K2 crew performance: a preliminary investigation of kinetic parameters in preferred and inverted positions among sub-elite kayakers

K2 performance depends on different kinematic and kinetic variables. Due to the lack of related studies in this area, we have tried to explain these features to better understand the best positioning of paddlers and how their synchronization affects performance. This study uses the DAQ system comprising two instrumented paddles—an IMU and a GPS (“E-kayak” system)—to investigate paddle synchronization and the specific positioning of paddlers’ in preferred and inverted configurations. In this study, 10 sub-elite paddlers participated, divided into five crews. The test included two trials of 500 m performed in preferred and inverted seating positions. The synchronization analysis highlighted that the rear paddler contributed efficiently to the propulsion of the boat while performing 30–40 ms earlier than the front paddler during the entry and exit phases. Despite the time results for 500 m, there is no evidence indicating a dominant indication of the preferred or inverted position among the athletes. The results show a significant correlation (p < 0.05) between the force of the front paddler (r = −0.88), the stroke frequency of the crew (r = −0.66), and the total force applied by the crew with the time for 500 m and between stroke frequency and the force of the front paddler (r = 0.64). Based on these indications, for only those crews who completed the 500 m test in the shortest time, the equation determining the time over 500 m was calculated using multiple regression analysis, considering the stroke frequency and the force of the front and rear paddler. The data showed a good estimation with CV% = 0.22, ICC = 0.99, and ES = −0.005. In conclusion, these findings can serve as a beneficial tool for assessing or monitoring K2 crew performance in sub-elite paddlers.

Introduction

Flatwater sprint kayaking is an Olympic discipline with individual (K1) and team competitions (K2 and K4) held over distances of 200, 500, and 1,000 m. In K2 kayaking, two athletes sit in tandem and, through cyclic and synchronized paddle movements, generate the propulsive force needed to overcome water resistance and accelerate the kayak (Romagnoli et al., 2022a; Romagnoli et al., 2022b). The synchronization between the athletes (referring to the temporal coordination of the different phases of their paddling cycle) is influenced by the kayak’s design and the athletes’ paddling timing (King and de Rond, 2011; Tay and Kong, 2018). This aspect plays a crucial role in avoiding paddle contact (Tay and Kong, 2018). Correct synchronization has been identified as a critical determinant of performance in kayak sprinting (Robinson et al., 2011; Bonaiuto et al., 2022; Romagnoli et al., 2024) and rowing (Wing and Woodburn, 1995; King and de Rond, 2011; Cuijpers et al., 2015). Although Kong et al. (2020b) suggested that a well-synchronized crew maintaining the same paddling rhythm can enhance performance, several studies indicate that slight asynchrony may be even more beneficial (Martin and Bernfield, 1980; Tellez et al., 2015). Tellez et al. (2015) showed that some of the best sprint kayak crews exhibit a slight asynchrony in paddling (with the rear paddler starting the stroke earlier and finishing it later than the front paddler). According to Martin and Bernfield (1980), this asynchrony could reduce power loss caused by fluctuations in kayak speed in the forward direction. However, studies supporting this theory have mainly been conducted on rowing ergometers (Brouwer et al., 2013) and through 2D video analysis (Tay and Kong, 2018). The latter research revealed that crews in K2 tend to be more synchronized during the catch than the release phase of paddling. Furthermore, paddling synchronization patterns vary significantly, and sprint kayakers have no universal synchronization profile. It is complex to study the coordination between crew members and their interactions with the boat and surroundings. Another crucial aspect of performance in K2 is the athletes’ positioning. Although there is a position that is generally considered optimal, current literature offers divergent opinions on what it should be. For example, Ong et al. (2005) and Tellez et al. (2015) argued that taller and heavier athletes should sit at the rear, as they are more effective in generating paddling force. In contrast, through a computational fluid kinetics analysis, Campbell Ritchie and Selamat (2010) found that the load experienced by the front athlete is greater than the rear and suggested locating the stronger athlete in front. According to Tay and Kong (2020), the common practice is to place the powerful paddler in the back (rear), showing that four out of eight assessed crews achieved better times by reversing the seating order from that preferred. Kong et al. (2020a) also found no differences in strength, power, or time variables between front and rear paddlers, even when athletes switched positions. The literature has not reported any useful indication for crew composition. Based on these considerations, the present study aims to investigate the paddling timing in K2 crews, analyzing the advances or delays between front and rear kayakers during the paddle entry and exit phases (Romagnoli et al., 2022a) and the best location of the athletes in the kayak, measuring kinematic and kinetic parameters through the use of the “E-kayak” system (Bonaiuto et al., 2020; Bonaiuto et al., 2022).

Materials and methods

Subject

A total of 10 male sub-elite kayakers aged 15.2 ± 0.42, height 171.6 ± 3.92 (cm), mass 61.45 ± 4.79 (kg), and a kayaking experience of 4.80 ± 1.23 (years) participated in the study (Table 1). They were national-level athletes from the Canottieri Eur club who trained nine times a week during the test periods. The study was approved by the University of Rome Tor Vergata Institutional Review Board. Testing procedures were fully explained to participants’ families before obtaining individual written informed consent. All procedures followed the Declaration of Helsinki.

Table 1

Table 1. Anthropometric characteristics of each paddler.

Test procedure

The test trials were all conducted early in the morning (between 7 a.m. and 9 a.m.) when the lake conditions were flat, not windy, and suitable for this study (weather temperature at approximately 22 °C and water temperature at approximately 18 °C). The athletes were asked to race with their optimal performance in two 500 m trials following the coach’s directions through the race records and then in inverted positions.

Before each test, the paddlers performed a 15-min warm-up on land (focusing on shoulder and pelvic joint mobility exercises) and a more intensive warm-up in a K2 craft for up to 20 min. On the first day of the test, three crews performed the simulated race in their preferred positions and two in inverted positions, while on the second test day (after one recovery day), all crews switched their positions. Each trial was monitored through the E-kayak system, which consisted of an IMU (TDK 20948-100 Hz) and a GPS (ublox NEO-M9N-25 Hz) placed behind the rear paddler seat; two paddles instrumented with a strain gauge (100 Hz) were used (Bonaiuto et al., 2022).

Variable extraction

The following kinematic and kinetic parameters were measured for each crew in the preferred and inverted mode: the air or aerial phase (from the paddle blade exit to the entry on the other side), the wet phase (from water entry to exit for each paddle blade) (McDonnell et al., 2012; Gomes et al., 2015; Bonaiuto et al., 2020), the average force for front and rear paddlers, the total force expressed from each crew, the stroke frequency, and the total time over 500 m. The definition of the entry and exit phases of the paddle corresponded to the start and end of force application in water (Figure 1). Figure 1 shows part of the force–time plot of a crew with the respective advances or delays between the front and rear kayakers during the entry and exit phases. All parameters considered were extrapolated and analyzed from the data provided by E-kayak (Romagnoli et al., 2024).

Figure 1

Figure 1. Force–time curves of a K2 crew showing the air and wet time (T_air–T_wet) for each side and highlighting the time difference during the paddle entry and exit phases between front and rear kayakers.

Statistical analysis

The results are presented as the mean (M) and standard deviation (±SD) unless otherwise specified. The Kolmogorov–Smirnov test was used to validate the assumption of normality. To verify the correlation between time over 500 m and each crew’s kinematic and kinetic data, the Pearson product–moment correlation coefficient (r) and 95% confidence interval (95% CI) for r were used. Statistical significance was accepted at p < 0.05. A multiple regression model was used to quantify the relationship $Y=\left(K_0+K_1{X}_{1+}{K}_2{X}_{2+}{K}_3{X}_3+K_n{X}_n\right)$ between the dependent variable (Y=Time on 500 m) and a set of explanatory variables (stroke frequency (X₁), force of front paddler (X₂), and force of rear paddler (X₃)), along with respective variance of the faster crew at 500 m. A weight dummy variable was assumed (AutoWeight 1/SD²) for an automatic weighted regression procedure to correct for heteroscedasticity (Neter et al., 1996). In addition, the coefficient of variation percentage (CV%), the effect size (ES), where a small effect was 0.1, a moderate effect was 0.3, and a large effect was 0.5 (Cooper and Hedges, 1993), and the relative 95% CI were calculated for the time measured at 500 m and the time estimated through the multiple regression equation. Furthermore, the interclass correlation (ICC) was used to assess reliability between the time measured and time estimated from the multiple regression equation. The statistics and data visualization were performed using MedCalc^® (version 23.0.1.).

Results

If the mean value is positive, the rear always anticipates the front paddler during the entry and exit phases. If the mean value is negative, the rear paddler postpones the entry and exit relative to the front paddler. These differences in both phases highlight the different paddling techniques adopted by each crew (Table 2).

Table 2

Table 2. Average differences and relative standard deviation of the blade entry and exit phases in the water between front and rear paddlers.

Table 3 shows the correlations and relative 95% CI between time over 500 m, the average total force developed by front and rear paddlers, front and rear paddlers’ strength, and stroke frequency. The total analysis of the crews in both the preferred and inverted positions shows a significant correlation between the time over 500 m and the average total force developed by the crew, the strength of the front kayaker, and the stroke frequency of K2. These results suggest that the front paddler plays a fundamental role in leading the crew by imposing the stroke frequency. No statistically significant correlations were found for all other parameters considered in the study (p > 0.05).

Table 3

Table 3. Time over 500 m correlated with the force expressed by front and rear kayakers, total force of crew (front + rear), stroke frequency, and 95% CI.

Table 4 shows the correlations between stroke frequency, front and rear paddlers’ strength, and time over 500 m. The only statistically significant correlations were between front kayaker strength and time over 500 m.

Table 4

Table 4. Stroke frequency correlated with force expressed by front and rear paddlers and time over 500 m.

The results from multiple regression are shown in Table 5. It indicates that the analysis of the variance table separates the total variation in the dependent variable into two parts: one attributed to the regression model (labeled “Regression”) and another that cannot be explained by the model (labeled “Residual”). If the p-value for the F-test is small (less than 0.05), as in the results obtained (F-ratio = 224.84 and p-value = 0.049), the hypothesis of no (linear) relationship can be rejected, and the multiple correlation coefficient is considered statistically significant. The equation takes account of the front and rear force of paddlers and the K2 stroke frequency; these parameters seem to determine the time over 500 m for the fast crews.

Table 5

Table 5. Summary of multiple regression equations and analysis of variance. The weighted least squares multiple regression section reports the coefficient of determination R², R² adjusted, multiple correlation coefficient (MCC), and residual standard deviation (RSD). The regression equation section shows independent variables and relative coefficient (K), standard error (Std. Er), 95% CI of K, t-value (t), P-value (P), and partial correlation (r_partial). Analysis of variance section shows F-ratio and P-value (P).

Figure 2 shows the mean data of air phase duration between the front and rear paddlers for each crew in the preferred reversed position, while Figure 3 shows the average data of the water phase for each component of the crew. Figure 4 reports the mean duration of paddle strokes (T_air + T_wet), considering the air and water phases of each athlete (for each stroke) in different seats during their performance.

Figure 2

Figure 2. Mean value ± SD of the air phase of the front (black) and rear (white) paddlers of each crew during the 500 m test.

Figure 3

Figure 3. Mean value ± SD of the wet or water phase of front (black) and rear (white) paddlers during the 500 m test.

Figure 4

Figure 4. Average values ± SD of stroke duration between front and rear paddlers shown in preferred and inverted positions during the 500 m test.

Figure 5 reports the data related to force expressed during the water phase of the front and rear paddlers in preferred and reversed seats. The total force (front + rear) provides a better understanding of the total mean force applied during the 500 m.

Figure 5

Figure 5. Mean value ± SD of force related to front (black) and rear paddler (white) and total force of the crew (gray).

Figure 6 reports the stroke frequency (black) and time performed on 500 m (white) in preferred and inverted seats for each crew. The figure shows that the crew with a high stroke frequency obtains the best performance.

Figure 6

Figure 6. Black-and-white histogram representing, respectively, the stroke frequency (s/min) and total time (s) performed over 500 m in the preferred and inverted seating positions.

These results (Figures 2–6) show the different strategies for each crew in preferred and inverted positions to lead the 500 m at maximum velocity, adapting the kinematic and kinetic parameters and the technical gesture.

Figure 7 reports the correlation between time measured over 500 m and the time estimated by the multiple regression model for the five fastest crews (A1–A2; B1–B2; C1–C2; D2–D1; and E2–E1). The results show a high correlation between the two (r = 0.99), with CV% = 0.22, ES = −0.005, and ICC = 0.99. This further confirms Table 5, which shows that the parameters related to front and rear paddlers’ strength and relative stroke frequency are critical for crews in K2.

Figure 7

Figure 7. Correlation between the measured and estimated times (from the multiple regression equation) over 500 m for the fastest crews only (A1–A2; B1–B2; C1–C2; D2–D1; and E2–E1).

Discussion

Chronometric analysis of the 500 m in K2 performed in the preferred and inverted orders shows that three of the crews performed the fastest run in the preferred position and two in the inverted position (Table 2). The data agree with the findings of Tay and Kong (2020) that approximately 50% of analyzed crews achieved better times by reversing the seating order from the preferred order. Moreover, they pointed out that the seating order in K2 minimally affects the synchronization of strokes and is not always decisive for maximum performance. Our tests show that the key to better performance is the paddling timing during the entry and exit phases of the water of the two athletes, also considering the levels of force and stroke frequency they express.

From the analysis in Table 2, the crew with the best performance over 500 m is in the preferred position A1–A2. During the entry and exit phases, the rear paddler always anticipated the entry and exit of the blade with an average timing difference between 33 and 40 ms. This temporal asynchrony confirms the data reported by Gomes (2015) and Kong et al. (2020a), who reported an anticipation of 34 ms during the entry phase. This anticipation explains that during the phase of blade entry into the water, the rear paddler must react quickly to generate high force values on the blade to compensate for the higher speed of the fast-moving water near the boat, and an early exit minimizes the drag (i.e., the deceleration of the K2 that starts before the end of the exit phase). The analysis showed an average timing offset of 38.30 ms and 41.70 ms in the early and exit phases, respectively. In contrast, for the other crews investigated in both the preferred and inverted positions, the kinematic data fall outside the ranges identified, with a minimum of −17 ms and a maximum of 64 ms in the entry phase (Gomes, 2015; Kong et al., 2020a). In the exit phase, the time ranged from a minimum of −27 ms to a maximum of 91 ms. This variation may have influenced the crew’s propulsive action. For example, in the B1–B2 crew (preferred position), an average anticipation of 17 ms in the entry phase and a delay of −23 ms in the exit phase by the rear paddler were observed. This difference in timing during water force application, where the rear paddler starts the stroke earlier and finishes later, could help reduce the power loss of the K2 during the advancement (Martin and Bernfield, 1980; Tellez et al., 2015). In addition to the timing of force application in the water, other kinematic and kinetic factors also appear to influence the performance of the K2 500 m. Figures 2–4 show the plots of the average durations of the air and wet phases and the stroke duration (T_air + T_wet) of the front and rear paddlers in both the preferred and inverted positions. The statistical analysis showed a non-statistically significant correlation (p > 0.05) between the stroke duration (T_air + T_wet) and 500 m performance, with a correlation of 0.62 for the front paddler and 0.60 for the rear paddler. Nevertheless, this result could be statistically significant in elite athletes, whose superior paddling techniques allow for a more efficient application of propulsive force. In elite paddlers, the air phase is not excessively long, minimizing deceleration due to drag. Another important aspect is the average and total forces exerted by the front and rear paddlers during 500 m in both the preferred and inverted positions. Figure 5 shows the average force values and their respective sums between the front and rear paddlers. The analysis shows that the time over 500 m is correlated with the sum of the force exerted by the crew, so the stronger crew (able to generate better propulsive power) is that which covers 500 m in less time (Table 3). This aspect is relevant because expressing a high level of power requires high levels of force and paddle velocity during the propulsive phase (Kristiansen et al., 2022; Romagnoli et al., 2022b). Another important aspect is the correlation found between the front paddler’s strength and time over 500 m (Table 3). This correlation, r = −0.88, seems to confirm the findings of Campbell Ritchie and Selamat (2010), where the maximum load experienced on the blade is found for the front athlete. This aspect could be explained because, in sub-elite crews, the more technical athlete is positioned in front and is consequently able to express a greater force than those with less technique. Furthermore, it is relevant that in all tests, the paddling frequency affected the total time. In particular, the analysis shows that the best test (between preferred and inverted) (Figure 6) is always that with a higher paddling frequency, as demonstrated by athletes in K1 (Caldognetto and Annino, 2010). Finally, a significant correlation is observed between front paddler strength and paddling frequency (Table 4). This could be further confirmation that for sub-elite athletes in K2, the front paddler must be the most technical. Consequently, they are able to develop high force gradients and thus dictate the optimal paddling frequency for the crew throughout the performance. Considering the impact that these features can have on K2 crews, a specific regression equation is investigated. From the results in Table 5, the analysis confirms the goodness-of-fit of the model, with R² = 0.99 (indicating the proportion of the variation in the dependent variable explained by the regression model) and MCC = 0.99. Despite t and P values in the regression equation section being not significant, we can consider the independent data (stroke frequency, force front paddler, and force rear paddler) to be statistically significant to estimate the dependent data (time over 500 m) because the relative F-ratio and P-value of analysis of variance is less than 0.05 (Altman, 1990; Armitage et al., 2013). From the comparison between the time measured and estimated over 500 m (Figure 7), CV% is 0.22 (95% CI from 0.00 to 0.38), ICC is 0.99 (95% CI from 0.98 to 0.99), and ES is −0.005 (95% CI from −0.11 to 0.05). For this reason, the following multiple regression equation applies:

$Timeon500m=197.77+\left[\left(-0.217SF\right)+\left(-0.816F_f\right)+\left(0.301F_r\right)\right],$

where SF is the stroke frequency, F_f is the force of the front paddler, and F_r is the force of the rear paddler.

This formula could be used by trainers to investigate how these parameters can influence the time over 500 m for sub-elite paddlers, and it could also serve as a valid tool for selecting a better K2 crew.

Limitations

The following study has some limitations, including the sample size of the crews, the technical level of the kayakers (sub-elite), the absence of an instrumented footrest to measure lower limb force, and the lack of a reference model (elite crew). With elite paddlers, parameters such as the water and air phases could play a fundamental role in K2 crew performance. For these reasons, future research is needed to understand whether other variables should be taken into consideration as dependent variables in multiple regression equations to explain K2 crew performance.

Conclusion

This study was conducted on sub-elite K2 athletes, and no significant differences were observed in 500 m race outcomes between the five crews in the preferred/inverted positions, with three performing best in the preferred session and two in the inverted position. However, a minimal difference of a few tenths confirms that there is no predominant position, as also observed by Kong et al. (2020a). The key findings emerging from the analysis suggest that the front paddler plays a crucial role in the crew’s performance as they must be able to produce a high level of force in the water while simultaneously maintaining an optimal paddling frequency for performance purposes. Furthermore, the rear paddler must be able to anticipate the front paddler during the entry and exit phase by approximately 30–40 ms to efficiently contribute to the propulsion of the boat, as previously observed by Gomes (2015), Kong et al. (2020a). Finally, the individualized multiple regression equations developed in this study can serve as valuable tools for assessing and monitoring different kinematic and kinetic parameters of sub-elite K2 crew performance. E-kayak software makes it possible to investigate kinetic parameters among the paddler’s crews to individualize some K2 performance limiting factors. Consequently, future studies could investigate elite crews to determine a performance model for K2 racing. Finally, the future integration of E-kayak with emerging artificial intelligence technologies could provide valuable tools for further investigating these concepts, including paddlers’ pose estimation and object detection (Edriss et al., 2024b; Edriss et al., 2024a; Najlaoui et al., 2024).

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by the University of Rome’s Tor Vergata Institutional Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.

Author contributions

CR: conceptualization, data curation, formal analysis, investigation, writing–original draft, and writing–review and editing. SE: supervision and writing–review and editing. LC: visualization and writing–review and editing. LG: data curation and writing–original draft. IC: supervision and writing–review and editing. AA: methodology and writing–review and editing. NL: software and writing–original draft. PB: software and writing–original draft. EP: supervision and writing–original draft. VB: data curation, investigation, software, and writing–review and editing. GA: writing–review and editing and writing–original draft.

Funding

The authors declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

The authors wish to thank Claudio Ghelardini and Lucia Lissoni of the Circolo Canottieri EUR for their assistance during the study.

Conflict of interest

Authors NL and PB were employed by APLAB.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Altman D. G. (1990). Practical statistics for medical research. New York: Chapman and Hall/CRC. doi:10.1201/9780429258589

CrossRef Full Text | Google Scholar

Armitage P., Berry G., Matthews J. N. S. (2013). Statistical methods in medical research. John Wiley and Sons.

Google Scholar

Bonaiuto V., Annino G., Boatto P., Lanotte N., Caprioli L., Padua E., et al. (2022). “System for performance assessment of K2 crews in flatwater sprint kayak,” in 2022 IEEE international workshop on sport, technology and research (STAR), Trento - Cavalese, Italy, 06-08 July 2022 (IEEE), 56–60. doi:10.1109/STAR53492.2022.9859584

CrossRef Full Text | Google Scholar

Bonaiuto V., Gatta G., Romagnoli C., Boatto P., Lanotte N., Annino G. (2020). A pilot study on the e-kayak system: a wireless DAQ suited for performance analysis in flatwater sprint kayaks. Sensors Switz. 20, 542. doi:10.3390/s20020542

PubMed Abstract | CrossRef Full Text | Google Scholar

Brouwer A. J. de, Poel H. J. de, Hofmijster M. J. (2013). Don’t rock the boat: how antiphase crew coordination affects rowing. PLOS ONE 8, e54996. doi:10.1371/journal.pone.0054996

PubMed Abstract | CrossRef Full Text | Google Scholar

Caldognetto E., Annino G. (2010). 200 metri acqua piatta: esperienze e proposte nel kayak maschile. Nuova Canoe Ric. 72, 23–38.

Google Scholar

Campbell Ritchie A., Selamat M. F. B. (2010). Comparison of blade designs in paddle sports. Sports Technol. 3, 141–149. doi:10.1080/19346182.2010.538400

CrossRef Full Text | Google Scholar

Cooper H., Hedges L. V. (1993). The Handbook of Research Synthesis. Russell Sage Foundation.

Google Scholar

Cuijpers L. S., Zaal F. T. J. M., Poel H. J. de (2015). Rowing crew coordination dynamics at increasing stroke rates. PLOS ONE 10, e0133527. doi:10.1371/journal.pone.0133527

PubMed Abstract | CrossRef Full Text | Google Scholar

Edriss S., Caprioli L., Campoli F., Manzi V., Padua E., Bonaiuto V., et al. (2024a). Advancing artistic swimming officiating and performance assessment: a computer vision study using MediaPipe. Int. J. Comput. Sci. Sport 23, 35–47. doi:10.2478/ijcss-2024-0010

CrossRef Full Text | Google Scholar

Edriss S., Romagnoli C., Caprioli L., Zanela A., Panichi E., Campoli F., et al. (2024b). The role of emergent technologies in the dynamic and kinematic assessment of human movement in sport and clinical applications. Appl. Sci. Switz. 14, 1012. doi:10.3390/app14031012

CrossRef Full Text | Google Scholar

Gomes B. B. (2015). Biomechanical determinants of kayak paddling performance in single-seat and crew boats. Portugal: Universidade do Porto.

Google Scholar

Gomes B. B., Ramos N. V., Conceição F. A., Sanders R. H., Vaz M. A., Vilas-Boas J. P. (2015). Paddling force profiles at different stroke rates in elite sprint kayaking. J. Appl. Biomechanics 31, 258–263. doi:10.1123/jab.2014-0114

CrossRef Full Text | Google Scholar

King A., de Rond M. (2011). Boat race: rhythm and the possibility of collective performance. Br. J. Sociol. 62, 565–585. doi:10.1111/j.1468-4446.2011.01381.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Kong P. W., Tay C. S., Pan J. W. (2020a). Application of instrumented paddles in measuring on-water kinetics of front and back paddlers in K2 sprint kayaking crews of various ability levels. Sensors 20, 6317. doi:10.3390/s20216317

PubMed Abstract | CrossRef Full Text | Google Scholar

Kong P. W., Tay C. S., Pan J. W. (2020b). The role of vision in maintaining stroke synchronization in K2 crew-boat kayaking. Front. Sports Act. Living 2, 569130. doi:10.3389/fspor.2020.569130

PubMed Abstract | CrossRef Full Text | Google Scholar

Kristiansen M., Pedersen A.-M. S. K., Sandvej G., Jørgensen P., Jakobsen J. V., de Zee M., et al. (2022). Enhanced maximal upper-body strength increases performance in sprint kayaking. J. Strength and Cond. Res. 37, e305–e312. doi:10.1519/JSC.0000000000004347

CrossRef Full Text | Google Scholar

Martin T. P., Bernfield J. S. (1980). Effect of stroke rate on velocity of a rowing shell. Med. Sci. Sports Exerc 12, 250–256. doi:10.1249/00005768-198024000-00004

PubMed Abstract | CrossRef Full Text | Google Scholar

McDonnell L. K., Hume P. A., Nolte V. (2012). An observational model for biomechanical assessment of sprint kayaking technique. Sports Biomech. 11, 507–523. doi:10.1080/14763141.2012.724701

PubMed Abstract | CrossRef Full Text | Google Scholar

Najlaoui A., Campoli F., Caprioli L., Edriss S., Frontuto C., Romagnoli C., et al. (2024). “AI-driven paddle motion detection,” in 2024 IEEE international workshop on sport, technology and research (STAR), 290–295. doi:10.1109/STAR62027.2024.10635932

CrossRef Full Text | Google Scholar

Neter J., Kutner M. H., Nachtsheim C. J., Wasserman W.others (1996). Applied linear statistical models.

Google Scholar

Ong K. B., Ackland T. R., Hume P. A., Ridge B., Broad E., Kerr D. A. (2005). Kayak: Equipment set-up among Olympic sprint and slalom Kayak paddlers. Sports Biomechanics 4, 47–58. doi:10.1080/14763140508522851

PubMed Abstract | CrossRef Full Text | Google Scholar

Robinson M. G., Holt L. E., Pelham T. W., Furneaux K. (2011). Accelerometry measurements of sprint kayaks: the coaches’ new tool. Int. J. Coach. Sci. 5.

Google Scholar

Romagnoli C., Boatto P., Campoli F., Caprioli L., Delgado D., Edriss S., et al. (2024). “Monitoring of kinetic parameters in sprint canoeing performance,” in Engineering methodologies for medicine and Sports. Editors R. Montanari, M. Richetta, M. Febbi, and E. M. Staderini (Cham: Springer Nature Switzerland), 710–724. doi:10.1007/978-3-031-63755-1_52

CrossRef Full Text | Google Scholar

Romagnoli C., Ditroilo M., Bonaiuto V., Annino G., Gatta G. (2022a). Paddle propulsive force and power balance: a new approach to performance assessment in flatwater kayaking. Sports Biomech., 1–14. doi:10.1080/14763141.2022.2109505

CrossRef Full Text | Google Scholar

Romagnoli C., Gatta G., Lamouchideli N., Bianco A., Loddo S., Alashram A. R., et al. (2022b). Specificity of weightlifting bench exercises in kayaking sprint performance: a perspective for neuromuscular training. Front. Physiology 13, 898468. doi:10.3389/fphys.2022.898468

CrossRef Full Text | Google Scholar

Tay C. S., Kong P. W. (2018). A video-based method to quantify stroke synchronisation in crew boat sprint kayaking. J. Hum. Kinet. 65, 45–56. doi:10.2478/hukin-2018-0038

PubMed Abstract | CrossRef Full Text | Google Scholar

Tay C. S., Kong P. W. (2020). Stroke characteristics in Sprint Kayaking— how does seat order influence synchronization in a K2 crew boat? J. Mech. Med. Biol. 20, 2050016. doi:10.1142/S0219519420500165

CrossRef Full Text | Google Scholar

Tellez J., Hernandez J., Imaz X. (2015). Tactics, techniques and team selection for K4 boats. Training Sprint Canoeing, Real Fed. Espanola Pirguismo, 227–251.

Google Scholar

Wing A. M., Woodburn C. (1995). The coordination and consistency of rowers in a racing eight. J. Sports Sci. 13, 187–197. doi:10.1080/02640419508732227

PubMed Abstract | CrossRef Full Text | Google Scholar