Propulsive force is a determinant of swimming performance. Several methods have been proposed to estimate the propulsive force in human swimming; however, their practical use in coaching is limited. Herein, we propose a novel method for estimating the propulsive force generated by swimmers’ hands using an inertial measurement unit (IMU) and pressure sensors. In Experiment 1, we use a hand model to examine the effect of a hand-mounted IMU on pressure around the hand model at several flow velocities and water flow directions. In Experiment 2, we compare the propulsive force estimated using the IMU and pressure sensors ($F_{\mathrm{IMU}}$) via an underwater motion-capture system and pressure sensors ($F_{\mathrm{Mocap}}$). Five swimmers had markers, pressure sensors, and IMUs attached to their hands and performed front crawl swimming for 25 m twice at each of nine different swimming speeds. The results show that the hand-mounted IMU affects the resultant force; however, the effect of the hand-mounted IMU varies with the flow direction. The mean values of $F_{\mathrm{Mocap}}$ and $F_{\mathrm{IMU}}$ are similar (19.59 ± 7.66 N and 19.36 ± 7.86 N, respectively; intraclass correlation coefficient_(2,1) = 0.966), and their waveforms are similar (coefficient of multiple correlation = 0.99). These results indicate that the IMU can estimate the same level of propulsive force as an underwater motion-capture system.

This study aimed to investigate the essential role of the kicking action in front crawl. To achieve this objective, we examined the relationships of the hand propulsive force and trunk inclination with swimming velocity over a wide range of velocities from 0.75 m·s^-1 to maximum effort, including the experimental conditions of arm stroke without a pull buoy. Seven male swimmers performed a 25 m front crawl at various speeds under three swimming conditions: arm stroke with a pull buoy, arm stroke without a pull buoy (AWOB) and arm stroke with a six-beat kick (SWIM). Swimming velocity, hand propulsive force and trunk inclination were calculated using an underwater motion-capture system and pressure sensors. Most notably, AWOB consistently exhibited greater values than SWIM for hand propulsive force across the range of observed velocities (p < 0.05) and for trunk inclination below the severe velocity (p < 0.05), and these differences increased with decreasing velocity. These results indicate that 1) the kicking action in front crawl has a positive effect on reducing the pressure drag acting on the trunk, thereby allowing swimmers to achieve a given velocity with less hand propulsive force, and 2) this phenomenon is significant in low-velocity ranges.

We aimed to investigate whether a linear relationship exists between swimming velocity and vertical body position for each stroke phase in front crawl, and to determine whether there are differences in the velocity effect among the stroke phases. Eleven male swimmers performed a 15 m front crawl at various swimming velocities. The whole-body centre of mass (CoM) was estimated from individual digital human models using inverse kinematics. The horizontal CoM velocity and vertical CoM position from the water surface were calculated for one stroke cycle and divided into five stroke phases: entry, pull, push, release, and recovery. Linear mixed-effects model analysis revealed a positive trend between the mean swimming velocity and the mean vertical CoM position for each stroke phase (p < 0.001 for all phases). The interaction term between stroke phase and swimming velocity was significant (p < 0.001), and the slopes of the propulsive phases (pull and push) were larger than those of the non-propulsive phases (entry, release, and recovery) (p < 0.001). These findings provide practical implications that vertical body position can be evaluated independently of the stroke phase while considering velocity effects, and that focusing on propulsive phases allows easier detection of vertical body position changes.

Purpose: This study aimed to provide evidence for the interpretation of the projected frontal area (PFA) during front crawl. To achieve this goal, we developed a method for calculating the PFA of each body segment using digital human technology and compared the pressure drag under two calculation conditions: a combination of the PFA with and without accounting for the horizontal velocity of each body segment. Methods: Twelve competitive male swimmers performed a 15-m front crawl at 1.20 m·s⁻¹. The three-dimensional positions of the reflective markers attached to the swimmer’s body were recorded using an underwater motion-capture system. Based on the body shape of each swimmer obtained from the photogenic body scanner, individual digital human body models were created with the color of the model’s vertices divided into eight body segments. The time series of the volumetric swimming motion was reconstructed using inverse kinematics. The PFA of each body segment was then calculated by the automatic processing of a series of parallel frontal images. The pressure drag index, defined as the value excluding the drag coefficient while simultaneously considering the PFA and the horizontal velocity, was calculated under two conditions: the static condition (accounting for only the PFA of each body segment) and the dynamic condition (accounting for the PFA and horizontal velocity of each body segment). Results: Notably, the pressure drag index was higher under the static condition than under the dynamic condition for the humerus, ulna, and hand segments (P < 0.001). Conclusions: The results obtained using our methodology indicate that the PFA of the upper limb segments overestimates their contribution to pressure drag during front crawl under the static condition.

Purpose : The critical power (CP) model is widely used to evaluate high-intensity exercise tolerance; however, the performance characteristics reflected by its parameters may differ across athletes with diverse physiological and neuromuscular profiles. This study examined how CP-model parameters relate to aerobic power, indices of anaerobic performance, and nonoxidative energy contribution in male track-and-field athletes. Methods : Twenty male track-and-field athletes completed assessments of maximal oxygen uptake ( ), CP and the curvature constant (W′), maximal accumulated oxygen deficit, maximal anaerobic power, and a 30-second Wingate test. Body composition variables were assessed using high-frequency bioelectrical impedance analysis. Associations between CP-model parameters and performance indices were examined using correlation analysis. Results : CP was significantly and positively correlated with ( r = .64, P = .002) and negatively correlated with several mechanical indices of anaerobic performance. In contrast, W′ was positively associated with mechanically expressed anaerobic performance, including maximal anaerobic power and Wingate-derived peak and mean power ( r = .73–.75, P < .001), as well as body composition variables. No statistically significant association was observed between W′ and maximal accumulated oxygen deficit. Conclusions : These findings indicate that CP and W′ reflect distinct aerobic and mechanical performance characteristics, with CP primarily reflecting aerobic power and W′ representing a mechanically expressed work reserve rather than a purely metabolic index of nonoxidative energy contribution. This distinction highlights the complementary roles of CP-model parameters in performance evaluation.