arxiv: 2604.01130 · v3 · submitted 2026-04-01 · 💻 cs.LG · cs.CV

Toward Personalized Darts Training: A Data-Driven Framework Based on Skeleton-Based Biomechanical Analysis and Motion Modeling

Zhantao Chen , Dongyi He , Jin Fang , Xi Chen , Yishuo Liu , Xiaozhen Zhong , Xuejun Hu This is my paper

Pith reviewed 2026-05-13 22:22 UTC · model grok-4.3

classification 💻 cs.LG cs.CV

keywords personalized trainingdart throwingbiomechanical analysismotion modelingskeleton-based featuresminimum jerk criteriondeviation diagnosiskinematic features

0 comments p. Extension

The pith

A data-driven framework personalizes darts training by evaluating throws against each athlete's own optimal motion range instead of a uniform standard.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a closed-loop system that captures dart throws with a Kinect depth sensor, extracts 18 kinematic features from coordination, release velocity, joint angles, and stability, then generates personalized reference trajectories. These references blend an athlete's historical high-quality throws with the minimum jerk criterion to produce smooth, natural models. A z-score diagnosis module then identifies deviations such as unstable trunk posture or imbalanced elbow motion and supplies targeted recommendations. Traditional methods rely on experience, local metrics, or fixed templates that ignore individual variability, limiting personalization. If the approach holds, training feedback becomes more interpretable and adaptable across skill levels for darts and similar precision sports.

Core claim

By constructing personalized optimal throwing trajectories from historical high-quality samples combined with the minimum jerk criterion and diagnosing deviations through z-scores on biomechanical features, the framework shifts evaluation from deviation from a uniform standard to deviation from an individual's optimal control range.

What carries the argument

The personalized optimal throwing trajectory model, which merges an athlete's high-quality historical samples with the minimum jerk criterion to create smooth reference paths, together with a hierarchical z-score deviation diagnosis module that maps feature deviations to actionable recommendations.

If this is right

The system detects specific issues such as poor trunk stability, abnormal elbow displacement, and velocity imbalance, then issues targeted training recommendations.
It adapts to both professional and non-professional athletes by drawing on their individual data sets of 2,396 throws.
The closed-loop pipeline from markerless capture through feature extraction to feedback supports iterative training cycles.
The same shift from uniform to individualized reference ranges applies to other high-precision target sports requiring coordinated release and stability.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Real-time versions of the framework could be paired with wearable sensors to deliver immediate corrective cues during live practice.
The emphasis on natural minimum-jerk paths may lower overuse injury risk by discouraging compensatory movements outside an athlete's optimal range.
The method could extend to youth or rehabilitation settings where body proportions and motor control differ markedly from elite norms.

Load-bearing premise

Historical high-quality samples combined with the minimum jerk criterion reliably yield accurate personalized optimal trajectories, and z-score thresholds on the features produce valid and actionable deviation diagnoses.

What would settle it

A study in which athletes trained with the personalized trajectories show no greater improvement in accuracy or consistency than a control group using standard coaching, or in which the generated trajectories are judged unrealistic by expert coaches.

Figures

Figures reproduced from arXiv: 2604.01130 by Dongyi He, Jin Fang, Xiaozhen Zhong, Xi Chen, Xuejun Hu, Yishuo Liu, Zhantao Chen.

**Figure 1.** Figure 1: Four Factors Affecting Darts Throwing (A) Postural Stability: Demonstrates the supporting role of global postural control. By establishing a stable foundation and minimizing the sway of the body’s center of mass and trunk, it effectively suppresses the error amplification effect transmitted from lower-body disturbances to the upper-body kinetic chain. (B) Joint Angles: This section explains the spatial cha… view at source ↗

**Figure 2.** Figure 2: Data preprocessing and dart-to-bullseye distance measurement pipeline. Figure (a) displays the raw captured image exhibiting significant perspective distortion and tilt. Figure (b) shows the geometrically restored image following an inverse perspective projection using chessboard calibration parameters. Figure (c) illustrates the bullseye localization process, isolating the red outer ring via HSV color spa… view at source ↗

**Figure 3.** Figure 3: Flow chart of optimal trajectory fitting. This figure illustrates the data-driven pipeline for generating a personalized optimal dart-throwing trajectory. The process initiates by aggregating the athlete’s most recent 200 throwing actions from historical data. Subsequently, a high-quality subset of 30 representative actions is extracted using a rigorous weighting scheme that evaluates both dart-to-bullseye… view at source ↗

**Figure 4.** Figure 4: Visualization of the optimal trajectories for seven dart players. The figures in this experiment visualize the fitting results. The top four images show the fitting results for professional athletes, while the bottom three show those for non-professional athletes. You can observe each athlete’s throwing process and hand movements. The green segments represent the motion from the wrist to the fingertips, or… view at source ↗

**Figure 5.** Figure 5: Fitted optimal throwing trajectory for athlete S3. This figure shows the time series of the personalized optimal throwing trajectory generated using the minimum jitter fitting method (from frame 15 to frame 120). The red curve depicts a smooth and continuous spatial path of the hand in the XY plane, indicating no excessive trembling or unnecessary fluctuations. Consistent with the principles of the three-l… view at source ↗

**Figure 6.** Figure 6: Not standard action from S6. This figure presents a non-standard throwing motion captured from a non-professional athlete across six sequential frames. The overlaid skeletal tracking highlights multiple kinematic deviations compared to an ideal reference trajectory. For instance, the sequence visually reveals inconsistencies such as noticeable trunk sway, inadequate head stability, and abnormal elbow displ… view at source ↗

read the original abstract

As sports training becomes more data-driven, traditional dart coaching based mainly on experience and visual observation is increasingly inadequate for high-precision, goal-oriented movements. Although prior studies have highlighted the importance of release parameters, joint motion, and coordination in dart throwing, most quantitative methods still focus on local variables, single-release metrics, or static template matching. These approaches offer limited support for personalized training and often overlook useful movement variability. This paper presents a data-driven dart training assistance system. The system creates a closed-loop framework spanning motion capture, feature modeling, and personalized feedback. Dart-throwing data were collected in markerless conditions using a Kinect 2.0 depth sensor and an optical camera. Eighteen kinematic features were extracted from four biomechanical dimensions: three-link coordination, release velocity, multi-joint angular configuration, and postural stability. Two modules were developed: a personalized optimal throwing trajectory model that combines historical high-quality samples with the minimum jerk criterion, and a motion deviation diagnosis and recommendation model based on z-scores and hierarchical logic. A total of 2,396 throwing samples from professional and non-professional athletes were collected. Results show that the system generates smooth personalized reference trajectories consistent with natural human movement. Case studies indicate that it can detect poor trunk stability, abnormal elbow displacement, and imbalanced velocity control, then provide targeted recommendations. The framework shifts dart evaluation from deviation from a uniform standard to deviation from an individual's optimal control range, improving personalization and interpretability for darts training and other high-precision target sports.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper builds a Kinect-based pipeline for personalized darts trajectories via min-jerk on historical samples plus z-score diagnosis, but supplies only qualitative case studies with no quantitative checks on accuracy or actionability.

read the letter

The core of this work is a closed-loop system that records dart throws markerlessly, extracts 18 kinematic features across coordination, release velocity, joint angles, and stability, then generates an individual reference trajectory by blending high-quality past throws with the minimum-jerk criterion and flags deviations through hierarchical z-scores. That pipeline and the shift from uniform standards to a person's own control range are the actual new pieces for the darts domain. The dataset of 2396 throws from both professional and non-professional athletes is a solid starting point, and the case studies do show the features can surface issues like trunk instability or elbow displacement in a way that feels interpretable for coaches. Those elements give the framework a practical engineering flavor that could be useful in applied sports tech. The main weakness is the absence of any hard numbers. There are no reconstruction errors on held-out throws, no comparisons against simpler averaging or other trajectory models, no ablation on the feature set, and no outcome data showing that the z-score flags improve throwing accuracy or training results. The z-score thresholds and sample selection criteria also appear tuned directly to the collected data, which raises the usual circularity concern. Without those checks the claim that the trajectories are individually optimal rather than merely smooth remains untested. This is the kind of applied paper that might interest researchers building tools for precision sports or biomechanics groups working on small-target tasks. A reader looking for concrete implementation ideas on feature extraction and feedback logic could extract value from the description even if the validation is light. It deserves peer review because the data volume and the specific application are reasonable, but any referee would need to see quantitative validation experiments added before acceptance.

Referee Report

3 major / 2 minor

Summary. The paper presents a data-driven framework for personalized darts training that collects 2,396 throwing samples via Kinect 2.0, extracts 18 kinematic features across three-link coordination, release velocity, multi-joint configuration, and postural stability, builds a personalized optimal trajectory model by combining historical high-quality samples with the minimum-jerk criterion, and implements a z-score-based deviation diagnosis module with hierarchical recommendations. It claims this shifts evaluation from uniform standards to an individual's optimal control range, supported by qualitative case studies showing detection of issues such as poor trunk stability and imbalanced velocity control.

Significance. If quantitatively validated, the approach could meaningfully advance personalized coaching in precision sports by replacing generic templates with individual-specific biomechanical references, leveraging a sizable dataset and integrating motion capture with interpretable feedback; the emphasis on movement variability and closed-loop personalization is a conceptual strength that could extend to other target sports.

major comments (3)

[Abstract/Results] Abstract and Results sections: the central claims rest on the trajectory model and z-score diagnoses, yet the manuscript supplies only qualitative statements that trajectories are 'smooth' and 'consistent with natural movement' plus case studies; no withheld-sample reconstruction error, no baseline comparisons, no ablation results, and no outcome metrics (e.g., throwing accuracy improvement or inter-rater reliability) are reported, leaving the personalization benefit unsupported.
[Trajectory Model] Trajectory model description: the personalized optimal trajectories are derived directly from the same historical high-quality samples later used for evaluation, creating circularity; no independent test set, cross-validation procedure, or quantitative check against actual expert kinematics is described to establish that the minimum-jerk outputs are biomechanically optimal rather than merely smooth.
[Diagnosis Model] Diagnosis and recommendation module: z-score thresholds for flagging deviations on the 18 features appear selected post hoc to align with the collected data, with no sensitivity analysis, justification, or validation against external ground truth; this undermines the claim that diagnoses are actionable and valid.

minor comments (2)

[Abstract] Abstract: specify the split between professional and non-professional athletes within the 2,396 samples to clarify generalizability.
[Methods] Methods: provide explicit formulas or pseudocode for computing each of the 18 kinematic features from the four biomechanical dimensions.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for the constructive and detailed feedback, which highlights important areas for strengthening the quantitative support of our framework. We address each major comment below and indicate the revisions we will make.

read point-by-point responses

Referee: [Abstract/Results] Abstract and Results sections: the central claims rest on the trajectory model and z-score diagnoses, yet the manuscript supplies only qualitative statements that trajectories are 'smooth' and 'consistent with natural movement' plus case studies; no withheld-sample reconstruction error, no baseline comparisons, no ablation results, and no outcome metrics (e.g., throwing accuracy improvement or inter-rater reliability) are reported, leaving the personalization benefit unsupported.

Authors: We agree that the current manuscript emphasizes qualitative case studies and lacks explicit quantitative validation metrics. In the revised version, we will add withheld-sample reconstruction error for the trajectory model, baseline comparisons against non-personalized minimum-jerk trajectories, and ablation results on the 18-feature set. We will also report inter-rater reliability for the diagnosis module where applicable. However, outcome metrics such as throwing accuracy improvement require a longitudinal intervention study that is outside the scope of this framework-development paper; we will explicitly note this limitation and outline it as future work. revision: partial
Referee: [Trajectory Model] Trajectory model description: the personalized optimal trajectories are derived directly from the same historical high-quality samples later used for evaluation, creating circularity; no independent test set, cross-validation procedure, or quantitative check against actual expert kinematics is described to establish that the minimum-jerk outputs are biomechanically optimal rather than merely smooth.

Authors: We acknowledge the risk of circularity when high-quality samples are used both to construct the personalized model and for subsequent evaluation. To resolve this, we will introduce a cross-validation scheme that partitions the high-quality throws into training and held-out test subsets. We will also add quantitative comparisons of the minimum-jerk outputs against kinematic profiles from expert throws excluded from model fitting, thereby providing an independent check on biomechanical plausibility beyond smoothness. revision: yes
Referee: [Diagnosis Model] Diagnosis and recommendation module: z-score thresholds for flagging deviations on the 18 features appear selected post hoc to align with the collected data, with no sensitivity analysis, justification, or validation against external ground truth; this undermines the claim that diagnoses are actionable and valid.

Authors: The z-score thresholds were initially chosen using conventional statistical cutoffs (|z| > 2) and then tuned to the observed variability in our 2,396-sample dataset. In the revision we will add a sensitivity analysis that varies the thresholds and reports the resulting changes in flagged deviations and recommendation stability. We will also supply explicit justification drawn from prior biomechanical studies on acceptable movement variability in precision throwing tasks and will discuss the need for external ground-truth validation in future work. revision: partial

standing simulated objections not resolved

Outcome metrics such as throwing accuracy improvement or inter-rater reliability of recommendations, because these require a separate longitudinal training study with pre/post performance measurements that was not part of the current framework-development work.

Circularity Check

1 steps flagged

Trajectory model fitted directly to historical high-quality samples then used to diagnose deviations from those samples

specific steps

fitted input called prediction [Abstract]
"a personalized optimal throwing trajectory model that combines historical high-quality samples with the minimum jerk criterion, and a motion deviation diagnosis and recommendation model based on z-scores and hierarchical logic. A total of 2,396 throwing samples from professional and non-professional athletes were collected. Results show that the system generates smooth personalized reference trajectories consistent with natural human movement. Case studies indicate that it can detect poor trunk stability, abnormal elbow displacement, and imbalanced velocity control"

The optimal trajectory is explicitly constructed from the historical high-quality samples; the subsequent z-score diagnosis then measures deviation from this fitted model on features drawn from the same 2,396-sample pool. The 'personalized optimal control range' therefore reduces to a statistical property of the input data by construction, with no separate validation step shown to establish that the min-jerk fit on high-quality samples yields an individually optimal trajectory rather than merely a smoothed version of the observed data.

full rationale

The paper's central personalization step constructs the 'optimal' reference trajectory by combining the same historical high-quality samples with the minimum-jerk criterion, then applies z-score thresholds on kinematic features extracted from the collected data to diagnose deviations. This creates a moderate dependence where the reference range is a direct statistical summary of the input samples rather than an independently validated biomechanical optimum. No withheld-sample validation or external outcome measure is reported to break the dependence. The remainder of the framework (feature extraction, Kinect capture, hierarchical logic) operates on independent biomechanical quantities and does not reduce to the same inputs.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The framework rests on standard biomechanical assumptions and statistical tools with a small number of fitted or chosen elements; no new physical entities are postulated.

free parameters (2)

z-score thresholds for deviation flagging
Chosen to identify abnormal trunk stability, elbow displacement, and velocity control in the diagnosis module.
historical sample selection criteria for trajectory model
High-quality samples are selected from the 2396 throws to anchor the personalized minimum-jerk paths.

axioms (1)

domain assumption Minimum jerk criterion generates trajectories consistent with natural human movement
Invoked to combine historical samples into smooth personalized reference paths.

pith-pipeline@v0.9.0 · 5595 in / 1299 out tokens · 41830 ms · 2026-05-13T22:22:23.713095+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

personalized optimal throwing trajectory model that combines historical high-quality samples with the minimum jerk criterion... A* = argmin ½‖A−fA*‖²_F + λ/2 ‖D³A‖²_F (eq. 11)
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

motion deviation diagnosis... based on z-scores... assessment(z_j) = acceptable if |z_j|≤1.0, slight if 1<|z_j|≤2.0, significant if >2.0 (eq. 12)

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

42 extracted references · 42 canonical work pages

[1]

C., Jaspers, A

Houtmeyers, K. C., Jaspers, A. & Figueiredo, P. Managing the training process in elite sports: from descriptive to prescriptive data analytics.Int. J. Sports Physiol. Perform.16, 1719–1723 (2021)

work page 2021
[2]

& Ganesh, G

Ikegami, T. & Ganesh, G. Watching novice action degrades expert motor performance: causation between action production and outcome prediction of observed actions by humans.Sci. reports4, 6989 (2014)

work page 2014
[3]

& Hopker, J

Passfield, L. & Hopker, J. G. A mine of information: can sports analytics provide wisdom from your data?Int. journal sports physiology performance12, 851–855 (2017)

work page 2017
[4]

A., Robinson, M

Wallace, E., Ditroilo, M., Exell, T. A., Robinson, M. & Vanicek, N. Sports medicine and biomechanics–synergies and nuances.J. Sports Sci.40, 838–839 (2022)

work page 2022
[5]

mechanics

Vigotsky, A. D., Zelik, K. E., Lake, J. & Hinrichs, R. N. Mechanical misconceptions: Have we lost the “mechanics” in “sports biomechanics”?J. Biomech.93, 1–5 (2019)

work page 2019
[6]

& Messou, P

Nagahara, R., Girard, O. & Messou, P. A. Rates of ground reaction force development are associated with running speed during sprint acceleration.Int. J. Sports Sci. & Coach.20, 123–129 (2025)

work page 2025
[7]

Aguinaldo, A., Cardinale, T., La Salle, D. T. & Escamilla, R. Estimating joint kinetics during baseball pitching using in-stadium markerless motion capture: 628.Medicine & Sci. Sports & Exerc.57, 201–202 (2025)

work page 2025
[8]

M., Leonard, N

Scarborough, D. M., Leonard, N. K., Mayer, L. W., Oh, L. S. & Berkson, E. M. The association of baseball pitch delivery and kinematic sequence on stresses at the shoulder and elbow joints.J. Sports Sci. & Medicine20, 94 (2021)

work page 2021
[9]

K., Ardern, C

Yung, K. K., Ardern, C. L., Serpiello, F. R. & Robertson, S. Characteristics of complex systems in sports injury rehabilitation: examples and implications for practice.Sports medicine-open8, 24 (2022). 10.Glazier, P. S. Towards a grand unified theory of sports performance.Hum. movement science56, 139–156 (2017)

work page 2022
[10]

Bolt, R., Heuvelmans, P., Benjaminse, A., Robinson, M. A. & Gokeler, A. An ecological dynamics approach to acl injury risk research: a current opinion.Sports biomechanics23, 1592–1605 (2024)

work page 2024
[11]

T., Hashmi, M

Naik, B. T., Hashmi, M. F. & Bokde, N. D. A comprehensive review of computer vision in sports: Open issues, future trends and research directions.Appl. Sci.12, 4429 (2022)

work page 2022
[12]

Bridging the lab-to-field gap using machine learning: a narrative review.Sports Biomech.24, 2779–2798 (2025)

Mundt, M. Bridging the lab-to-field gap using machine learning: a narrative review.Sports Biomech.24, 2779–2798 (2025). 21/23

work page 2025
[13]

& Yao, X

Chen, C., Xue, J., Gou, W., Xie, M. & Yao, X. Quantitative analysis and evaluation of research on the application of computer vision in sports since the 21st century.Front. Sports Active Living7, DOI: 10.3389/fspor.2025.1604232 (2025)

work page doi:10.3389/fspor.2025.1604232 2025
[14]

& Choi, D

Yang, Y ., Kim, D. & Choi, D. Ball tracking and trajectory prediction system for tennis robots.J. Comput. Des. Eng.10, 1176–1184 (2023)

work page 2023
[15]

& Ikenaga, T

Tian, L., Cheng, X., Honda, M. & Ikenaga, T. Multi-view 3d human pose reconstruction based on spatial confidence point group for jump analysis in figure skating.Complex & Intell. Syst.9, 865–879 (2023)

work page 2023
[16]

E., Sweeting, A

Cust, E. E., Sweeting, A. J., Ball, K. & Robertson, S. Machine and deep learning for sport-specific movement recognition: A systematic review of model development and performance.J. sports sciences37, 568–600 (2019). 18.Rezaei, A. & Wu, L. C. Automated soccer head impact exposure tracking using video and deep learning.Sci. reports12, 9282 (2022)

work page 2019
[17]

& Hauser, T

Hecksteden, A., Keller, N., Zhang, G., Meyer, T. & Hauser, T. Why humble farmers may in fact grow bigger potatoes: a call for street-smart decision-making in sport.Sports Medicine-Open9, 94 (2023)

work page 2023
[18]

biomechanics81, 1–11 (2018)

Halilaj, E.et al.Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities.J. biomechanics81, 1–11 (2018)

work page 2018
[19]

& Robins, M

Bartlett, R., Wheat, J. & Robins, M. Is movement variability important for sports biomechanists?Sports biomechanics6, 224–243 (2007)

work page 2007
[20]

L., Evans, M., Cosker, D

Colyer, S. L., Evans, M., Cosker, D. P. & Salo, A. I. A review of the evolution of vision-based motion analysis and the integration of advanced computer vision methods towards developing a markerless system.Sports medicine-open4, 24 (2018)

work page 2018
[21]

D.et al.Sensing and characterization of the wrist using dart thrower’s movement.IEEE Sensors J.18, 4145–4153, DOI: 10.1109/jsen.2018.2821243 (2018)

Nguyen, N. D.et al.Sensing and characterization of the wrist using dart thrower’s movement.IEEE Sensors J.18, 4145–4153, DOI: 10.1109/jsen.2018.2821243 (2018)

work page doi:10.1109/jsen.2018.2821243 2018
[22]

Davis, L. ‘stand up if you love the darts!’–understanding the key catalysts responsible for the rapid transformation of professional darts corporation (pdc) darts during the 2000s.Sport Hist.44, 1–28 (2024)

work page 2024
[23]

& Isableu, B

Rezzoug, N., Hansen, C., Gorce, P. & Isableu, B. Contribution of interaction torques during dart throwing: Differences between novices and experts.Hum. movement science57, 258–266 (2018)

work page 2018
[24]

From a pub game to a sporting spectacle: the professionalisation of british darts, 1970-1997.Sport Hist.38, 507–533, DOI: 10.1080/17460263.2018.1511462 (2018)

Davis, L. From a pub game to a sporting spectacle: the professionalisation of british darts, 1970-1997.Sport Hist.38, 507–533, DOI: 10.1080/17460263.2018.1511462 (2018)

work page doi:10.1080/17460263.2018.1511462 1970
[25]

F.et al.Darts fast-learning reduces theta power but is not affected by hf-trns: A behavioral and electrophysiological investigation.Brain Res.1846, 149249 (2025)

Scaramuzzi, G. F.et al.Darts fast-learning reduces theta power but is not affected by hf-trns: A behavioral and electrophysiological investigation.Brain Res.1846, 149249 (2025)

work page 2025
[26]

Song, S. H. & Han, D. W. The influence of balance training on accuracy, quiet eye, and power spectrum in dart-throwing task.Percept. Mot. Ski.00315125251395552(2025)

work page 2025
[27]

Searching for flow: A performance optimization intervention with a professional dart thrower.Case Stud

Whitehead, A. Searching for flow: A performance optimization intervention with a professional dart thrower.Case Stud. Sport Perform. Psychol.9, 93–99 (2025)

work page 2025
[28]

& Strauss, B

Greve, J., van Meurs, E. & Strauss, B. Elite darts performance and the social influence of real crowds and simulated crowd noise.Sci. Reports13, 12346 (2023)

work page 2023
[29]

W., Crisco, J

Wolfe, S. W., Crisco, J. J., Orr, C. M. & Marzke, M. W. The dart-throwing motion of the wrist: is it unique to humans? The J. hand surgery31, 1429–1437 (2006)

work page 2006
[30]

radiology69, 462–467 (2014)

Lee, S.et al.Ct-based three-dimensional kinematic comparison of dart-throwing motion between wrists with malunited distal radius and contralateral normal wrists.Clin. radiology69, 462–467 (2014). 33.Robalino, J.et al.Kinetic chain contribution to speed and energy in karate techniques.Appl. Sci.15, 9726 (2025)

work page 2014
[31]

& Yamamoto, Y

Kudo, K., Tsutsui, S., Ishikura, T., Ito, T. & Yamamoto, Y . Compensatory coordination of release parameters in a throwing task.J. motor behavior32, 337–345 (2000)

work page 2000
[32]

& O’mathuna, C

Walsh, M., Tyndyk, M., Barton, J., O’Flynn, B. & O’mathuna, C. Biomechanical performance measurement using wireless inertial sensors for professional and recreational darts players.Br. journal sports medicine45, A10–A10 (2011)

work page 2011
[33]

& Tyndyk, M

Walsh, M., Barton, J., O’Flynn, B., O’Mathuna, C. & Tyndyk, M. Capturing the overarm throw in darts employing wireless inertial measurement. InSENSORS, 2011 IEEE, 1441–1444 (IEEE, 2011)

work page 2011
[34]

N., Yano, S

Tran, B. N., Yano, S. & Kondo, T. Coordination of human movements resulting in motor strategies exploited by skilled players during a throwing task.PLoS One14, e0223837 (2019). 22/23 38.Nasu, D., Matsuo, T. & Kadota, K. Two types of motor strategy for accurate dart throwing.PLoS One9, e88536 (2014)

work page 2019
[35]

& Gastaldi, L

Panero, E., Agostini, V . & Gastaldi, L. Biomechanical assessment of throwing gesture and performance in female water-polo players.Appl. Sci.12, 7856 (2022)

work page 2022
[36]

Herring, R. M. & Chapman, A. E. Effects of changes in segmental values and timing of both torque and torque reversal in simulated throws.J. Biomech.25, 1173–1184 (1992)

work page 1992
[37]

& Schmiedeler, J

Ambike, S. & Schmiedeler, J. P. The leading joint hypothesis for spatial reaching arm motions.Exp. brain research224, 591–603 (2013)

work page 2013
[38]

& Aune, T

Van den Tillaar, R. & Aune, T. K. Effect of instructions emphasizing velocity or accuracy given in a random or blocked order on performance testing and kinematics in dart throwing.Front. Psychol.10, 1359 (2019)

work page 2019
[39]

& Potts, J

James, D. & Potts, J. Experimental validation of dynamic stability analysis applied to dart flight.Sports Eng.21, 347–358 (2018)

work page 2018
[40]

E., Park, S

Zhang, Z., Guo, D., Huber, M. E., Park, S. W. & Sternad, D. Exploiting the geometry of the solution space to reduce sensitivity to neuromotor noise.PLoS Comput. Biol.14(2018). 45.Juras, G. & Słomka, K. Anticipatory postural adjustments in dart throwing.J. Hum. Kinetics37, 39 (2013)

work page 2018
[41]

& Kováˇciková, Z

Zemková, E. & Kováˇciková, Z. Sport-specific training induced adaptations in postural control and their relationship with athletic performance.Front. Hum. Neurosci.16, 1007804 (2023)

work page 2023
[42]

& Takiyama, K

Furuki, D. & Takiyama, K. Detecting the relevance to performance of whole-body movements.Sci. Reports7, 15659 (2017). 23/23

work page 2017