Optimization-based motor control of a Paralympic wheelchair athlete

Laschowski, Brock; Mehrabi, Naser; McPhee, John

doi:10.1007/s12283-018-0265-2

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…Among the studies that investigated wheelchair curling, 50% of them assessed kinematic trajectories. These studies measured a biomechanical model of the curling delivery to quantify angular joint velocities and range of motion through the curling delivery in relation to the dynamics of stone translation ( 50 – 52 ). Trunk and motor control stability of the curling wheelchair athlete was assessed within four studies ( 50 – 53 ); this analysis concluded wheelchair curling as a beneficial method of rehabilitation for spinal cord injury (SCI) patients ( 53 ).…”

Section: Resultsmentioning

confidence: 99%

An examination of studies related to the sport of curling: a scoping review

Zacharias,

Robak,

Passmore

2024

Front. Sports Act. Living

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IntroductionThere has been growth in research in the sport of curling over the past few decades. The need for a scoping review is warranted. This study's purpose was to identify and synthesize research evidence regarding quantitative variables for a series of components within the sport of curling.MethodsA scoping review of studies published and established within four databases was performed. One independent reviewer selected studies based on a systematic procedure. Inclusion criteria for studies were: (1) interventions that focused on the sport of curling; (2) quantitative in nature; (3) written in English; and (4) published within a peer-reviewed journal, a conference presentation, or a published thesis.ResultsSearching identified 8,467 articles and 94 met the inclusion criteria. Data were organized and synthesized based on the devised research variables from the sport of curling: The curl mechanism of the curling stone; the impact of sweeping on stone trajectory; curling delivery mechanics; wheelchair curling; technology analysis; strategy and tactics; psychological factors; injury occurrences; facility and arena infrastructure; and assessment of curling training and ability. The findings confirm the strong knowledge base that exists across game variables and unveil controversy between the underlying physics that produces curl, as well as the mechanisms of sweeping responsible for manipulating the stone trajectory.ConclusionsKnowledge derived from this review can assist researchers, coaches, and curlers in addressing the specific variables of the sport that influence stone trajectory and game results. Such awareness will expose gaps in the current understanding and foster new research endeavors to further the knowledge of the sport.

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Section: Resultsmentioning

confidence: 99%

An examination of studies related to the sport of curling: a scoping review

Zacharias,

Robak,

Passmore

2024

Front. Sports Act. Living

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“…A search in Web of Science (by applying the keywords: wheelchair, curling, and biomechanics) showed only one case study with a single subject dealing with the skill control/biomechanics of participating in wheelchair curling. The single-subject case study built a model of wheelchair curling [ 15 ] and performed thereafter a model-based optimization of curling skills [ 16 ]. It is known that the generalizability of a case study is always an issue.…”

Section: Introductionmentioning

confidence: 99%

“…The dominant skill of wheelchair curling is control of the upper limbs [ 15 , 16 , 22 ]. A previous injury study has revealed that wheelchair curling remains a low-risk sport in terms of possible muscular-skeleton injury and the provision of injury prevention for chronic upper limb conditions seems appropriate [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

The Proper Motor Control Model Revealed by Wheelchair Curling Quantification of Elite Athletes

Wang

Liu

Zhang

et al. 2022

Biology

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Background: Wheelchair users are disadvantaged when it comes to accruing the benefits of physical activities. Hence, promoting various sports is crucial for keeping this population healthy. Since wheelchair curling can be played by individuals from a wide range of ages, strengths, and endurance levels, it has potential to improve wheelchair users’ well-being. Yet, hardly any motion studies exist. This study aimed to facilitate understanding of optimized control of wheelchair curling for promoting wheelchair users’ participation. Methods: Using motion capture technology, nine national-level athletes were tested. Kinematic parameters related to segment/joint control and their coordination were quantified for both slow and fast curling. Descriptive statistics (means and standard deviations) and correlation analysis were applied for characterizing the skill. Results: (1) Curling control consists of an acceleration phase and a stabilizing delivery phase; (2) the control of trunk, shoulder, and wrist are responsible for accelerating the rock; (3) elbow control is accountable for the accurate delivery of the rock; and (4) during the slow curling, a synchronized effort of trunk, shoulder, and wrist is used for accelerating the rock, while a sequential control among the segment/joints is applied in fast curling. Conclusions: The results supply valuable motor learning markers that could have a significant positive impact on the teaching and learning of wheelchair curling, as such, the findings have great potential for the health promotion of wheelchair users.

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“…Knowing the muscle forces or activation signals during human movement can assist in understanding the underlying biomechanical systems (Crowninshield and Brand, 1981 ). These analyses can improve movement performance, especially for athletes and patients (Laschowski et al, 2018 ). Forward dynamic simulations of a given musculoskeletal model, driven via muscle activations, yield calculated motions ( Figure 1 ) (Ezati et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

InverseMuscleNET: Alternative Machine Learning Solution to Static Optimization and Inverse Muscle Modeling

Nasr

Inkol

Bell

et al. 2021

Front. Comput. Neurosci.

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InverseMuscleNET, a machine learning model, is proposed as an alternative to static optimization for resolving the redundancy issue in inverse muscle models. A recurrent neural network (RNN) was optimally configured, trained, and tested to estimate the pattern of muscle activation signals. Five biomechanical variables (joint angle, joint velocity, joint acceleration, joint torque, and activation torque) were used as inputs to the RNN. A set of surface electromyography (EMG) signals, experimentally measured around the shoulder joint for flexion/extension, were used to train and validate the RNN model. The obtained machine learning model yields a normalized regression in the range of 88–91% between experimental data and estimated muscle activation. A sequential backward selection algorithm was used as a sensitivity analysis to discover the less dominant inputs. The order of most essential signals to least dominant ones was as follows: joint angle, activation torque, joint torque, joint velocity, and joint acceleration. The RNN model required 0.06 s of the previous biomechanical input signals and 0.01 s of the predicted feedback EMG signals, demonstrating the dynamic temporal relationships of the muscle activation profiles. The proposed approach permits a fast and direct estimation ability instead of iterative solutions for the inverse muscle model. It raises the possibility of integrating such a model in a real-time device for functional rehabilitation and sports evaluation devices with real-time estimation and tracking. This method provides clinicians with a means of estimating EMG activity without an invasive electrode setup.

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Optimization-based motor control of a Paralympic wheelchair athlete

Cited by 9 publications

References 28 publications

An examination of studies related to the sport of curling: a scoping review

An examination of studies related to the sport of curling: a scoping review

The Proper Motor Control Model Revealed by Wheelchair Curling Quantification of Elite Athletes

InverseMuscleNET: Alternative Machine Learning Solution to Static Optimization and Inverse Muscle Modeling

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