A Subject-Specific Analysis of the Kinematic Constraint Imposed by the Relink Trainer

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Sarah Ward ◽  
Lukas Wiedemann ◽  
Kazuto Kora ◽  
Andrew McDaid

Abstract The relink trainer (RLT) is a novel end-effector device designed for gait-retraining poststroke. The user's foot is constrained to a specific kinematic trajectory relative to the trainer, while the hip and knee are unconstrained. As the RLT only fixes the footplate trajectory, the expected constraint on the hip and knee angles will be subject-specific due to individual lower limb geometries. This study had two objectives (1) to calculate the subject-specific theoretical joint angle trajectories, the RLT should constrain the hip and knee angle to using computer simulation, assuming a fixed hip position relative to the RLT, and (2) experimentally determine the actual hip and knee joint angle trajectories of healthy users walking in the RLT, and compare them to the theoretical joint angle trajectories. The root-mean-square (RMS) error between joint trajectories obtained from motion capture and simulation ranged from 4.31 deg to 20.51 deg for the hip and between 4.48 deg and 22.58 deg for the knee, suggestive of moderate to poor accuracy and distinct kinematic adaptation strategies when using the RLT. A linear fit method (LFM) was used to determine the similarity between the obtained and simulated joint angle trajectories. LFM results would suggest that users' hip and knee joint angles follow the simulated joint angle trajectories when walking in the RLT; however, the actual joint angle trajectories are offset from the simulation trajectories. Post hoc analyses suggest hip motion when using the RLT influences the hip and knee angle trajectory differences for participants.

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2759 ◽  
Author(s):  
Eric Allseits ◽  
Kyoung Kim ◽  
Christopher Bennett ◽  
Robert Gailey ◽  
Ignacio Gaunaurd ◽  
...  

Tele-rehabilitation of patients with gait abnormalities could benefit from continuous monitoring of knee joint angle in the home and community. Continuous monitoring with mobile devices can be restricted by the number of body-worn sensors, signal bandwidth, and the complexity of operating algorithms. Therefore, this paper proposes a novel algorithm for estimating knee joint angle using lower limb angular velocity, obtained with only two leg-mounted gyroscopes. This gyroscope only (GO) algorithm calculates knee angle by integrating gyroscope-derived knee angular velocity signal, and thus avoids reliance on noisy accelerometer data. To eliminate drift in gyroscope data, a zero-angle update derived from a characteristic point in the knee angular velocity is applied to every stride. The concurrent validity and construct convergent validity of the GO algorithm was determined with two existing IMU-based algorithms, complementary and Kalman filters, and an optical motion capture system, respectively. Bland–Altman analysis indicated a high-level of agreement between the GO algorithm and other measures of knee angle.


Background: Gait patterns are influenced by various factors. Every person walks differently in different scenarios and it becomes difficult to identify the person correctly by his walking style. Research question: Is it possible to correctly recognize a person through his gait while he walks carrying load? What is the effect of distance on a person’s gait while he walks with carrying load? Objective: The paper is an attempt to study the effect of load on gait of a person. As the knee angle varies from person to person in varying conditions, we have studied the effect of load on knee angle and thus on gait recognition. Methods: Experimentation is done on 41 subjects of age group 18-30 years carrying bilateral weights of 2.5kg in both hands. Data collected from accelerometer has been studied using J48 decision tree classification algorithm. Results: Results of experiments shows 86.12% accuracy in recognizing subjects carrying load up to a distance of 60 meters on a flat surface. The FAR and FRR are found to be 0.77% and 27.52% respectively. Conclusion: Carrying load affects the gait of the subject. This makes it difficult to recognize the subject while he walks carrying load. After walking for some distance, the gait pattern and knee angle of the subject shows significant variations


Author(s):  
Olesya Gladushyna ◽  
Rolf Strietholt ◽  
Isa Steinmann

AbstractThe paper uses data from the combined TIMSS (Trends in International Mathematics and Science Study) and PIRLS (Progress in International Reading Literacy Study) assessment in 2011 to explore the subject-specific strengths and weaknesses among fourth grade students worldwide. Previous research came to the conclusion that students only differed in overall achievement levels and did not exhibit subject-specific strengths and weaknesses. This research did, however, not control for differences in overall performance levels when searching for profile differences. Therefore, the present study uses factor mixture analysis to study qualitatively different performance profiles in mathematics, reading, and science while controlling for differences in performance levels. Our findings suggest that the majority of students do not show pronounced strengths and weaknesses and differ mainly in performance levels across mathematics, reading, and science. At the same time, a smaller share of students does indeed show pronounced subject-specific strengths and weaknesses. This result does not represent an artefact, but we find clear and theory-conforming associations between the identified profiles and covariates. We find evidence for cross-country differences in the frequency of subject-specific strengths and weaknesses and gender differences, as well as differences between students who do not or only sometimes speak the language of test at home.


Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4966
Author(s):  
Xunju Ma ◽  
Yali Liu ◽  
Qiuzhi Song ◽  
Can Wang

Continuous joint angle estimation based on a surface electromyography (sEMG) signal can be used to improve the man-machine coordination performance of the exoskeleton. In this study, we proposed a time-advanced feature and utilized long short-term memory (LSTM) with a root mean square (RMS) feature and its time-advanced feature (RMSTAF; collectively referred to as RRTAF) of sEMG to estimate the knee joint angle. To evaluate the effect of joint angle estimation, we used root mean square error (RMSE) and cross-correlation coefficient ρ between the estimated angle and actual angle. We also compared three methods (i.e., LSTM using RMS, BPNN (back propagation neural network) using RRTAF, and BPNN using RMS) with LSTM using RRTAF to highlight its good performance. Five healthy subjects participated in the experiment and their eight muscle (i.e., rectus femoris (RF), biceps femoris (BF), semitendinosus (ST), gracilis (GC), semimembranosus (SM), sartorius (SR), medial gastrocnemius (MG), and tibialis anterior (TA)) sEMG signals were taken as algorithm inputs. Moreover, the knee joint angles were used as target values. The experimental results showed that, compared with LSTM using RMS, BPNN using RRTAF, and BPNN using RMS, the average RMSE values of LSTM using RRTAF were respectively reduced by 8.57%, 46.62%, and 68.69%, whereas the average ρ values were respectively increased by 0.31%, 4.15%, and 18.35%. The results demonstrated that LSTM using RRTAF, which contained the time-advanced feature, had better performance for estimating the knee joint motion.


2019 ◽  
Vol 6 ◽  
pp. 205566831986854 ◽  
Author(s):  
Rob Argent ◽  
Sean Drummond ◽  
Alexandria Remus ◽  
Martin O’Reilly ◽  
Brian Caulfield

Introduction Joint angle measurement is an important objective marker in rehabilitation. Inertial measurement units may provide an accurate and reliable method of joint angle assessment. The objective of this study was to assess whether a single sensor with the application of machine learning algorithms could accurately measure hip and knee joint angle, and investigate the effect of inertial measurement unit orientation algorithms and person-specific variables on accuracy. Methods Fourteen healthy participants completed eight rehabilitation exercises with kinematic data captured by a 3D motion capture system, used as the reference standard, and a wearable inertial measurement unit. Joint angle was calculated from the single inertial measurement unit using four machine learning models, and was compared to the reference standard to evaluate accuracy. Results Average root-mean-squared error for the best performing algorithms across all exercises was 4.81° (SD = 1.89). The use of an inertial measurement unit orientation algorithm as a pre-processing step improved accuracy; however, the addition of person-specific variables increased error with average RMSE 4.99° (SD = 1.83°). Conclusions Hip and knee joint angle can be measured with a good degree of accuracy from a single inertial measurement unit using machine learning. This offers the ability to monitor and record dynamic joint angle with a single sensor outside of the clinic.


2003 ◽  
Vol 15 (05) ◽  
pp. 186-192 ◽  
Author(s):  
WEN-LAN WU ◽  
JIA-HROUNG WU ◽  
HWAI-TING LIN ◽  
GWO-JAW WANG

The purposes of the present study were to (1) investigate the effects of the arm movement and initial knee joint angle employed in standing long jump by the ground reaction force analysis and three-dimensional motion analysis; and (2) investigate how the jump performance of the female gender related to the body configuration. Thirty-four healthy adult females performed standing long jump on a force platform with full effort. Body segment and joint angles were analyzed by three-dimensional motion analysis system. Using kinetic and kinematic data, the trajectories on mass center of body, knee joint angle, magnitude of peak takeoff force, and impulse generation in preparing phase were calculated. Average standing long jump performances with free arm motion were +1.5 times above performance with restricted arm motion in both knee initial angles. The performances with knee 90° initial flexion were +1.2 times above performance with knee 45° initial flexion in free and restricted arm motions. Judging by trajectories of the center mass of body (COM), free arm motion improves jump distance by anterior displacement of the COM in starting position. The takeoff velocity with 90° knee initial angle was as much as 11% higher than in with 45° knee initial angle. However, the takeoff angles on the COM trajectory showed no significant differences between each other. It was found that starting jump from 90° bend knee relatively extended the time that the force is applied by the leg muscles. To compare the body configurations and the jumping scores, there were no significant correlations between jump scores and anthropometry data. The greater muscle mass or longer leg did not correlated well with the superior jumping performance.


2012 ◽  
Vol 112 (4) ◽  
pp. 560-565 ◽  
Author(s):  
John McDaniel ◽  
Stephen J. Ives ◽  
Russell S. Richardson

Although a multitude of factors that influence skeletal muscle blood flow have been extensively investigated, the influence of muscle length on limb blood flow has received little attention. Thus the purpose of this investigation was to determine if cyclic changes in muscle length influence resting blood flow. Nine healthy men (28 ± 4 yr of age) underwent a passive knee extension protocol during which the subjects' knee joint was passively extended and flexed through 100–180° knee joint angle at a rate of 1 cycle per 30 s. Femoral blood flow, cardiac output (CO), heart rate (HR), stroke volume (SV), and mean arterial pressure (MAP) were continuously recorded during the entire protocol. These measurements revealed that slow passive changes in knee joint angle did not have a significant influence on HR, SV, MAP, or CO; however, net femoral blood flow demonstrated a curvilinear increase with knee joint angle ( r2 = 0.98) such that blood flow increased by ∼90% (125 ml/min) across the 80° range of motion. This net change in blood flow was due to a constant antegrade blood flow across knee joint angle and negative relationship between retrograde blood flow and knee joint angle ( r2 = 0.98). Thus, despite the absence of central hemodynamic changes and local metabolic factors, blood flow to the leg was altered by changes in muscle length. Therefore, when designing research protocols, researchers need to be cognizant of the fact that joint angle, and ultimately muscle length, influence limb blood flow.


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