scholarly journals Joint Kinetics and Kinematics During Common Lower Limb Rehabilitation Exercises

2015 ◽  
Vol 50 (10) ◽  
pp. 1011-1018 ◽  
Author(s):  
Paul Comfort ◽  
Paul Anthony Jones ◽  
Laura Constance Smith ◽  
Lee Herrington

Context  Unilateral body-weight exercises are commonly used to strengthen the lower limbs during rehabilitation after injury, but data comparing the loading of the limbs during these tasks are limited. Objective  To compare joint kinetics and kinematics during 3 commonly used rehabilitation exercises. Design  Descriptive laboratory study. Setting  Laboratory. Patients or Other Participants  A total of 9 men (age = 22.1 ± 1.3 years, height = 1.76 ± 0.08 m, mass = 80.1 ± 12.2 kg) participated. Intervention(s)  Participants performed the single-legged squat, forward lunge, and reverse lunge with kinetic data captured via 2 force plates and 3-dimensional kinematic data collected using a motion-capture system. Main Outcome Measure(s)  Peak ground reaction forces, maximum joint angles, and peak sagittal-joint moments. Results  We observed greater eccentric and concentric peak vertical ground reaction forces during the single-legged squat than during both lunge variations (P ≤ .001). Both lunge variations demonstrated greater knee and hip angles than did the single-legged squat (P < .001), but we observed no differences between lunges (P > .05). Greater dorsiflexion occurred during the single-legged squat than during both lunge variations (P < .05), but we noted no differences between lunge variations (P = .70). Hip-joint moments were greater during the forward lunge than during the reverse lunge (P = .003) and the single-legged squat (P = .011). Knee-joint moments were greater in the single-legged squat than in the reverse lunge (P < .001) but not greater in the single-legged squat than in the forward lunge (P = .41). Ankle-joint moments were greater during the single-legged squat than during the forward lunge (P = .002) and reverse lunge (P < .001). Conclusions  Appropriate loading progressions for the hip should begin with the single-legged squat and progress to the reverse lunge and then the forward lunge. In contrast, loading progressions for the knee and ankle should begin with the reverse lunge and progress to the forward lunge and then the single-legged squat.

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Anabèle Brière ◽  
Sylvie Nadeau ◽  
Séléna Lauzière ◽  
Denis Gravel

Background. The weight-bearing (WB) and effort distributions during the five-repetition sit-to-stand test (5R-STS) were assessed in healthy and hemiparetic subjects and were compared to the distributions obtained for a single STS task (1-STS). Methods. Eighteen hemiparetic subjects and 12 controls were included. The WB distribution and time were computed using the vertical ground reaction forces. The knee muscles' effort distribution was quantified with the electromyographic (EMG) data of the STS transfers expressed relatively to the EMG values of maximal strength assessments. Results. In both groups, the time, WB, and effort distributions did not differ between repetitions of the 5R-STS test. The WB and effort distributions of the first repetition were more asymmetrical than those for the 1-STS for the hemiparetic subjects only. Conclusions. Since no changes were found between repetitions, the 5R-STS test might not be demanding enough. The hemiparetic subjects adopt different WB and effort distribution strategies according to the number of STSs to complete.


Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7709
Author(s):  
Serena Cerfoglio ◽  
Manuela Galli ◽  
Marco Tarabini ◽  
Filippo Bertozzi ◽  
Chiarella Sforza ◽  
...  

Nowadays, the use of wearable inertial-based systems together with machine learning methods opens new pathways to assess athletes’ performance. In this paper, we developed a neural network-based approach for the estimation of the Ground Reaction Forces (GRFs) and the three-dimensional knee joint moments during the first landing phase of the Vertical Drop Jump. Data were simultaneously recorded from three commercial inertial units and an optoelectronic system during the execution of 112 jumps performed by 11 healthy participants. Data were processed and sorted to obtain a time-matched dataset, and a non-linear autoregressive with external input neural network was implemented in Matlab. The network was trained through a train-test split technique, and performance was evaluated in terms of Root Mean Square Error (RMSE). The network was able to estimate the time course of GRFs and joint moments with a mean RMSE of 0.02 N/kg and 0.04 N·m/kg, respectively. Despite the comparatively restricted data set and slight boundary errors, the results supported the use of the developed method to estimate joint kinetics, opening a new perspective for the development of an in-field analysis method.


1995 ◽  
Vol 3 (2) ◽  
pp. 86
Author(s):  
H.John Yack ◽  
Carole Tucker ◽  
Scott C White Heather Collins

2010 ◽  
Vol 71 (12) ◽  
pp. 1413-1416 ◽  
Author(s):  
David Levine ◽  
Denis J. Marcellin-Little ◽  
Darryl L. Millis ◽  
Verena Tragauer ◽  
Jason A. Osborne

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