## 4 Discussion
Laser Doppler flowmetry (LDF) can measure blood flow in a relatively small area, but there are limitations to the measurement technology, and there are also limitations to trying to measure blood flow in a limited area of small fish (e.g., gills and certain fin blood vessels). There are individual differences in the ability to capture the heart rate rhythm and errors in time measurement, such as counting seconds, and discrepancies in the timing of starting and stopping a stopwatch that can also cause errors. Physiological fluctuations are considered as factors on both sides. The heart rate varies greatly throughout the day and also changes depending on meals. The feeding time for the fish used in the experiment was generally 10 a.m., but sometimes it was delayed. Any one of these factors, or a combination of multiple factors, may cause abnormal values in the estimated heart rate and manual measurements. Despite its advantages, the proposed method has several limitations. First, the system relies on the subject remaining stationary within the ROI. Any significant movement of the larva, such as swimming or body rotation, can shift the heart outside the fixed ROI, leading to inaccurate measurements or signal loss. While this was minimized in our current study by selecting periods of immobility, a robust implementation for live monitoring would require an automatic tracking system capable of dynamically adjusting the ROI. (Because larvae move in three dimensions, changes in depth, angle, and position can affect measurements. In this study, we only used a 60 s clip in which fish movement was minimal.) Second, the threshold value for binary segmentation was determined heuristically. While effective in this experiment, this parameter may need tuning under different lighting conditions, fish orientations, or developmental stages. Future studies should investigate adaptive thresholding techniques or incorporate machine learning algorithms to improve generalizability. Third, the frame rate (interlaced 60i) of the recording may introduce temporal artifacts that could influence the accuracy of motion estimation. Although we achieved meaningful results with this setup, higher frame rate recordings may enhance temporal resolution and allow for more precise heartbeat detection, especially in faster-beating or smaller hearts. Further testing samples should be increased. Additionally, validation against more precise physiological measurement systems--such as electrocardiography (ECG) or laser Doppler flowmetry--was not conducted in this study. While the handheld counter served as a practical reference, it does not provide temporal precision at the millisecond level. Incorporating such gold-standard measurements in future work would offer a more rigorous evaluation of our algorithm's accuracy. This study is merely a pilot experiment. Since measurements were conducted on only four fish, the statistical power is sufficient for performing counterbalanced within-subject comparisons but not for conducting an independent two-sample t-test between different individuals. For example, when examining whether there is a difference in the heart rate of a single fish between resting and active states, the required sample size for a paired t-test is approximately three, assuming an effect size of d = 2.0, a significance level of \(\alpha\) = 0.05 (two-tailed), and a statistical power of 0.80. However, for an independent two-sample t-test comparing different fish, approximately five fish per group would be required. In such cases, it is necessary to balance the groups by randomly assigning fish to each group. The proposed method in this study is intended solely for preliminary data collection. As future perspectives, automating the ROI selection using object tracking techniques, such as centroid-based tracking (YOLO, You Only Look Once) [27; 28; 29; 30; 31; 32], would increase robustness and applicability for longer recording durations or more active larvae.