scholarly journals The Effect of Ankle Support on Lower Limb Kinematics During the Y-Balance Test Using Non-linear Dynamic Measures

2019 ◽  
Vol 10 ◽  
Author(s):  
Herbert F. Jelinek ◽  
Kinda Khalaf ◽  
Julie Poilvet ◽  
Ahsan H. Khandoker ◽  
Lainey Heale ◽  
...  
Keyword(s):  
Lower Limb ◽  
Balance Test ◽  
Linear Dynamic ◽  
Non Linear ◽  
Limb Kinematics ◽  
Ankle Support ◽  
Dynamic Measures ◽  
Lower Limb Kinematics

Analysis of the Relationships between Balance Ability and Walking in Terms of Muscle Activities and Lower Limb Kinematics and Kinetics

Biomechanics ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 190-201
Author(s):  
Pathmanathan Cinthuja ◽  
Graham Arnold ◽  
Rami J. Abboud ◽  
Weijie Wang
Keyword(s):  
Regression Analysis ◽  
Lower Limb ◽  
Linear Regression Analysis ◽  
Multivariate Linear Regression Analysis ◽  
Vastus Medialis ◽  
Balance Test ◽  
Balance Ability ◽  
Limb Kinematics ◽  
Lower Limb Kinematics ◽  
Kinematics And Kinetics

There is a lack of evidence about the ways in which balance ability influences the kinematic and kinetic parameters and muscle activities during gait among healthy individuals. The hypothesis is that balance ability would be associated with the lower limb kinematics, kinetics and muscle activities during gait. Twenty-nine healthy volunteers (Age 32.8 ± 9.1; 18 males and 11 females) performed a Star Excursion Balance test to measure their dynamic balance and walked for at least three trials in order to obtain a good quality of data. A Vicon® 3D motion capture system and AMTI® force plates were used for the collection of the movement data. The selected muscle activities were recorded using Delsys® Electromyography (EMG). The EMG activities were compared using the maximum values and root mean squared (RMS) values within the participants. The joint angle, moment, force and power were calculated using a Vicon Plug-in-Gait model. Descriptive analysis, correlation analysis and multivariate linear regression analysis were performed using SPSS version 23. In the muscle activities, positive linear correlations were found between the walking and balance test in all muscles, e.g., in the multifidus (RMS) (r = 0.800 p < 0.0001), vastus lateralis (RMS) (r = 0.639, p < 0.0001) and tibialis anterior (RMS) (r = 0.539, p < 0.0001). The regression analysis models showed that there was a strong association between balance ability (i.e., reaching distance) and the lower limb muscle activities (i.e., vastus medialis–RMS) (R = 0.885, p < 0.0001), and also between balance ability (i.e., reaching distance) and the lower limb kinematics and kinetics during gait (R = 0.906, p < 0.0001). In conclusion, the results showed that vastus medialis (RMS) muscle activity mainly contributes to balance ability, and that balance ability influences the lower limb kinetics and kinematics during gait.

scholarly journals Lower limb kinematics and hip extensors strengths are associated with performance of runners at high risk of injury during the modified Star Excursion Balance Test

2020 ◽  
Vol 24 (6) ◽  
pp. 488-495 ◽  
Author(s):  
Larissa Santos Pinto Pinheiro ◽  
Juliana de Melo Ocarino ◽  
Natália Franco Netto Bittencourt ◽  
Thales Rezende Souza ◽  
Suelen Cristina de Souza Martins ◽  
...  
Keyword(s):  
High Risk ◽  
Lower Limb ◽  
Balance Test ◽  
Risk Of Injury ◽  
Limb Kinematics ◽  
Star Excursion Balance Test ◽  
Lower Limb Kinematics

Kinect-based assessment of lower limb kinematics and dynamic postural control during the star excursion balance test

2017 ◽  
Vol 58 ◽  
pp. 421-427 ◽  
Author(s):  
Moataz Eltoukhy ◽  
Christopher Kuenze ◽  
Jeonghoon Oh ◽  
Savannah Wooten ◽  
Joseph Signorile
Keyword(s):  
Postural Control ◽  
Lower Limb ◽  
Balance Test ◽  
Limb Kinematics ◽  
Star Excursion Balance Test ◽  
Lower Limb Kinematics

Rate of loading, but not lower limb kinematics or muscle activity, is moderated by limb and aerial variation when surfers land aerials

2021 ◽  
pp. 1-9
Author(s):  
James R. Forsyth ◽  
Christopher J. Richards ◽  
Ming-Chang Tsai ◽  
John W. Whitting ◽  
Diane L. Riddiford-Harland ◽  
...  
Keyword(s):  
Lower Limb ◽  
Muscle Activity ◽  
Limb Kinematics ◽  
Lower Limb Kinematics ◽  
Rate Of Loading

Changes in drive phase lower limb kinematics during a 60min cycling time trial

2012 ◽  
Vol 15 (2) ◽  
pp. 169-174 ◽  
Author(s):  
Mark G.L. Sayers ◽  
Amanda L. Tweddle ◽  
Joshua Every ◽  
Aaron Wiegand
Keyword(s):  
Lower Limb ◽  
Time Trial ◽  
Cycling Time Trial ◽  
Limb Kinematics ◽  
Lower Limb Kinematics ◽  
Cycling Time

scholarly journals Effects of gait retraining with focus on impact versus gait retraining with focus on cadence on pain, function and lower limb kinematics in runners with patellofemoral pain: Protocol of a randomized, blinded, parallel group trial with 6-month follow-up

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250965
Author(s):  
José Roberto de Souza Júnior ◽  
Pedro Henrique Reis Rabelo ◽  
Thiago Vilela Lemos ◽  
Jean-Francois Esculier ◽  
João Pedro da Silva Carto ◽  
...  
Keyword(s):  
Lower Limb ◽  
Intervention Group ◽  
Patellofemoral Pain ◽  
Control Group ◽  
Gait Retraining ◽  
Positive Effects ◽  
Clinical Applicability ◽  
Limb Kinematics ◽  
Lower Limb Kinematics

Patellofemoral pain (PFP) is one of the most prevalent injuries in runners. Unfortunately, a substantial part of injured athletes do not recover fully from PFP in the long-term. Although previous studies have shown positive effects of gait retraining in this condition, retraining protocols often lack clinical applicability because they are time-consuming, costly for patients and require a treadmill. The primary objective of this study will be to compare the effects of two different two-week partially supervised gait retraining programs, with a control intervention; on pain, function and lower limb kinematics of runners with PFP. It will be a single-blind randomized clinical trial with six-month follow-up. The study will be composed of three groups: a group focusing on impact (group A), a group focusing on cadence (group B), and a control group that will not perform any intervention (group C). The primary outcome measure will be pain assessed using the Visual Analog Pain scale during running. Secondary outcomes will include pain during daily activities (usual), symptoms assessed using the Patellofemoral Disorders Scale and lower limb running kinematics in the frontal (contralateral pelvic drop; hip adduction) and sagittal planes (foot inclination; tibia inclination; ankle dorsiflexion; knee flexion) assessed using the MyoResearch 3.14—MyoVideo (Noraxon U.S.A. Inc.). The study outcomes will be evaluated before (t0), immediately after (t2), and six months (t24) after starting the protocol. Our hypothesis is that both partially supervised gait retraining programs will be more effective in reducing pain, improving symptoms, and modifying lower limb kinematics during running compared with the control group, and that the positive effects from these programs will persist for six months. Also, we believe that one gait retraining group will not be superior to the other. Results from this study will help improve care in runners with PFP, while maximizing clinical applicability as well as time and cost-effectiveness.

scholarly journals Estimating Lower Limb Kinematics Using a Lie Group Constrained Extended Kalman Filter with a Reduced Wearable IMU Count and Distance Measurements

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6829
Author(s):  
Luke Wicent F. Sy ◽  
Nigel H. Lovell ◽  
Stephen J. Redmond
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Lie Group ◽  
Lower Limb ◽  
Sagittal Plane ◽  
Ultra Wideband ◽  
Distance Measurements ◽  
Limb Kinematics ◽  
Mean Square Errors ◽  
Lower Limb Kinematics

Tracking the kinematics of human movement usually requires the use of equipment that constrains the user within a room (e.g., optical motion capture systems), or requires the use of a conspicuous body-worn measurement system (e.g., inertial measurement units (IMUs) attached to each body segment). This paper presents a novel Lie group constrained extended Kalman filter to estimate lower limb kinematics using IMU and inter-IMU distance measurements in a reduced sensor count configuration. The algorithm iterates through the prediction (kinematic equations), measurement (pelvis height assumption/inter-IMU distance measurements, zero velocity update for feet/ankles, flat-floor assumption for feet/ankles, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). The knee and hip joint angle root-mean-square errors in the sagittal plane for straight walking were 7.6±2.6∘ and 6.6±2.7∘, respectively, while the correlation coefficients were 0.95±0.03 and 0.87±0.16, respectively. Furthermore, experiments using simulated inter-IMU distance measurements show that performance improved substantially for dynamic movements, even at large noise levels (σ=0.2 m). However, further validation is recommended with actual distance measurement sensors, such as ultra-wideband ranging sensors.

scholarly journals Time-Frequency Linearization of Reactive Cortical Responses for the Early Detection of Balance Losses

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Giovanni Mezzina ◽  
Daniela De Venuto
Keyword(s):  
Lower Limb ◽  
Fall Detection ◽  
Time Frequency ◽  
Average Value ◽  
Cortical Responses ◽  
Loss Of Balance ◽  
Spectrum Density ◽  
System Functioning ◽  
Limb Kinematics ◽  
Lower Limb Kinematics

Aiming at finding a fast and accurate preimpact fall detection (PIFD) strategy, this paper proposes a novel methodology that precociously discriminates the occurrence of unexpected loss of balance from the steady walking, by analyzing the subject’s cortical signal modifications (at the scalp level) in the time-frequency domain. In this study, the subjects were asked to walk at their preferred speed on the treadmill platform programmed to provide unexpected bilateral slippages. The proposed PIFD method exploits synchronously recorded electromyographic (EMG: 2 channels from the same lower limb muscle bundle, bilaterally) and electro-encephalographic (EEG: 13 channels from motor, sensory-motor and parietal cortex areas) signals. To validate the method offline, also, the lower limb kinematics has been reconstructed via a motion capture system (23 reflective markers and 8 fixed cameras). During the PIFD system functioning, the EMG signals from the lateral gastrocnemii are first translated in a binary waveform and then used to trigger the EEG analysis. Once enabled via EMG (every gait cycle), the EEG computation branch extracts and linearizes the rate of variation in the EEG power spectrum density (PSD) for five bands of interests: θ (4–7 Hz), α (8–12 Hz), β I, β II, β III rhythms (13–15 Hz, 16–20 Hz, and 21–28 Hz). The slope of the linearized trend identifies, in this context, the cortical responsiveness parameter. Experimental results from six subjects revealed that the proposed system can distinguish the loss of balance with an overall accuracy of ~96% (average value between sensitivity and specificity). The discrimination process requests, on average, 370.6 ms. This value could be considered suitable for the implementation of countermeasures aimed at restoring the balance of the subject.

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