# Real-Time Feedback in Swimming Exercises Using Embedded Devices in Water
## 3 Results  
The swimming style employed in this study is front crawl, performed with the assistance of a polyester kickboard. The use of the board enables isolation of upper or lower limb movements, facilitating a more controlled and sequential execution. This approach ensures consistent data acquisition, allowing for systematic analysis and accurate documentation of the motion patterns. This approach ensured that stroke execution followed a sequential pattern, similar to well-established technique drills commonly practiced by swimmers. In each repetition, the swimmer's hand returned to the same starting position--at the water'ssurface--allowing reliable wireless data transmission from the slave devices attached to the swimmer to the master device. This surface-level hand position is crucial for communication, as Wi-Fi transmission from submerged slave nodes is unreliable, and data sent while underwater is typically not received by the master device. However, this limitation does not affect the real-time feedback provided to the swimmer, since the vibration motor feedback is locally executed by the slave device immediately after computation. The wireless data transmission is currently intended for debugging and visualization purposes only. Future developments may focus on enhancing the system's communication protocols and hardware design to better support swimmers of varying skill levels and training intensities, particularly those requiring continuous underwater feedback. All testing of the system was performed on a single subject who was 30 years old and 193 cm tall, with an arm span of 203 cm. As defined by the _similarity percentage_ formula in Equation (1), Figure 7 presents a plot of the resulting _similarity percentage_ as a function of the total Dynamic Time Warping (DTW) distance. The system exhibits high sensitivity to the cumulative DTW values (_dtw_total_) obtained from all six axes. When the sum of the DTW values remains below 25, the calculated _similarity percentage_ stays within a high confidence range of 90% to 100%. However, once the _dtw_total_ exceeds 25, the system begins to penalize the similarity output more significantly. A steep decline in similarity is observed as _dtw_total_ increases from 30 to 70, with the _similarity percentage_ dropping from approximately 80% to as low as 15%. For _dtw_total_ values exceeding 80, the system effectively classifies the movement as dissimilar, assigning a near-zero similarity score. The similarity function was designed and tuned during dryland testing to offer high discrimination with minimal interference from environmental factors. However, this calibration presents challenges in aquatic environments, where variables such as water resistance, whole-body motion (influencing gyroscope data), and forward swimmer velocity can introduce noise and variability into the acceleration data. These factors may degrade the accuracy of similarity evaluation when the system is used in water. Figure 7: Applied formula for similarity percentage. Figures 8-10 present a series of recorded data samples in water alongside the application of the DTW algorithm and the resulting _similarity percentage_ computed using the defined formula. The areas highlighted with the read marker indicate deviations in the sensor readings when compared to the mean dataset obtained during the training phase. Although the orange line (representing current readings) does not fully overlap with the blue line (training reference) in several instances, the overall _similarity percentage_ remains promising. It is evident that, while the trajectories do not perfectly align, the deviation between blue-orange lines exceeds 1 (vertical axis) in only a few isolated cases, suggesting a generally consistent motion pattern. Figure 8: Data read from Node_1 (wrist) with a total of 27% similarity.