Dataset Information

Comparison of Lower Extremity EMG Muscle Testing With Hands-Free Single Crutch vs Standard Axillary Crutches.

ABSTRACT:

Background

In order to maintain nonweightbearing restrictions of the lower extremity, an assistive device must be utilized. Currently most devices require the restricted limb to be held in a static position while the contralateral extremity provides forward propulsion. Atrophy and disuse conditions ensue rapidly, slowing healing and prolonging recovery. A hands-free single crutch (HFSC) utilizes both lower extremities, potentially reducing atrophy. The purpose of this study was to examine the electromyographic (EMG) differences between an HFSC and standard axillary crutches (SAC).

Methods

A prospective, crossover study was performed using 21 healthy volunteers from an active duty foot and ankle clinic. Demographic data were obtained and then subjects were fitted with an HFSC and SAC. Wireless surface EMG sensors were applied to the belly of the rectus femoris (RF), vastus lateralis (VL), lateral gastrocnemius (LG), and the gluteus maximus (GM) by a board-certified orthopedic surgeon. Subjects then ambulated at a self-selected velocity for 30 m while 15 seconds of the gait cycle were recorded for each device. Mean muscle activity and the maximum voluntary isometric contraction (MVIC) were recorded.

Results

The RF, GM, and LG showed significantly increased levels of muscle activity while using the HFSC compared to SAC (respectively P = .05, P = .03, P = .03). The VL did not show significantly higher muscle activity while using the HFSC (P = .051). The RF, GM, and VL showed statistically significant higher MVIC percentages while using the HFSC compared with SAC (respectively P = .005, P = .005, P = .013). The LG did not show significantly higher MVIC percentage while using the HFSC (P = .076).

Conclusion

The HFSC subjects demonstrated increased muscle recruitment and intensity while maintaining cyclic contractions consistent with bipedal gait pattern. SAC demonstrated less recruitment and intensity with an isometric pattern regardless of the phase of gait.

Clinical relevance

Muscle atrophy following lower extremity immobilization.

SUBMITTER: Dewar C

PROVIDER: S-EPMC8697267 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Neuromusculoskeletal disorders affecting walking ability are often difficult to manage, in part due to limited understanding of how a patient's lower extremity muscle excitations contribute to the patient's lower extremity joint moments. To assist in the study of these disorders, researchers have developed electromyography (EMG) driven neuromusculoskeletal models utilizing scaled generic musculoskeletal geometry. While these models can predict individual muscle contributions to lower extremity joint moments during walking, the accuracy of the predictions can be hindered by errors in the scaled geometry. This study presents a novel EMG-driven modeling method that automatically adjusts surrogate representations of the patient's musculoskeletal geometry to improve prediction of lower extremity joint moments during walking. In addition to commonly adjusted neuromusculoskeletal model parameters, the proposed method adjusts model parameters defining muscle-tendon lengths, velocities, and moment arms. We evaluated our EMG-driven modeling method using data collected from a high-functioning hemiparetic subject walking on an instrumented treadmill at speeds ranging from 0.4 to 0.8 m/s. EMG-driven model parameter values were calibrated to match inverse dynamic moments for five degrees of freedom in each leg while keeping musculoskeletal geometry close to that of an initial scaled musculoskeletal model. We found that our EMG-driven modeling method incorporating automated adjustment of musculoskeletal geometry predicted net joint moments during walking more accurately than did the same method without geometric adjustments. Geometric adjustments improved moment prediction errors by 25% on average and up to 52%, with the largest improvements occurring at the hip. Predicted adjustments to musculoskeletal geometry were comparable to errors reported in the literature between scaled generic geometric models and measurements made from imaging data. Our results demonstrate that with appropriate experimental data, joint moment predictions for walking generated by an EMG-driven model can be improved significantly when automated adjustment of musculoskeletal geometry is included in the model calibration process.