Mechanisms of gait phase entrainment in healthy subjects during rhythmic electrical stimulation of the medial gastrocnemius

Thorp, Jenna E.; Adamczyk, Peter G.

doi:10.1371/journal.pone.0241339

Cited by 15 publications

(28 citation statements)

References 32 publications

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“…A similar phenomenon occurs in laboratory experiments where individuals are asked to walk on treadmills actuated with controlled oscillations in mediolateral 2 and vertical directions 3,4 . A recent study demonstrated human entrainment with periodic electrical stimulations of the medial gastrocnemius while walking on a conventional treadmill 5 . Furthermore, during development, infants can learn to entrain their bouncing frequency with the resonance of an elastic harness 6 .…”

Section: Introductionmentioning

confidence: 99%

The Role of Entrainment in Human Walking: Energy Minimization in Oscillating Environments

Schroeder¹,

Croft²,

Bertram³

2020

Preprint

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During locomotion, humans often entrain (i.e. synchronize) their steps to external oscillations: e.g. swaying bridges, tandem walking, bouncy harnesses, vibrating treadmills, exoskeletons. Previous studies have discussed the role of nonlinear oscillators (e.g. central pattern generators) in facilitating entrainment. However, the underlying benefits of entrainment are unknown. Given substantial evidence that humans prioritize economy during locomotion, we tested whether reduced metabolic expenditure accompanies human entrainment to vertical force oscillations, where frequency and amplitude were prescribed via a custom mechatronics system during walking. Although metabolic cost was not significantly reduced during entrainment, individuals who experienced negative work from oscillations had a higher cost than those who experienced positive work, and subjects generally selected phase relationships indicating the latter. It is possible that individuals use mechanical cues to infer energy cost and inform effective gait strategies. If so, an accurate prediction may rely on the relative stability of interactions with the environment. Our results suggest that entrainment is preferred over a wide range of oscillation parameters, though not as a direct priority for minimizing metabolic cost. Instead, entrainment may act to stabilize interactions with the environment, thus increasing predictability for the effective implementation of internal models that guide energy minimization.

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Section: Introductionmentioning

confidence: 99%

The Role of Entrainment in Human Walking: Energy Minimization in Oscillating Environments

Schroeder¹,

Croft²,

Bertram³

2020

Preprint

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show abstract

“…Since the ankle generates high positive power during push off, the alignment likely means that individuals chose to leverage positive power from the device, similar to our subjects. A different study found that individuals prefer to align pulses of electrical stimulation to the plantar flexors either just before toe off or in advance of heel strike 6 .…”

Section: Discussionmentioning

confidence: 98%

“…However, some of these studies have shown entrainment to be an uncommon response 3 . In a different experimental paradigm, human entrainment was demonstrated with periodic electrical stimulations of the medial gastrocnemius while walking on a conventional treadmill 6 . Furthermore, infants have shown the ability to learn entrainment of their bouncing frequency with the resonance of an elastic harness 7 .…”

Section: Introductionmentioning

confidence: 99%

Evaluating the energetics of entrainment in a human–machine coupled oscillator system

Schroeder

Croft

Bertram

2021

Sci Rep

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During locomotion, humans sometimes entrain (i.e. synchronize) their steps to external oscillations: e.g. swaying bridges, tandem walking, bouncy harnesses, vibrating treadmills, exoskeletons. Previous studies have discussed the role of nonlinear oscillators (e.g. central pattern generators) in facilitating entrainment. However, the energetics of such interactions are unknown. Given substantial evidence that humans prioritize economy during locomotion, we tested whether reduced metabolic expenditure is associated with human entrainment to vertical force oscillations, where frequency and amplitude were prescribed via a custom mechatronics system during walking. Although metabolic cost was not significantly reduced during entrainment, individuals expended less energy when the oscillation forces did net positive work on the body and roughly selected phase relationships that maximize positive work. It is possible that individuals use mechanical cues to infer energy cost and inform effective gait strategies. If so, an accurate prediction may rely on the relative stability of interactions with the environment. Our results suggest that entrainment occurs over a wide range of oscillation parameters, though not as a direct priority for minimizing metabolic cost. Instead, entrainment may act to stabilize interactions with the environment, thus increasing predictability for the effective implementation of internal models that guide energy minimization.

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“…It is interesting to note, that because muscle performance and injury propensity is often couched using relative rather than absolute measures of fascicle length (i.e., strain vs. mm), using ML models to track lengths of longer muscles would reduce the bearing of inaccuracies in data extraction. Furthermore, feeding ultrasound images along with data from other sensing modalities (e.g., electromyography [35], tensiometry [36,37]) thru ML schemes could provide improved measurement robustness and /or resolution.…”

Section: Limitations Areas For Improvement and Potential Applicationsmentioning

confidence: 99%

Machine learning to extract muscle fascicle length changes from dynamic ultrasound images in real-time

et al. 2021

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Background and objective Dynamic muscle fascicle length measurements through B-mode ultrasound have become popular for the non-invasive physiological insights they provide regarding musculoskeletal structure-function. However, current practices typically require time consuming post-processing to track muscle length changes from B-mode images. A real-time measurement tool would not only save processing time but would also help pave the way toward closed-loop applications based on feedback signals driven by in vivo muscle length change patterns. In this paper, we benchmark an approach that combines traditional machine learning (ML) models with B-mode ultrasound recordings to obtain muscle fascicle length changes in real-time. To gauge the utility of this framework for ‘in-the-loop’ applications, we evaluate accuracy of the extracted muscle length change signals against time-series’ derived from a standard, post-hoc automated tracking algorithm. Methods We collected B-mode ultrasound data from the soleus muscle of six participants performing five defined ankle motion tasks: (a) seated, constrained ankle plantarflexion, (b) seated, free ankle dorsi/plantarflexion, (c) weight-bearing, calf raises (d) walking, and then a (e) mix. We trained machine learning (ML) models by pairing muscle fascicle lengths obtained from standardized automated tracking software (UltraTrack) with the respective B-mode ultrasound image input to the tracker, frame-by-frame. Then we conducted hyperparameter optimizations for five different ML models using a grid search to find the best performing parameters for a combination of high correlation and low RMSE between ML and UltraTrack processed muscle fascicle length trajectories. Finally, using the global best model/hyperparameter settings, we comprehensively evaluated training-testing outcomes within subject (i.e., train and test on same subject), cross subject (i.e., train on one subject, test on another) and within/direct cross task (i.e., train and test on same subject, but different task). Results Support vector machine (SVM) was the best performing model with an average r = 0.70 ±0.34 and average RMSE = 2.86 ±2.55 mm across all direct training conditions and average r = 0.65 ±0.35 and average RMSE = 3.28 ±2.64 mm when optimized for all cross-participant conditions. Comparisons between ML vs. UltraTrack (i.e., ground truth) tracked muscle fascicle length versus time data indicated that ML tracked images reliably capture the salient qualitative features in ground truth length change data, even when correlation values are on the lower end. Furthermore, in the direct training, calf raises condition, which is most comparable to previous studies validating automated tracking performance during isolated contractions on a dynamometer, our ML approach yielded 0.90 average correlation, in line with other accepted tracking methods in the field. Conclusions By combining B-mode ultrasound and classical ML models, we demonstrate it is possible to achieve real-time tracking of human soleus muscle fascicles across a number of functionally relevant contractile conditions. This novel sensing modality paves the way for muscle physiology in-the-loop applications that could be used to modify gait via biofeedback or unlock novel wearable device control techniques that could enable restored or augmented locomotion performance.

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Mechanisms of gait phase entrainment in healthy subjects during rhythmic electrical stimulation of the medial gastrocnemius

Cited by 15 publications

References 32 publications

The Role of Entrainment in Human Walking: Energy Minimization in Oscillating Environments

The Role of Entrainment in Human Walking: Energy Minimization in Oscillating Environments

Evaluating the energetics of entrainment in a human–machine coupled oscillator system

Machine learning to extract muscle fascicle length changes from dynamic ultrasound images in real-time

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