of the patellofemoral joint has been associated with the aetiology of patellofemoral pain symptoms, the current investigation indicates that minimalist footwear may be able reduce runners susceptibility to patellofemoral disorders.

Concepts: Pressure, Classical mechanics, Analysis of variance, The Current, Motion capture, Limb, Kinetics, Kinetic


: The purpose of this study was to analyze the transference of increased passive hip ROM and core endurance to functional movement. 24 healthy young men with limited hip mobility were randomly assigned to 4 intervention groups: 1)Stretching; 2)Stretching plus hip/spine disassociation exercises; 3)Core endurance; 4)Control. Previous work has documented the large increase in passive ROM and core endurance that was attained over the 6 week interventions, but whether these changes transferred to functional activities was unclear.Four dynamic activities were analyzed before and after the 6 week interventions: active standing hip extension, lunge, a standing twist/reach maneuver, and exercising on an elliptical trainer. A Vicon motion capture system collected body segment kinematics, with hip and lumbar spine angles subsequently calculated in Visual 3D. Repeated measures ANOVAs determined group effects on various hip and spine angles, with paired t-tests on specific pre/post pairs.Despite the large increases in passive hip ROM, there was no evidence of increased hip ROM utilized during functional movement testing. Similarly, the only significant change in lumbar motion was a reduction in lumbar rotation during the active hip extension manoeuvre (p< 0.05).These results indicate that changes in passive ROM or core endurance do not automatically transfer to changes in functional movement patterns. This implies that training and rehabilitation programs may benefit from an additional focus on 'grooving' new motor patterns if new found movement range is to be utilized.

Concepts: Lumbar vertebrae, Physical exercise, Intervention, Motion capture, Active, Segmentation, Core, Elliptical trainer


Ground reaction forces and moments (GRF&M) are important measures used as input in biomechanical analysis to estimate joint kinetics, which often are used to infer information for many musculoskeletal diseases. Their assessment is conventionally achieved using laboratory-based equipment that cannot be applied in daily life monitoring. In this study, we propose a method to predict GRF&M during walking, using exclusively kinematic information from fully-ambulatory inertial motion capture (IMC). From the equations of motion, we derive the total external forces and moments. Then, we solve the indeterminacy problem during double stance using a distribution algorithm based on a smooth transition assumption. The agreement between the IMC-predicted and reference GRF&M was categorized over normal walking speed as excellent for the vertical (ρ = 0.992, rRMSE = 5.3%), anterior (ρ = 0.965, rRMSE = 9.4%) and sagittal (ρ = 0.933, rRMSE = 12.4%) GRF&M components and as strong for the lateral (ρ = 0.862, rRMSE = 13.1%), frontal (ρ = 0.710, rRMSE = 29.6%), and transverse GRF&M (ρ = 0.826, rRMSE = 18.2%). Sensitivity analysis was performed on the effect of the cut-off frequency used in the filtering of the input kinematics, as well as the threshold velocities for the gait event detection algorithm. This study was the first to use only inertial motion capture to estimate 3D GRF&M during gait, providing comparable accuracy with optical motion capture prediction. This approach enables applications that require estimation of the kinetics during walking outside the gait laboratory.

Concepts: Force, Classical mechanics, Acceleration, Velocity, Kinematics, Ground reaction force, Motion capture, Inertia


Human movement analysis has become easier with the wide availability of motion capture systems. Inertial sensing has made it possible to capture human motion without external infrastructure, therefore allowing measurements in any environment. As high-quality motion capture data is available in large quantities, this creates possibilities to further simplify hardware setups, by use of data-driven methods to decrease the number of body-worn sensors. In this work, we contribute to this field by analyzing the capabilities of using either artificial neural networks (eager learning) or nearest neighbor search (lazy learning) for such a problem. Sparse orientation features, resulting from sensor fusion of only five inertial measurement units with magnetometers, are mapped to full-body poses. Both eager and lazy learning algorithms are shown to be capable of constructing this mapping. The full-body output poses are visually plausible with an average joint position error of approximately 7 cm, and average joint angle error of 7 ∘ . Additionally, the effects of magnetic disturbances typical in orientation tracking on the estimation of full-body poses was also investigated, where nearest neighbor search showed better performance for such disturbances.

Concepts: Artificial intelligence, Machine learning, Units of measurement, Neural network, Artificial neural network, Motion capture, Unsupervised learning, Nearest neighbor search