Optical Motion Capture System Research Articles

Step Length and Gait Speed Estimation Using a Hearing Aid Integrated Accelerometer: A Comparison of Different Algorithms.

Gait is an indicator of a person's health status and abnormal gait patterns are associated with a higher risk of falls, dementia, and mental health disorders. Wearable sensors facilitate long-term assessment of walking in the user's home environment. Earables, wearable sensors that are worn at the ear, are gaining popularity for digital health assessments because they are unobtrusive and can easily be integrated into the user's daily routine, for example, in hearing aids. A comprehensive gait analysis pipeline for an ear-worn accelerometer that includes spatial-temporal parameters is currently not existing. Therefore, we propose and compare three algorithmic approaches to estimate step length and gait speed based on ear-worn accelerometer data: a biomechanical model, feature-based machine learning (ML) models, and a convolutional neural network. We evaluated their performance on a step and walking bout level and compared it with an optical motion capture system. The feature-based ML model achieved the best performance with a precision of 4.8cm on a walking bout level. For gait speed, the machine learning approach achieved an absolute percentage error of 5.4 % ( ± 4.0 %). We find that the ML model is able to estimate step length and gait speed with clinically relevant precision. Furthermore, the model is insensitive to different age groups and sampling rates but sensitive to walking speed. To our knowledge, this work is the first contribution to estimating step length and gait speed using ear-worn accelerometers. Moreover, it lays the foundation for a comprehensive gait analysis framework for ear-worn sensors enabling continuous and long-term monitoring at home.

Accurate detection of gait events using neural networks and IMU data mimicking real-world smartphone usage

Wearable technologies such as inertial measurement units (IMUs) can be used to evaluate human gait and improve mobility, but sensor fixation is still a limitation that needs to be addressed. Therefore, aim of this study was to create a machine learning algorithm to predict gait events using a single IMU mimicking the carrying of a smartphone. Fifty-two healthy adults (35 males/17 females) walked on a treadmill at various speeds while carrying a surrogate smartphone in the right hand, front right trouser pocket, and right jacket pocket. Ground-truth gait events (e.g. heel strikes and toe-offs) were determined bilaterally using a gold standard optical motion capture system. The tri-dimensional accelerometer and gyroscope data were segmented in 20-ms windows, which were labelled as containing or not the gait events. A long-short term memory neural network (LSTM-NN) was used to classify the 20-ms windows as containing the heel strike or toe-off for the right or left legs, using 80% of the data for training and 20% of the data for testing. The results demonstrated an overall accuracy of 92% across all phone positions and walking speeds, with a slightly higher accuracy for the right-side predictions (∼94%) when compared to the left side (∼91%). Moreover, we found a median time error <3% of the gait cycle duration across all speeds and positions (∼77 ms). Our results represent a promising first step towards using smartphones for remote gait analysis without requiring IMU fixation, but further research is needed to enhance generalizability and explore real-world deployment.

Age-related changes in gait, balance, and strength parameters: A cross-sectional study.

Longevity is increasing worldwide due to improvements in healthcare and living standards. Aging is often associated with disability and multiple health concerns. To address these challenges, effective interventions are essential. This study investigated potential age-related declines in gait, balance, and strength. We also sought to assess any relationships between these three parameters and explore potential differences between women and men. Healthy individuals over 50 years of age were recruited for this cross-sectional study. Upper extremity (grip) strength and lower extremity (knee) strength of the dominant side were measured. Static balance was performed on the force plate in different situations each for 30 seconds: bilateral stance with eyes open, bilateral stance with eyes closed, as well as dominant leg and non-dominant leg unilateral stance with eyes open. Gait was measured during level walking using an optical motion capture system. Additionally, the dynamic stability margin (DSM) was calculated for the level walking trials. The study results indicated that gait parameters were not significantly affected by age (p≥0.12), while knee and grip strength, along with several balance parameters, showed a significant decline with age. All individuals were able to maintain their bipedal balance, but their center of pressure movement increased significantly by age (p≤0.028). Z-scores were calculated to compare significant age parameters. Unipedal stance time was found to be the most affected by age compared to other contributing factors (p≤0.001). The duration of unipedal balance showed the most significant change per decade (non-dominant: -0.62 SDs; dominant: -0.53 SDs), while strength measures exhibited the lowest amount of change per decade (grip strength: -0.34 SDs; knee strength: -0.26 SDs). Sex differences were observed exclusively in strength parameters, with no discernible impact on the decline in balance parameters. These findings suggest that the duration of unipedal stance can serve as a reliable and gender-independent measure of neuromuscular aging for both elderly male and female subjects.

Spatiotemporal gait characteristics post-total hip arthroplasty and its impact on locomotive syndrome: a before-after comparative study in hip osteoarthritis patients.

Understanding the gait pattern of patients eligible for total hip arthroplasty (THA) due to hip osteoarthritis (OA) offers valuable information for improving locomotive syndrome (LS). This study aims to measure the gait patterns of THA-eligible patients using an optical motion capture system and to analyze these patterns using principal component analysis (PCA). Additionally, this study examines the relationship between THA-induced gait patterns and LS. This before-after study included 237 patients who underwent unilateral primary THA due to hip OA. The primary outcome measures were spatiotemporal gait parameters. Secondary outcome measures included three LS risk tests: a stand-up test, a two-step test, a 25-question Geriatric Locomotive Function Scale (GLFS-25), and total clinical decision limits stages. PCA was performed using 16 spatiotemporal gait parameters collected before and three months after THA. Principal components (PC) were selected to achieve a cumulative contribution rate of 90% (0.9) or higher. Each summarized PC was compared using a paired t-test before and three months after THA. Furthermore, multiple regression analysis was conducted to determine how changes in each PC between before and three months after THA related to changes in the four LS evaluation items. PCA identified three principal components (PC1, PC2, PC3) that accounted for a cumulative contribution rate of 0.910 using 16 spatiotemporal gait parameters. When comparing before and three months after THA for all three PCs, significant differences were observed in each PC (p<0.001), with overall walking ability and stance phase being higher three months after THA than before THA, while the asymmetry of support time was lower three months after THA. The results of multiple regression analysis revealed that PC1, PC2, and PC3 were the most influential factors in total clinical decision limits stage. For each LS risk test, the factors related to the stand-up test were identified as PC1, PC2, and PC3, while the factors related to the two-step test were identified as PC1 and PC2. The factors related to the GLFS-25 were also identified as PC1 and PC2. The most important findings of this study indicate that the three PCs represent over 90% of the 16 spatiotemporal gait parameters, which are associated with total clinical decision limits stage and LS risk tests. The present results suggest that PC1 represents overall walking ability, PC2 represents the stance phase, and PC3 represents asymmetry of support time. Gait pattern characteristics, such as overall walking ability, stance phase, and asymmetry of support time, were clearly defined by these PCs. Regarding the relationship between PC and LS, all three PCs are related to total clinical decision limits stage. In addition, PC1 and PC2 related to all three LS risk tests, and PC3 related only to the stand-up test.

Differences in throwing kinematics due to changes in run-up speed in javelin throwing.

In javelin throw training, although short run-up throwing is more frequently performed than full run-up throwing (throwing like a competition), it is unclear whether the throwing motion of the short run-up throwing is different from full run-up throwing. Therefore, the purpose of this study was to clarify the differences between the short and full run-up throwing in javelin throw. Twelve male javelin throwers were included in this study. The participants performed one-cross throwing (Cross), which involved throwing from a short run-up, and full run-up throwing (Run), which was similar to a competition. The coordinates of the reflective markers attached to the participant's body and javelin was recorded using an optical motion capture system. The analysis phase was from the time of last foot contact to the time of release. We calculated the kinematic variables related to javelin throwing performance and compared the Cross and Run. The results showed significant differences in the joint angles of the left knee, trunk, and right shoulder between Cross and Run. The results indicate that different throwing motions occur in short run-up throwing compared to full run-up throwing.

Modeling Muscle Activities of Squat Motion using OpenSIM

Human beings need to be able to monitor their muscle health. Muscle health can be assessed by directly measuring its signal using an electromyography sensor or, recently, from a musculoskeletal modeling software such as OpenSIM. OpenSIM is an open-source platform for modeling, simulating, and analyzing the neuromusculoskeletal system. This work aims to simulate muscle forces during squat movement using OpenSIM simulation and validate the results using electromyography sensors attached to the body. The squat movement data generated from the optical motion capture system and the force plate will be used as inputs for the OpenSIM software. OpenSIM can provide output, such as a graph of the working muscle forces. That graph will be compared and analyzed with a surface electromyography sensor results graph. Based on the analysis results, the chart of working muscle forces generated by the OpenSIM software has shown a similar trend to the graph of muscle activity resulting from surface electromyography sensor reading. Therefore, the simulation results from the OpenSIM software were validated, and it was implied that the data collection process and the modeling were done correctly.

Comparison of joint kinematics from optical marker-based and inertial sensor-based motion capture during change-of-direction movements

The development of inertial measurement units (IMUs) has opened up possibilities to test kinematics of sports movements in natural environments (Di Paolo et al., 2023). This advantage over lab-based optical motion capture (OMC) comes at the cost of inherent inaccuracies. Especially, fast multidirectional movements including de- and accelerations pose a challenge for inertial-based movement reconstruction with limited task-specific validation research available (Heuvelmans et al., 2022). Therefore, we aimed to test the validity of IMU-based kinematics in a 180° change of direction (COD) task to provide insight into the applicability for future on-field ACL injury risk research. Twenty-six sports science students were equipped with eight IMU sensors of the Noraxon Ultium Motion System and an adaption of the Plug-in-Gait markerset (53 markers) recorded by a VICON OMC system. They performed five 180° anti-clockwise CODs at their maximum speed. As an example for sagittal and frontal plane angles relevant for ACL injury risk, the knee flexion and abduction angle at the initial contact of the cutting step (right leg) were calculated using the Noraxon MyoMotion software for IMU-recorded data and OpenSim for marker-based OMC data. At the time of abstract submission, the data of five (randomly chosen) participants had been analysed. More participants and more joint angle outcomes will be included in follow-up analyses. Deviations between IMU-generated and OMC-generated joint angles are depicted in Figure 1. Within subjects, deviations below 1.5 standard deviations were observed. Between subjects, deviations ranged from 0° and 22° in knee flexion angles and 0° and 11° in knee abduction angles. A general trend towards higher joint angles measured by the IMU system is noticeable, suggesting a possible off-set error. For four out of five participants, the knee flexion deviation was below 10°, which can be considered an accuracy which is tolerable but requires consideration in the interpretation (&lt; 2°: good accuracy, 2-5°: acceptable accuracy, 5-10°: tolerable accuracy, &gt; 10° unbearable accuracy; Bessone et al., 2022). Deviations in knee abduction angles appear too large given the small physiological joint range and motion in this plane. This preliminary analysis of individual deviations suggests acceptable deviations between IMU and OMC measurements regarding knee flexion but problematic deviations for knee abduction. Prospectively, we aim to investigate possible off-set corrections, e.g. through a better alignment of the underlying biomechanical models, and alternative approaches for IMU-based movement reconstruction, e.g. through optimal control simulations (Nitschke et al., 2024).

Reliability testing of an IMU-based 2-segment foot model for clinical gait analysis

BackgroundThe one of the most commonly used reference system for clinical gait analysis is an optical motion capture system (OMC) using a multi-segment foot model. A time- and cost-efficient alternative could be an inertial measurement unit (IMU)-based systems. However, these are limited to a single segment approach for the foot and ankle. Therefore, the current setup was modified to be based on a 2-segment foot model, allowing for a separate analysis of the hind- and midfoot. The study aimed to evaluate the reliability (inter-rater, intra-rater, and test-retest reliability) of an IMU-based 2-segment foot model. Material and methodsTwelve healthy subjects were recruited to test the inter-rater, intra-rater, and test-retest reliability of the new IMU based 2-segment foot model. Gait analysis was performed on a treadmill at a constant speed of 4 km/h. Kinematic data of the tibia/hindfoot, tibia/forefoot and hindfoot/forefoot over 100 % gait cycle were analyzed. The reliability was tested by using statistical parametric mapping (SPM) and the intraclass correlation coefficient (ICC). ResultsThe SPM showed no significant difference for inter-, intra-rater, and test-retest reliability, but for a small segment of tibia/forefoot dorsiflexion test-retest reliability (2.1° difference). The single standard deviation measurement error for the sagittal and transverse plane was <5° and worse for the frontal plane. ConclusionThe new 2-segment foot model revealed a high inter-rater, intra-rater, and test-retest reliability. It is suitable for use in adult clinical practice. Still, comparative data to the OMC system using a multi-segment foot model are missing.

DAMO: A Deep Solver for Arbitrary Marker Configuration in Optical Motion Capture

Marker-based optical motion capture (mocap) systems are increasingly utilized for acquiring 3D human motion, offering advantages in capturing the subtle nuances of human movement, style consistency, and ease of obtaining desired motion. Motion data acquisition via mocap typically requires laborious marker labeling and motion reconstruction, recent deep-learning solutions have aimed to automate the process. However, such solutions generally presuppose a fixed marker configuration to reduce learning complexity, thereby limiting flexibility. To overcome the limitation, we introduce DAMO, an end-to-end deep solver, proficiently inferring arbitrary marker configurations and optimizing pose reconstruction. DAMO outperforms state-of-the-art like SOMA and MoCap-Solver in scenarios with significant noise and unknown marker configurations. We expect that DAMO will meet various practical demands such as facilitating dynamic marker configuration adjustments during capture sessions, processing marker clouds irrespective of whether they employ mixed or entirely unknown marker configurations, and allowing custom marker configurations to suit distinct capture scenarios. DAMO code and pretrained models are available at https://github.com/CritBear/damo

Flexible Sensor-based Whole-body Biomechanics of Exoskeleton-assisted Patient Handling

The issue of work-related musculoskeletal disorders (WMSDs) stemming from manual handling tasks remains a significant economic and societal burden, with healthcare workers being one of the most persistently affected groups. Exoskeletons have seen growing implementation for aiding manual handling tasks in industrial sectors. Traditional measurements and analyses of joint kinematics and dynamics commonly utilize optical motion capture systems and force plates. Nevertheless, the limitations of optical motion capture systems include their dependence on marker visibility and the necessity for a controlled lab environment. An innovative wireless technology featuring miniaturized, skin-mounted sensors, BioStamp nPoint (MC10, Inc., MA, USA) exhibits significant potential in addressing the challenges mentioned above. Therefore, the objective of this study was to explore a flexible sensor-based approach to assessing the effects of a back exoskeleton on joint kinematics during patient handling.

Validity of Valor Inertial Measurement Unit for Upper and Lower Extremity Joint Angles.

Inertial measurement units (IMU) are increasingly utilized to capture biomechanical measures such as joint kinematics outside traditional biomechanics laboratories. These wearable sensors have been proven to help clinicians and engineers monitor rehabilitation progress, improve prosthesis development, and record human performance in a variety of settings. The Valor IMU aims to offer a portable motion capture alternative to provide reliable and accurate joint kinematics when compared to industry gold standard optical motion capture cameras. However, IMUs can have disturbances in their measurements caused by magnetic fields, drift, and inappropriate calibration routines. Therefore, the purpose of this investigation is to validate the joint angles captured by the Valor IMU in comparison to an optical motion capture system across a variety of movements. Our findings showed mean absolute differences between Valor IMU and Vicon motion capture across all subjects' joint angles. The tasks ranged from 1.81 degrees to 17.46 degrees, the root mean squared errors ranged from 1.89 degrees to 16.62 degrees, and interclass correlation coefficient agreements ranged from 0.57 to 0.99. The results in the current paper further promote the usage of the IMU system outside traditional biomechanical laboratories. Future examinations of this IMU should include smaller, modular IMUs with non-slip Velcro bands and further validation regarding transverse plane joint kinematics such as joint internal/external rotations.

Cervical Sensorimotor Function Tests Using a VR Headset-An Evaluation of Concurrent Validity.

Sensorimotor disturbances such as disturbed cervical joint position sense (JPS) and reduced reaction time and velocity in fast cervical movements have been demonstrated in people with neck pain. While these sensorimotor functions have been assessed mainly in movement science laboratories, new sensor technology enables objective assessments in the clinic. The aim was to investigate concurrent validity of a VR-based JPS test and a new cervical reaction acuity (CRA) test. Twenty participants, thirteen asymptomatic and seven with neck pain, participated in this cross-sectional study. The JPS test, including outcome measures of absolute error (AE), constant error (CE), and variable error (VE), and the CRA test, including outcome measures of reaction time and maximum velocity, were performed using a VR headset and compared to a gold standard optical motion capture system. The mean bias (assessed with the Bland-Altman method) between VR and the gold standard system ranged from 0.0° to 2.4° for the JPS test variables. For the CRA test, reaction times demonstrated a mean bias of -19.9 milliseconds (ms), and maximum velocity a mean bias of -6.5 degrees per seconds (°/s). The intraclass correlation coefficients (ICCs) between VR and gold standard were good to excellent (ICC 0.835-0.998) for the JPS test, and excellent (ICC 0.931-0.954) for reaction time and maximum velocity for the CRA test. The results show acceptable concurrent validity for the VR technology for assessment of JPS and CRA. A slightly larger bias was observed in JPS left rotation which should be considered in future research.

PAM: Research on posture alignment method for camera robot system

By acquiring the posture information of the rigid body with markers, the camera motion on the robot end-effector can be controlled remotely. The essential step of posture alignment in teleoperation is to calibrate the transformation matrix of the world coordinate system of the optical motion capture system and the robot base coordinate system. It was necessary to change the position and attitude of the camera robot in the studio frequently. At the same time, the studio was complex with strong personnel mobility, thus the world coordinate system of the optical tracking system in the studio was needed to be re-established frequently. These two points asked the pose calibration scheme of the camera robot in the optical tracking system to be convenient and universal. In this paper, a novel posture alignment method (PAM) was proposed, which was an automatic, accurate and quick method to complete the posture alignment for related coordinate systems without non-systematic errors. PAM was applied to teleoperation for camera robot by inverse movement matrix. The comparative experiments prove that the proposed method is better than manual method, with higher precision and stability, more flexible physical setting for reference coordinate system, and shorter operation time consuming. Meanwhile, PAM is more suitable for camera robot teleoperation than existed automatic method, because it does not require paying attention to the calibration postures.

A dataset of optical camera and IMU sensor derived kinematics of thirty transtibial prosthesis wearers

Motion analysis has played a crucial role in providing gait analysis for prosthetic users. Understanding kinematics in motion analysis allows for the evaluation of the effects of prostheses and the development of a prosthetic component design that aids in walking within the community. However, there are currently limited open datasets available to study the locomotion of individuals using transtibial prostheses, and most research studies involve a limited number of participants. This dataset shows 30 transtibial prosthesis users walking comfortably on a 10-meter walkway inside a laboratory. We offer a set of joint ankles obtained from the inertial measurement unit (IMU) signals in CSV file format. We also provide the optical motion capture (OMC) system’s raw trajectory marker data in C3D file format and joint angle in CSV file format. This open dataset will provide resources for professionals interested in amputee gait for research in amputee gait detection and tracking algorithms. Moreover, the data will be the control data for comparison in developing advanced prosthetic components.

An exploration of the agreement, inter- and intra-rater reliability, and reproducibility of three common methods used to measure minimum toe clearance with optical motion capture systems under three shoe conditions

BackgroundThe gait variable minimum toe clearance (MTC) has been investigated concerning trip-related fall research in older adults. However, comparing studies is difficult due to the different methods used to measure MTC and shoe conditions, which may affect agreement. Measurement methods can include using a single virtual point (SVP), multiple virtual points (MVPS), or metatarsal head markers (marker-based). The shoe types used in MTC studies include standard shoes (SS), personal shoes (PS), and barefoot (BF) conditions. Research questionWhat is the agreement, inter and intra-rater reliability, and repeatability for the 3 commonly used methods of measuring MTC (SVP, MVPS, marker-based) under the 3 shoe conditions for optical motion capture systems (SS, PS, BF)? MethodsTwelve healthy young adults (mean [SD] 23.8 [1.9] years,7 males) participated in this observational study. In a randomized order, participants completed 25 walking trials at self-selected normal and slow speeds in SS, PS, and BF conditions while infrared cameras recorded the maker trajectories. Each participant performed a familiarization trial for at least 1 minute before collecting data on each shoe condition. Statistical analyses included Bland-Altman 95 % limits of agreement (LOA) analyses, interclass correlation coefficient (ICC) analyses for inter- and intra-rater reliability, and the repeatability coefficient (RC). ResultsThe SVP and MVPS had a tighter 95 % LOA than the marker-based method, particularly under SS and BF conditions. The inter-rater reliability was good to excellent under these shoe conditions. Intra-reliability for all methods under all shoe conditions was excellent (ICC >.90). The RC was very similar for each method, with none exceeding 1.02 cm. SignificanceThe study provides estimates of the agreement between MTC methods and suggests that only SVP or MVPS produced similar results in SS/BF conditions. Additionally, a "true" change in MTC requires a difference greater than 1.02 cm.

Ankle Moment Estimation Based on A Novel Distributed Plantar Pressure Sensing System.

Ankle moment plays an important role in human gait analysis, patients' rehabilitation process monitoring, and the human-machine interaction control of exoskeleton robots. However, current ankle moment estimation methods mainly rely on inverse dynamics (ID) based on optical motion capture system (OMC) and force plate. These methods rely on fixed instruments in the laboratory, which are difficult to be applied to the control of exoskeleton robots. To solve this problem, this paper developed a new distributed plantar pressure system and proposed an ankle plantar flexion moment estimation method using the plantar pressure system. We integrated eight pressure sensors in each insole to collect the pressure data of the key area of the foot and then used the plantar pressure data to train four neural networks to obtain the ankle moment. The performance of the models was evaluated using normalized root mean square error (NRMSE) and cross-correlation coefficient (ρ). During experiments, eight subjects were recruited for the overground walking tests, and OMC and force plate were used as the gold standard. The results indicate that the Genetic algorithm - Gated recurrent unit estimation algorithm (GA-GRU) was the best estimation model which achieved the highest accuracy in generalized ankle moment estimation (NRMSE = 7.23%, ρ = 0.85) compared with the other models. The designed novel distributed plantar pressure system and the proposed method could serve as a joint moment estimation approach in wearable robot control and human motion state monitoring.

Cognitive Load and Dual-Task Performance in Individuals with and without Forward Head Posture.

Background: Recent studies have found forward head posture (FHP) is associated with altered physiology. There is a lack of research into whether FHP is associated with altered gait parameters when cognitively challenged. Our hypothesis is that individuals with FHP and those without will demonstrate different responses when undergoing dual-task assessment. Methods: Forty-five asymptomatic participants with FHP, defined as a craniovertebral angle (CVA) < 50°, were matched to forty-five participants with normal head posture (NHP) with a CVA > 55°. Participants walked along a 10 m platform under a control condition (no cognitive load) while an optical motion-capture system assessed gait kinematics. Secondly, participants were assessed under a dual-task cognitive load condition to identify the impact on gait kinematics. Results: Under the single-task condition, there were no significant differences for any gait parameter. In the dual-task condition, 12/13 gait parameters were significantly altered for the FHP vs. NHP group (p < 0.01). A calculation of the dual-task cost (DTC) percentage showed significant increases in all gait parameters in participants with FHP (p < 0.02). Correlations between the CVA and gait parameters were not significant for the single-task condition, but all gait parameters were correlated to CVA for the dual-task condition (p < 0.01). The correlation between CVA and DTC for all gait variables was significant (p < 0.04). Conclusions: This study demonstrates that FHP significantly increases the cognitive cost during walking, highlighting the importance of proper postural alignment for maintaining cognitive function under a dual-task condition.

Development of an IMU based 2-segment foot model for an applicable medical gait analysis

BackgroundThe two most commonly instrumented gait analysis tools used are Optical Motion Capture systems (OMC) and Inertial Measurement Units (IMU). To date, OMC based gait analysis is considered the gold-standard. Still, it is space-, cost-, and time-intense. On the other hand IMU systems are more cost- and time effective but simulate the whole foot as a single segment. To get a more detailed model of the foot and ankle, a new 2-segment foot model using IMU was developed, comparable to the multi-segment foot models assessed by OMC.Research questionCan an IMU based 2-segment foot model be developed to provide a more detailed representation of the foot and ankle kinematics?MethodsTo establish a 2-segment foot model, in addition to the previous 1-segment foot model an IMU sensor was added to the calcaneus. This allowed the differentiation between the hindfoot and forefoot kinematics. 30 healthy individuals (mean age 27 ± 7 years) were recruited to create a norm data set of a healthy cohort. Moreover, the kinematic data of the 2-segment foot model were compared to those of the traditional 1-segment foot model using statistical parametric mapping.ResultsThe 2-segment foot model proved to be applicable. Furthermore, it allowed for a more detailed representation of the foot and ankle joints, similar to other multi-segment foot model. The healthy cohort’s norm data set showed a homogeneous motion pattern for gait.ConclusionThe 2-segment foot model allows for an extension of IMU-based gait analysis. Futures studies must prove the reliability and validity of the 2-segment foot model in healthy and pathologic situations.Level of evidenceLevel II.

Comparison of the efficacy of a pulsed thulium YAG laser versus a holmium YAG laser on dusting lithotripsy: In vitro analysis using an optical motion capture system.

Comparison of the efficacy of a pulsed thulium YAG laser versus a holmium YAG laser on dusting lithotripsy: In vitro analysis using an optical motion capture system.

Verification of acromion marker cluster and scapula spinal marker cluster methods for tracking shoulder kinematics: a comparative study with upright four-dimensional computed tomography

BackgroundThis study validated the accuracy of the acromion marker cluster (AMC) and scapula spinal marker cluster (SSMC) methods compared with upright four-dimensional computed tomography (4DCT) analysis.MethodsSixteen shoulders of eight healthy males underwent AMC and SSMC assessments. Active shoulder elevation was tracked using upright 4DCT and optical motion capture system. The scapulothoracic and glenohumeral rotation angles calculated from AMC and SSMC were compared with 4DCT. Additionally, the motion of these marker clusters on the skin with shoulder elevation was evaluated.ResultsThe average differences between AMC and 4DCT during 10°−140° of humerothoracic elevation were − 2.2° ± 7.5° in scapulothoracic upward rotation, 14.0° ± 7.4° in internal rotation, 6.5° ± 7.5° in posterior tilting, 3.7° ± 8.1° in glenohumeral elevation, − 8.3° ± 10.7° in external rotation, and − 8.6° ± 8.9° in anterior plane of elevation. The difference between AMC and 4DCT was significant at 120° of humerothoracic elevation in scapulothoracic upward rotation, 50° in internal rotation, 90° in posterior tilting, 120° in glenohumeral elevation, 100° in external rotation, and 100° in anterior plane of elevation. However, the average differences between SSMC and 4DCT were − 7.5 ± 7.7° in scapulothoracic upward rotation, 2.0° ± 7.0° in internal rotation, 2.3° ± 7.2° in posterior tilting, 8.8° ± 7.9° in glenohumeral elevation, 2.0° ± 9.1° in external rotation, and 1.9° ± 10.1° in anterior plane of elevation. The difference between SSMC and 4DCT was significant at 50° of humerothoracic elevation in scapulothoracic upward rotation and 60° in glenohumeral elevation, with no significant differences observed in other rotations. Skin motion was significantly smaller in AMC (28.7 ± 4.0 mm) than SSMC (38.6 ± 5.8 mm). Although there was smaller skin motion in AMC, SSMC exhibited smaller differences in scapulothoracic internal rotation, posterior tilting, glenohumeral external rotation, and anterior plane of elevation compared to 4DCT.ConclusionThis study demonstrates that AMC is more accurate for assessing scapulothoracic upward rotation and glenohumeral elevation, while SSMC is preferable for evaluating scapulothoracic internal rotation, posterior tilting, glenohumeral external rotation, and anterior plane of elevation, with smaller differences compared to 4DCT.