Optical Motion Capture System Research Articles

Kinematic Contribution to Javelin Velocity at Different Run-Up Velocities in Male Athletes.

In javelin training, many athletes improve their throwing technique by throwing from a slower run-up velocity than in competitions. However, whether the acquisition of javelin velocity in throwing from a slower run-up velocity is the same as in full run-up throwing is unclear. The purpose of this study was to clarify the differences in the contribution of each movement to the javelin velocity caused by changes in the run-up velocity within an individual. Twelve collegiate male javelin throwers were included in this study. Athletes performed two types of throws: one-cross throwing (Cross) and full run-up throwing (Run). The coordinates of reflective markers attached to the thrower's body and the javelin were recorded using an optical motion capture system. The percentage contribution of each joint movement to the javelin velocity was calculated and compared between Cross and Run. Cross had a lower contribution of trunk forward lean to forward and upward javelin velocities compared to Run. On the other hand, Cross had a higher contribution of trunk counter-clockwise rotation to forward and upward javelin velocities than Run. These results suggest that as the velocity of run-up changes within an individual, the acquisition of javelin velocity also changes.

Dynamic segmental kinematics of the lumbar spine during diagnostic movements.

Background: In vivo measurements of segmental-level kinematics are a promising avenue for better understanding the relationship between pain and its underlying, multi-factorial basis. To date, the bulk of the reported segmental-level motion has been restricted to single plane motions. Methods: The present work implemented a novel marker set used with an optical motion capture system to non-invasively measure dynamic, 3D in vivo segmental kinematics of the lower spine in a laboratory setting. Lumbar spinal kinematics were measured for 28 subjects during 17 diagnostic movements. Results: Overall regional range of motion data and lumbar angular velocity measurement were consistent with previously published studies. Key findings from the work included measurement of differences in ascending versus descending segmental velocities during functional movements and observations of motion coupling paradigms in the lumbar spinal segments. Conclusion: The work contributes to the task of establishing a baseline of segmental lumbar movement patterns in an asymptomatic cohort, which serves as a necessary pre-requisite for identifying pathological and symptomatic deviations from the baseline.

Inertial Measurement Unit Sensor-to-Segment Calibration Comparison for Sport-Specific Motion Analysis.

Wearable inertial measurement units (IMUs) can be utilized as an alternative to optical motion capture as a method of measuring joint angles. These sensors require functional calibration prior to data collection, known as sensor-to-segment calibration. This study aims to evaluate previously described sensor-to-segment calibration methods to measure joint angle range of motion (ROM) during highly dynamic sports-related movements. Seven calibration methods were selected to compare lower extremity ROM measured using IMUs to an optical motion capture system. The accuracy of ROM measurements for each calibration method varied across joints and sport-specific tasks, with absolute mean differences between IMU measurement and motion capture measurement ranging from <0.1° to 24.1°. Fewer significant differences were observed at the pelvis than at the hip, knee, or ankle across all tasks. For each task, one or more calibration movements demonstrated non-significant differences in ROM for at least nine out of the twelve ROM variables. These results suggest that IMUs may be a viable alternative to optical motion capture for sport-specific lower-extremity ROM measurement, although the sensor-to-segment calibration methods used should be selected based on the specific tasks and variables of interest for a given application.

Utilizing Motion Capture Systems for Instrumenting the OCRA Index: A Study on Risk Classification for Upper Limb Work-Related Activities.

In the search to enhance ergonomic risk assessments for upper limb work-related activities, this study introduced and validated the efficiency of an inertial motion capture system, paired with a specialized platform that digitalized the OCRA index. Conducted in a semi-controlled environment, the proposed methodology was compared to traditional risk classification techniques using both inertial and optical motion capture systems. The inertial method encompassed 18 units in a Bluetooth Low Energy tree topology network for activity recording, subsequently analyzed for risk using the platform. Principal outcomes emphasized the optical system's preeminence, aligning closely with the conventional technique. The optical system's superiority was further evident in its alignment with the traditional method. Meanwhile, the inertial system followed closely, with an error margin of just ±0.098 compared to the optical system. Risk classification was consistent across all systems. The inertial system demonstrated strong performance metrics, achieving F1-scores of 0.97 and 1 for "risk" and "no risk" classifications, respectively. Its distinct advantage of portability was reinforced by participants' feedback on its user-friendliness. The results highlight the inertial system's potential, mirroring the precision of both traditional and optical methods and achieving a 65% reduction in risk assessment time. This advancement mitigates the need for intricate video setups, emphasizing its potential in ergonomic assessments.

Comparing Trunk Kinematics Computed by Optical Marker-Based Motion Capture System and Inertial Measurement Units During Overground Trips

Falls are the most common cause of non-fatal injuries, and trips are responsible for high percentages of those falls in the United States. Traditional method for estimating trunk kinematics during overground trips uses optical marker-based motion capture systems. However, their cost and space requirements can often be barriers in this research field. Inexpensive and portable inertial measurement units may be an appropriate alternative. This study compared trunk flexion angle and angular velocity at touchdown of the initial recovery step after laboratory-induced trips while walking captured by the optical markerbased motion capture system versus IMUs. Our results provide evidence that a sternum-worn IMU can provide trunk kinematic measurements of clinical relevance and may be used to provide meaningful data to understand kinematic responses to trips or trip-induced falls that occur in real life.

Classification of high mental workload and emotional statuses via machine learning feature extractions in gait

Gait features could help to identify workers' mental workload and different emotional statuses. In the current study, thirty participants were recruited to walk after four emotional states were elicited and the gait data was collected using the optical motion capture system. The 111 posture and kinematic features were extracted from 2 min gait performances. The top 40 ranked features were selected for the Support Vector Machine (SVM) classifier to classify each emotional state. For individual emotional classification, the results revealed that the accuracy rate was 83.33% for neutral, 85.72% for happy, 85.71% for sad, and 89.29% for the high mental workload. For the model evaluation of feature weighting, the minimum value of the right foot in the x- and z-axis, cadence, and stride length were ranked as the top four features. The successful classification could help administrators be aware of employers’ mental and emotional statuses during work.

Gait analysis algorithm for lower limb rehabilitation robot applications

Abstract. When patients with lower limb dyskinesia use robots for rehabilitation training, gait parameters are of great significance for disease diagnosis and rehabilitation evaluation. Gait measurement is usually carried out by using optical motion capture systems, pressure plates and so on. However, it is difficult to apply these systems to lower limb rehabilitation robots due to their high price, limited scope and wearing requirements. At the same time, most of the current applications in robots focus on the basic gait parameters (such as step length and step speed) for robot control or user intention recognition. Therefore, this paper proposes an online gait analysis algorithm for lower limb rehabilitation robots, which uses a lidar sensor as the gait data acquisition sensor. The device is installed on the lower limb rehabilitation robot, which not only avoids the problems of decline in the detection accuracy and failure of leg tracking caused by lidar placement on the ground, but it also calculates seven gait parameters, such as step length, stride length, gait cycle and stance time, with high precision in real time. At the same time, the walking track of the patient may not be straight, and the lidar coordinate system is also changed due to the movement of the lower limb rehabilitation robot when the patient moves forward. In order to overcome this situation, a spatial parameter-splicing algorithm based on a time series is proposed to effectively reduce the error impact on gait spatiotemporal parameters. The experimental results show that the gait analysis algorithm proposed in this paper can measure the gait parameters effectively and accurately. Except for the swing time and double support time, which are calculated with large relative errors due to their small values, the relative errors of the remaining gait parameters are kept below 8 %, meeting the requirements of clinical applications.

Accelerometry applications and methods to assess standing balance in older adults and mobility-limited patient populations: a narrative review.

Accelerometers provide an opportunity to expand standing balance assessments outside of the laboratory. The purpose of this narrative review is to show that accelerometers are accurate, objective, and accessible tools for balance assessment. Accelerometry has been validated against current gold standard technology, such as optical motion capture systems and force plates. Many studies have been conducted to show how accelerometers can be useful for clinical examinations. Recent studies have begun to apply classification algorithms to accelerometry balance measures to discriminate populations at risk for falls. In addition to healthy older adults, accelerometry can monitor balance in patient populations such as Parkinson's disease, multiple sclerosis, and traumatic brain injury. The lack of software packages or easy-to-use applications have hindered the shift into the clinical space. Lack of consensus on outcome metrics has also slowed the clinical adoption of accelerometer-based balance assessments. Future studies should focus on metrics that are most helpful to evaluate balance in specific populations and protocols that are clinically efficacious.

IMU/UWB Fusion Method Using a Complementary Filter and a Kalman Filter for Hybrid Upper Limb Motion Estimation.

Motion capture systems have enormously benefited the research into human-computer interaction in the aerospace field. Given the high cost and susceptibility to lighting conditions of optical motion capture systems, as well as considering the drift in IMU sensors, this paper utilizes a fusion approach with low-cost wearable sensors for hybrid upper limb motion tracking. We propose a novel algorithm that combines the fourth-order Runge-Kutta (RK4) Madgwick complementary orientation filter and the Kalman filter for motion estimation through the data fusion of an inertial measurement unit (IMU) and an ultrawideband (UWB). The Madgwick RK4 orientation filter is used to compensate gyroscope drift through the optimal fusion of a magnetic, angular rate, and gravity (MARG) system, without requiring knowledge of noise distribution for implementation. Then, considering the error distribution provided by the UWB system, we employ a Kalman filter to estimate and fuse the UWB measurements to further reduce the drift error. Adopting the cube distribution of four anchors, the drift-free position obtained by the UWB localization Kalman filter is used to fuse the position calculated by IMU. The proposed algorithm has been tested by various movements and has demonstrated an average decrease in the RMSE of 1.2 cm from the IMU method to IMU/UWB fusion method. The experimental results represent the high feasibility and stability of our proposed algorithm for accurately tracking the movements of human upper limbs.

Inertial Sensor Technologies-Their Role in Equine Gait Analysis, a Review.

Objective gait analysis provides valuable information about the locomotion characteristics of sound and lame horses. Due to their high accuracy and sensitivity, inertial measurement units (IMUs) have gained popularity over objective measurement techniques such as force plates and optical motion capture (OMC) systems. IMUs are wearable sensors that measure acceleration forces and angular velocities, providing the possibility of a non-invasive and continuous monitoring of horse gait during walk, trot, or canter during field conditions. The present narrative review aimed to describe the inertial sensor technologies and summarize their role in equine gait analysis. The literature was searched using general terms related to inertial sensors and their applicability, gait analysis methods, and lameness evaluation. The efficacy and performance of IMU-based methods for the assessment of normal gait, detection of lameness, analysis of horse-rider interaction, as well as the influence of sedative drugs, are discussed and compared with force plate and OMC techniques. The collected evidence indicated that IMU-based sensor systems can monitor and quantify horse locomotion with high accuracy and precision, having comparable or superior performance to objective measurement techniques. IMUs are reliable tools for the evaluation of horse-rider interactions. The observed efficacy and performance of IMU systems in equine gait analysis warrant further research in this population, with special focus on the potential implementation of novel techniques described and validated in humans.

Real-time ground reaction force and knee extension moment estimation during drop landings via modular LSTM modeling and wearable IMUs.

This work investigates real-time estimation of vertical ground reaction force (vGRF) and external knee extension moment (KEM) during single- and double-leg drop landings via wearable inertial measurement units (IMUs) and machine learning. A real-time, modular LSTM-based model with four sub-deep neural networks was developed to estimate the vGRF and KEM from wearable IMUs. Sixteen subjects wore eight IMUs on the chest, waist, right and left thighs, shanks, and feet and performed drop landing trials. Ground embedded force plates and an optical motion capture system were used to capture lower extremity biomechanics for model training and evaluation. During single-leg drop landings, accuracy for the vGRF and KEM estimation was R2=0.88 ±0.12 and R2=0.84 ±0.14, respectively, and during double-leg drop landings, accuracy for the vGRF and KEM estimation was R2=0.85 ±0.11 and R2=0.84 ±0.12, respectively. The best vGRF and KEM estimations of the model with the optimal LSTM unit number (130) require eight IMUs placed on the eight selected locations during single-leg drop landings. During double-leg drop landings, the best estimation on a leg only needs five IMUs placed on the chest, waist, and the leg's shank, thigh, and foot. The proposed modular LSTM-based model with optimally-configurable wearable IMUs can accurately estimate vGRF and KEM in real-time with relatively low computational cost during single- and double-leg drop landing tasks. This investigation could potentially enable in-field, non-contact anterior cruciate ligament injury risk screening and intervention training programs.

The effect of acute equine temporomandibular joint inflammation on response to rein-tension and kinematics.

Although the temporomandibular joint (TMJ) is the major contact point between the reins in the riders' hand, the bit in the mouth, and the rest of the horse under saddle, the role of inflammation of this joint on equine locomotion and rein tension is unknown. To determine the effect of acute TMJ inflammation on rein-tension and horse movement when horses were long-reined on a treadmill. A randomized, controlled, cross-over design. Five horses were trained by one clinician to walk and trot on a treadmill wearing long-reining equipment instrumented with a rein-tension device and reflective optical tracking markers. Subjective assessment of horse's dominant side, and movement, were determined without rein-tension (free walk and trot); and with rein-tension (long-reined walk and trot). Continuous rein-force data from both sides were collected over ~60s from each trial. Movement was recorded using a 12-camera optical motion capture system. One randomly assigned TMJ was subsequently injected with lipopolysaccharide and the treadmill tests repeated by investigators blinded to treatment side. A second, identical assessment was performed 10 days later with the opposite TMJ being the target of intervention. All horses showed reduced rein-tension on the injected (inflamed) side. Increased rein-tension was required on the non-injected side at trot, to maintain them in the correct position on the treadmill post-injection. The only kinematic variable to show any significant change due to rein tension or TMJ inflammation during the walk or trot was an increase in forward head tilt in the presence of rein tension in the trot after injection. Low number of horses and investigation of response to acute inflammation only. TMJ inflammation changed, subjectively and objectively, the response to rein-input, but the horses did not become lame.

Automated Classification of the Phases Relevant to Work-Related Musculoskeletal Injury Risks in Residential Roof Shingle Installation Operations Using Machine Learning

Awkward kneeling in sloped shingle installation operations exposes roofers to knee musculoskeletal disorder (MSD) risks. To address the varying levels of risk associated with different phases of shingle installation, this research investigated utilizing machine learning to automatically classify seven distinct phases in a typical shingle installation task. The classification process relied on analyzing knee kinematics data and roof slope information. Nine participants were recruited and performed simulated shingle installation tasks while kneeling on a sloped wooden platform. The knee kinematics data were collected using an optical motion capture system. Three supervised machine learning classification methods (i.e., k-nearest neighbors (KNNs), decision tree (DT), and random forest (RF)) were selected for evaluation. The KNN classifier provided the best performance for overall accuracy. The results substantiated the feasibility of applying machine learning in classifying shingle installation phases from workers’ knee joint rotation and roof slope angles, which may help facilitate method and tool development for automated knee MSD risk surveillance and assessment among roofers.

Method for Using IMU-Based Experimental Motion Data in BVH Format for Musculoskeletal Simulations via OpenSim

Biomechanical simulation allows for in silico estimations of biomechanical parameters such as muscle, joint and ligament forces. Experimental kinematic measurements are a prerequisite for musculoskeletal simulations using the inverse kinematics approach. Marker-based optical motion capture systems are frequently used to collect this motion data. As an alternative, IMU-based motion capture systems can be used. These systems allow flexible motion collection without nearly any restriction regarding the environment. However, one limitation with these systems is that there is no universal way to transfer IMU data from arbitrary full-body IMU measurement systems into musculoskeletal simulation software such as OpenSim. Thus, the objective of this study was to enable the transfer of collected motion data, stored as a BVH file, to OpenSim 4.4 to visualize and analyse the motion using musculoskeletal models. By using the concept of virtual markers, the motion saved in the BVH file is transferred to a musculoskeletal model. An experimental study with three participants was conducted to verify our method’s performance. Results show that the present method is capable of (1) transferring body dimensions saved in the BVH file to a generic musculoskeletal model and (2) correctly transferring the motion data saved in the BVH file to a musculoskeletal model in OpenSim 4.4.

Shoulder Range of Motion Measurement Using Inertial Measurement Unit—Validation with a Robot Arm

The invention of inertial measurement units allowed the construction of sensors suitable for human motion tracking that are more affordable than expensive optical motion capture systems, but there are a few factors influencing their accuracy, such as the calibration methods and the fusion algorithms used to translate sensor readings into angles. The main purpose of this study was to test the accuracy of a single RSQ Motion sensor in comparison to a highly precise industrial robot. The secondary objectives were to test how the type of sensor calibration affects its accuracy and whether the time and magnitude of the tested angle have an impact on the sensor’s accuracy. We performed sensor tests for nine repetitions of nine static angles made by the robot arm in eleven series. The chosen robot movements mimicked shoulder movements in a range of motion test (flexion, abduction, and rotation). The RSQ Motion sensor appeared to be very accurate, with a root-mean-square error below 0.15°. Furthermore, we found a moderate-to-strong correlation between the sensor error and the magnitude of the measured angle but only for the sensor calibrated with the gyroscope and accelerometer readings. Although the high accuracy of the RSQ Motion sensors was demonstrated in this paper, they require further study on human subjects and comparisons to the other devices known as the gold standards in orthopedics.

Validity of IMU measurements on running kinematics in non-rearfoot strike runners across different speeds

ABSTRACT This study aims to determine the validity of the lower extremity joint kinematics measured by inertial measurement units (IMUs) in non-rearfoot strike pattern (NRFS) runners across different speeds. Fifteen NRFS runners completed three 2-min running tests on a treadmill in random order at 8, 10 and 12 km/h, whilst data were synchronously collected using the IMU system and an optical motion capture system. Before the offset was corrected, the validity of the knee angle waveform was higher than that of the hip and ankle; after the offset was corrected, the validity increased in all three joints. The correlation between the touchdown angles in the sagittal plane measured by the two systems was relatively high after the offset was corrected. The running speed influenced the offset-corrected measurements, with higher error values at higher speeds. The IMU system was able to provide measurements of running kinematics in the sagittal plane of NRFS runners at different running speeds but was unable to reliably measure motion in the frontal and horizontal planes. Future research should analyse the 3D gait of NRFS runners under a larger range of speed conditions to provide evidentiary support for the use of IMUs in running analysis outside the laboratory.

The Novel Method for Prediction of Neurodegenerative Diseases Using Human GAIT Analysis

Abstract: Analyzing human motion is essential for diagnosing movement disorders and guiding neurodegenerative diseases like Parkinson’s diseases and Alzheimer’s diseases. Optical motion capture systems are the standard for estimating diseases, but the equipment is expensive and requires a predefined space, while wearable sensors systems can estimate in any environment and the use of these technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. Here the sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to human gait analysis. First, the phase of human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. Then the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problem and future directions is outlined.

Center of Mass Estimation Using a Force Platform and Inertial Sensors for Balance Evaluation in Quiet Standing

Accurate estimation of the center of mass is necessary for evaluating balance control during quiet standing. However, no practical center of mass estimation method exists because of problems with estimation accuracy and theoretical validity in previous studies that used force platforms or inertial sensors. This study aimed to develop a method for estimating the center of mass displacement and velocity based on equations of motion describing the standing human body. This method uses a force platform under the feet and an inertial sensor on the head and is applicable when the support surface moves horizontally. We compared the center of mass estimation accuracy of the proposed method with those of other methods in previous studies using estimates from the optical motion capture system as the true value. The results indicate that the present method has high accuracy in quiet standing, ankle motion, hip motion, and support surface swaying in anteroposterior and mediolateral directions. The present method could help researchers and clinicians to develop more accurate and effective balance evaluation methods.

Using embedded prosthesis sensors for clinical gait analyses in people with lower limb amputation: A feasibility study

BackgroundBiomechanical gait analyses are typically performed in laboratory settings, and are associated with limitations due to space, marker placement, and tasks that are not representative of the real-world usage of lower limb prostheses. Therefore, the purpose of this study was to investigate the possibility of accurately measuring gait parameters using embedded sensors in a microprocessor-controlled knee joint. MethodsTen participants were recruited for this study and equipped with a Genium X3 prosthetic knee joint. They performed level walking, stair/ramp descent, and ascent. During these tasks, kinematics and kinetics (sagittal knee and thigh segment angle, and knee moment) were recorded using an optical motion capture system and force plates (gold standard), as well as the prosthesis-embedded sensors. Root mean square errors, relative errors, correlation coefficients, and discrete outcome variables of clinical relevance were calculated and compared between the gold standard and the embedded sensors. FindingsThe average root mean square errors were found to be 0.6°, 5.3°, and 0.08 Nm/kg, for the knee angle, thigh angle, and knee moment, respectively. The average relative errors were 0.75% for the knee angle, 11.67% for the thigh angle, and 9.66%, for the knee moment. The discrete outcome variables showed small but significant differences between the two measurement systems for a number of tasks (higher differences only at the thigh). InterpretationThe findings highlight the potential of prosthesis-embedded sensors to accurately measure gait parameters across a wide range of tasks. This paves the way for assessing prosthesis performance in realistic environments outside the lab.

Motion Analysis of the Torso and the Elbow During Picking and Placing Heavy Object on Shelf

In recent years, labor shortages and workforce aging have become problematic. One solution to this problem is introducing humanoid robots to perform human tasks. This research is aimed at analyzing the posture and preparation posture of the torso and elbow in human motions for improving the motion planning of humanoid robots utilizing the redundant part. We analyze the frontal tilt angle of the torso, time of backward bending the torso, and lateral tilt angle of the elbow when humans apply force. In the experiments, we focused on picking and placing heavy objects on the shelf, which requires maintaining balance and exerting force, and confirmed the differences in movements by changing the weight of the objects. An optical motion capture system and a six-axis load cell were used for measuring the motion. The subjects were asked to approach the shelf from a distance of 1 m and move a heavy object from the upper to the middle height of the shelf. As a result, the heavier the object, the earlier the preparation posture of a backward bending of the torso before grasping. The heavier the object, the larger the tilt angle of the torso forward. Significant differences were observed between the time of backward bending and picking the object, distances from the shoulder to wrist joint, and tilt angle of the torso. We identified the posture and time required to bend the torso for holding a heavier object in front of the shelf, considering the dynamic stability of the Zero-Moment Point.