Testing a Quaternion Conversion Method to Determine Human 3D Tibiofemoral Angles During an in Vitro Simulated Jump Landing

2021 ◽  
Author(s):  
Mirel Ajdaroski ◽  
James A. Ashton-Miller ◽  
So Young Baek ◽  
Payam Mirshams Shahshahani ◽  
Amanda Esquivel
Keyword(s):  
Motion Capture ◽  
Knee Kinematics ◽  
Motion Capture System ◽  
Limits Of Agreement ◽  
Jump Landing ◽  
Significant Difference ◽  
Measurement Units ◽  
Validation Testing ◽  
Cadaveric Knee

Abstract Lower limb joint kinematics have been measured in laboratory settings using fixed camera-based motion capture systems; however, recently inertial measurement units (IMUs) have been developed as an alternative. The purpose of this study was to test a quaternion conversion (QC) method for calculating the three orthogonal knee angles during the high velocities associated with a jump landing using commercially available IMUs. Nine cadaveric knee specimens were instrumented with APDM Opal IMUs to measure knee kinematics in one-legged 3-4x bodyweight simulated jump landings, four of which were used in establishing the parameters (training) for the new method and five for validation (testing). We compared the angles obtained from the QC method to those obtained from a commercially available sensor and algorithm (APDM Opal) with those calculated from an active marker motion capture system. Results showed a significant difference between both IMU methods and the motion capture data in the majority of orthogonal angles (p<0.01), though the differences between the QC method and Certus system in the testing set for flexion and rotation angles were smaller than the APDM Opal algorithm, indicating an improvement. Additionally, in all 3 directions both the limits of agreement and root mean square error between the QC method and the motion capture system were smaller than between the commercial algorithm and the motion capture.

Quantifying the Accuracy of a Motion Detecting Neural Prosthesis

2020 ◽  
Author(s):  
Martin L. Tanaka ◽  
Premkumar Subbukutti ◽  
David Hudson ◽  
Kimberly Hudson ◽  
Pablo Valenzuela ◽  
...  
Keyword(s):  
Motion Capture ◽  
Gait Cycle ◽  
Neural Prosthesis ◽  
Average Error ◽  
Camera Motion ◽  
Motion Capture System ◽  
General Shape ◽  
Neural Connections ◽  
Measurement Units ◽  
The Brain

Abstract The neural prosthesis under development is designed to improve gait in people with muscle weakness. The strategy is to augment impaired or damaged neural connections between the brain and the muscles that control walking. This third-generation neural prosthesis contains triaxial inertial measurement units (IMUs - accelerometers, gyroscopes, and processing chip) to measure body segment position and force sensitive resistors placed under the feet to detect ground contact. A study was conducted to compare the accuracy of the neural prosthesis using a traditional camera motion capture system as a reference. The IMUs were found to accurately represent the amplitude of the gait cycle components and generally track the motion. However, there are some differences in phase, with the IMUs lagging the actual motion. Phase lagged by about 10 degrees in the ankle and by about 5 degrees in the knee. Error of the neural prosthesis varied over the gait cycle. The average error for the ankle, knee and hip were 6°, 8°, and 9°, respectively. Testing showed that the neural prosthesis was able to capture the general shape of the joint angle curves when compared to a commercial camera motion capture system. In the future, measures will be taken to reduce lag in the gyroscope and reduce jitter in the accelerometer so that data from both sensors can be combination to obtain more accurate readings.

scholarly journals Is the Landing Error Scoring System Reliable and Valid? A Systematic Review

2020 ◽  
Vol 12 (2) ◽  
pp. 181-188 ◽  
Author(s):  
Ivana Hanzlíková ◽  
Kim Hébert-Losier
Keyword(s):  
Lower Extremity ◽  
Predictive Validity ◽  
Motion Capture ◽  
Study Design ◽  
Scoring System ◽  
Motion Capture System ◽  
Lower Extremity Injuries ◽  
3 Dimensional ◽  
Jump Landing ◽  
Extremity Injuries

Context: The Landing Error Scoring System (LESS) is a clinical tool often used in research and practice to identify athletes presenting high injury-risk biomechanical patterns during a jump-landing task. Objective: To systematically review the literature addressing the psychometric properties of the LESS. Data Sources: Three electronic databases (PubMed, Web of Science, and Scopus) were searched on March 28, 2018, using the term “Landing Error Scoring System.” Study Selection: All studies using the LESS as main outcome measure and addressing its reliability, validity against motion capture system, and predictive validity were included. Original English-language studies published in peer-reviewed journals were reviewed. Studies using modified versions of the LESS were excluded. Study Design: Systematic literature review. Level of Evidence: Level 4. Data Extraction: Study design, population, LESS testing procedures, LESS scores, statistical analysis, and main results were extracted from studies using a standardized template. Results: Ten studies met inclusion criteria and were appraised using Newcastle-Ottawa Quality Assessment Scale adapted for cross-sectional studies. The overall LESS score demonstrated good-to-excellent intrarater (intraclass correlation coefficient [ICC], 0.82-0.99), interrater (ICC, 0.83-0.92), and intersession reliability (ICC, 0.81). The validity of the overall LESS score against 3-dimensional jump-landing biomechanics was good when individuals were divided into 4 quartiles based on LESS scores. The validity of individual LESS items versus 3-dimensional motion capture data was moderate-to-excellent for most of the items addressing key risk factors for anterior cruciate ligament (ACL) injury. The predictive value of the LESS for ACL and other noncontact lower-extremity injuries remains uncertain based on the current scientific evidence. Conclusion: The LESS is a reliable screening tool. However, further work is needed to improve the LESS validity against motion capture system and confirm its predictive validity for ACL and other noncontact lower-extremity injuries.

scholarly journals Kinematics Analysis of Cervical Rotation-Traction Manipulation Measured by a Motion Capture System

2017 ◽  
Vol 2017 ◽  
pp. 1-6
Author(s):  
Zhu Liguo ◽  
Feng Minshan ◽  
Yin Xunlu ◽  
Wang Shangquan ◽  
Yu Jie
Keyword(s):  
Motion Capture ◽  
Rotation Angle ◽  
Head Rotation ◽  
Motion Capture System ◽  
Kinematics Analysis ◽  
Active Range Of Motion ◽  
Significant Difference ◽  
Spine Motion ◽  
Low Amplitude ◽  
Cervical Rotation

Objectives. To analyze the kinematics of cervical rotation-traction manipulation (CRTM). Methods. An experimental study measuring the kinematics of CRTM was conducted. A total of 18 healthy volunteers participated in the study. A single manipulator operated the CRTM for all subjects. Motion capture technology was adopted to track the trajectory during the CRTM operation. Results. The manipulated side did not influence the cervical spine motion. The motion ranges obtained during CRTM were well below the active range of motion reported in the literature. The head rotation angle after thrusting was less than the angle of the rotary-position (P<0.05). There was no significant difference in the head rotation angle between pretraction and upward-thrust. The thrust direction of CRTM was mainly upward. The thrust operation was of high-velocity and low-amplitude (thrust velocity: 203.06±49.95 mm/s; thrust acceleration: 3836.27±1262.28 mm/s2; thrust displacement: 3.25±1.30 mm). Conclusions. CRTM has clear operation steps and repeatability that is suitable for clinical application.

scholarly journals Closing the Wearable Gap—Part VI: Human Gait Recognition Using Deep Learning Methodologies

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 796 ◽  
Author(s):  
Samaneh Davarzani ◽  
David Saucier ◽  
Preston Peranich ◽  
Will Carroll ◽  
Alana Turner ◽  
...  
Keyword(s):  
Linear Regression ◽  
Motion Capture ◽  
Mean Squared Error ◽  
Error Rates ◽  
Human Gait ◽  
Motion Capture System ◽  
3D Motion ◽  
Ankle Kinematics ◽  
Significant Difference ◽  
3D Motion Capture

A novel wearable solution using soft robotic sensors (SRS) has been investigated to model foot-ankle kinematics during gait cycles. The capacitance of SRS related to foot-ankle basic movements was quantified during the gait movements of 20 participants on a flat surface as well as a cross-sloped surface. In order to evaluate the power of SRS in modeling foot-ankle kinematics, three-dimensional (3D) motion capture data was also collected for analyzing gait movement. Three different approaches were employed to quantify the relationship between the SRS and the 3D motion capture system, including multivariable linear regression, an artificial neural network (ANN), and a time-series long short-term memory (LSTM) network. Models were compared based on the root mean squared error (RMSE) of the prediction of the joint angle of the foot in the sagittal and frontal plane, collected from the motion capture system. There was not a significant difference between the error rates of the three different models. The ANN resulted in an average RMSE of 3.63, being slightly more successful in comparison to the average RMSE values of 3.94 and 3.98 resulting from multivariable linear regression and LSTM, respectively. The low error rate of the models revealed the high performance of SRS in capturing foot-ankle kinematics during the human gait cycle.

scholarly journals Automated Quantification of the Landing Error Scoring System With a Markerless Motion-Capture System

2017 ◽  
Vol 52 (11) ◽  
pp. 1002-1009 ◽  
Author(s):  
Timothy C. Mauntel ◽  
Darin A. Padua ◽  
Laura E. Stanley ◽  
Barnett S. Frank ◽  
Lindsay J. DiStefano ◽  
...  
Keyword(s):  
Motion Capture ◽  
Scoring System ◽  
Video Recording ◽  
Automated System ◽  
Depth Camera ◽  
Motion Capture System ◽  
Percentage Agreement ◽  
Jump Landing ◽  
Markerless Motion Capture ◽  
Consensus Expert

Context:  The Landing Error Scoring System (LESS) can be used to identify individuals with an elevated risk of lower extremity injury. The limitation of the LESS is that raters identify movement errors from video replay, which is time-consuming and, therefore, may limit its use by clinicians. A markerless motion-capture system may be capable of automating LESS scoring, thereby removing this obstacle. Objective:  To determine the reliability of an automated markerless motion-capture system for scoring the LESS. Design:  Cross-sectional study. Setting:  United States Military Academy. Patients or Other Participants:  A total of 57 healthy, physically active individuals (47 men, 10 women; age = 18.6 ± 0.6 years, height = 174.5 ± 6.7 cm, mass = 75.9 ± 9.2 kg). Main Outcome Measure(s):  Participants completed 3 jump-landing trials that were recorded by standard video cameras and a depth camera. Their movement quality was evaluated by expert LESS raters (standard video recording) using the LESS rubric and by software that automates LESS scoring (depth-camera data). We recorded an error for a LESS item if it was present on at least 2 of 3 jump-landing trials. We calculated κ statistics, prevalence- and bias-adjusted κ (PABAK) statistics, and percentage agreement for each LESS item. Interrater reliability was evaluated between the 2 expert rater scores and between a consensus expert score and the markerless motion-capture system score. Results:  We observed reliability between the 2 expert LESS raters (average κ = 0.45 ± 0.35, average PABAK = 0.67 ± 0.34; percentage agreement = 0.83 ± 0.17). The markerless motion-capture system had similar reliability with consensus expert scores (average κ = 0.48 ± 0.40, average PABAK = 0.71 ± 0.27; percentage agreement = 0.85 ± 0.14). However, reliability was poor for 5 LESS items in both LESS score comparisons. Conclusions:  A markerless motion-capture system had the same level of reliability as expert LESS raters, suggesting that an automated system can accurately assess movement. Therefore, clinicians can use the markerless motion-capture system to reliably score the LESS without being limited by the time requirements of manual LESS scoring.

scholarly journals Biomechanical study of screw fixation and plate fixation of a posterior malleolar fracture in a simulation of the normal gait cycle

2017 ◽  
Vol 2 (3) ◽  
pp. 2473011417S0004
Author(s):  
Xu Wang
Keyword(s):  
Motion Capture ◽  
Gait Cycle ◽  
Fatigue Loading ◽  
Repeated Loading ◽  
Motion Capture System ◽  
Spatial Motion ◽  
Malleolar Fracture ◽  
Posterior Malleolar Fracture ◽  
Significant Difference ◽  
Loading System

Category: Ankle Introduction/Purpose: Fixation of the posterior malleolar fracture with plate or screws is under debating. A fatigue loading system and a spatial motion capture system will provide a theoretical basis for the selection. Methods: Thirty-six below-knee specimens with Haraguchi I type posterior malleolar fracture model was obtained. The specimens were randomly divided into two groups, Group A used two parallel-placed 3.5 mm semi-threaded hollow titanium alloy screws to fix the fracture from back to front; group B used an anatomical plate to fix the posterior malleolus. According to the ratio S of the area of the sagittal fracture over the total area of the distal tibial articular surface, each group was subdivided into three groups recorded as A1, B1; A2, B2; and A3, B3. In group A1 and B1, S=1/4; in A2 and B2, S=1/3; in A3 and B3, S=1/2. In the simulations of gait cycle, 4 kinds of ankle joints were subjected to mechanical loading. A fatigue loading system was used for repeated loading. The spatial motion capture system was used to measure the displacement X (mm) in the final stage of loading. Results: At different locations and different loading conditions, the posterior malleolar fracture displacement X in the three groups of posterior malleolar fracture showed no statistically significant difference. Conclusion: here is no biomechanical difference between the internal fixation of two parallel 3.5-mm hollow screws and anatomical plate for Haraguchi I type posterior malleolar fractures with an average fracture block height of 19.27 mm.

scholarly journals Comparison of Vertical Breast Displacement during Rope Skipping, Walking, Jogging and Running

2021 ◽  
Vol 237 ◽  
pp. 04033
Author(s):  
X Sheng ◽  
X Chen ◽  
Y Li ◽  
G Sun ◽  
H Xie
Keyword(s):  
Motion Capture ◽  
Three Dimensional ◽  
Motion Capture System ◽  
Exercise Modality ◽  
Significant Difference ◽  
Rope Skipping

In order to investigate the effect of exercise modality and bra type on vertical breast displacement, seven participants with similar ages and body types were selected to take part in this study. Three-dimensional motion capture system (NDI Optotrak Certus) was used to collect the data of vertical breast displacement when participants were walking at the speed of 5 km/h, jogging at the speed of 7.5 km/h, running at the speed of 10 km/h on a treadmill, and rope skipping at the frequency of twice per second. It was found that there was no significant difference in vertical breast displacement between rope skipping, jogging and running. And the vertical breast displacement when wearing sports bra is significantly less than everyday bra in four exercise modalities. The results of this study might be useful for designing special sports bra for rope skipping modality.

Predictive Model for Live Birth at 12 Months After Starting In-Vitro Fertilization Treatment

MedPharmRes ◽  
2018 ◽  
Vol 2 (2) ◽  
pp. 5-20
Author(s):  
Vu Ho ◽  
Toan Pham ◽  
Tuong Ho ◽  
Lan Vuong
Keyword(s):  
In Vitro Fertilization ◽  
Live Birth ◽  
Cox Regression ◽  
Financial Burden ◽  
Oocyte Retrieval ◽  
Operating Characteristics ◽  
Vitro Fertilization ◽  
Cox Regression Analysis ◽  
Significant Difference

IVF carries a considerable physical, emotional and financial burden. Therefore, it would be useful to be able to predict the likelihood of success for each couple. The aim of this retrospective cohort study was to develop a prediction model to estimate the probability of a live birth at 12 months after one completed IVF cycle (all fresh and frozen embryo transfers from the same oocyte retrieval). We analyzed data collected from 2600 women undergoing in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) at a single center in Vietnam between April 2014 and December 2015. All patients received gonadotropin-releasing hormone (GnRH) antagonist stimulation, followed by fresh and/or frozen embryo transfer (FET) on Day 3. Using Cox regression analysis, five predictive factors were identified: female age, total dose of recombinant follicle stimulating hormone used, type of trigger, fresh or FET during the first transfer, and number of subsequent FET after the first transfer. The area under the receiver operating characteristics curve for the final model was 0.63 (95% confidence interval [CI] 0.60‒0.65) and 0.60 (95% CI 0.57‒0.63) for the validation cohort. There was no significant difference between the predicted and observed probabilities of live birth (Hosmer-Lemeshow test, p > 0.05). The model developed had similar discrimination to existing models and could be implemented in clinical practice.

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