# Learning Swimming Techniques by Means of Real-Time Monitoring with Embedded Devices
## 4 Discussion  
By using the devices and application created, this system performs the following actions: it stores a movement pattern, continuously reads data, verifies when a movement is made, and computes an algorithm (DTW or PCC) for pattern recognition between two series of data (trained vs. newly read movement). It then provides live feedback data using wireless technology. The standstill feature helps optimize battery consumption by reducing CPU load during periods of no movement. This study presents a dryland system designed for use in swimming practice prior to entering the pool. Such training can be integrated into a warm-up session or an entire training session, enabling the calibration of a swimmer's technique outside the pool. The long-term goal of this approach is to help improve swimming technique over time. However, this study does not aim to demonstrate the effectiveness of this system in achieving that goal, as it involves only two subjects. What is demonstrated in this study is the practical application of the proposed hardware devices and the software implementation required to create this simulator, supported by evidence from the recorded data and the application of algorithms, as shown in the presented graphs. Figure 12 shows that the blue subject needs to improve shoulder movement (series reading from 21 to 30--Device_3), as the palm (series readings from 1 to 10--Device_1) and elbow (series readings from 11 to 20--Device_2) are closer to the 90-100% range. This is because the results are constantly below 80% only for the shoulder movement. The limitations of the proposed system include the requirement for continuous movement without start-stop routines. The system works well for \"drill-like swimming of crawl\" exercises but may not perform adequately with fast, continuous swimming. If a full arm rotation occurs in less than one second and is executed continuously, the system may fail to function properly, as the acceleration values would approach the normalized maximum and minimum limits, rendering the output verification inaccurate. The 'drill-like swimming crawl' is a type of swimming exercise in which the swimmer performs strokes with one arm at a time while the other arm remains stretched out in front of the head. An alternative approach is to execute the arm rotations at approximately 1.5 to 2 s per stroke, simulating a slow swimming motion. If the movements are moderate and the system remains on the same person who trained it, both algorithms (DTW and PCC) provide a reliable solution. It can be noted that PCC penalizes lighter movements more than DTW, making PCC a more tolerant algorithm in comparison to DTW. Future verification will be needed to assess the CPU load for each algorithm. For complex movements, continuous swimming, and transitioning the system to another person (not the one who trained it), Dynamic Time Warping is the preferred solution due to its flexibility in handling time shifts. This algorithm is suitable for scenarios involving different individuals, particularly when the arm length differs (Figures 12 and 13). Future improvements include storing training values in non-volatile memory, which will extend the system's usability by reducing the need for frequent retraining. Additionally, integrating gyroscope data from the MPU6050 sensor will enable the system to capture rotational movement information. Another enhancement involves comparing the CPU usage of the microcontroller based on the applied algorithms to optimize performance. Battery consumption monitoring can also be implemented by using an ADC to measure battery levels in real time. Lastly, making the system waterproof will allow for testing in a swimming pool, ensuring its functionality in real-world aquatic environments. **Author Contributions:** Conceptualization, V.-A.D.; methodology, S.F. and I.-M.D. ; software, V.-A.D.; validation, V.-A.D., I.-M.D. and S.F. ; formal analysis, I.-M.D. and S.F. ; investigation, V.-A.D.; resources, V.-A.D.; data curation, S.F.