Knockdown-time assays, which are often used to assess physiological injury from stress exposure, typically rely on human observation to determine the end of biological activity. Increased throughput and reliability of these classic assays is needed to improve large-scale phenotypic screens of stress tolerance for species comparisons and genomic association studies, but the impact of observer bias on measurement uncertainty and treatment effect estimates has not been systematically tested. Here, we develop a novel computer algorithm to automatically analyze video files from time-to-knockdown assays, and we compare this method against human-derived estimates. Adult Drosophila melanogaster were held at constant high temperatures and video-recorded until movement ceased, and video-frames were converted to a pixel change rate. We then developed custom Python scripts to test several algorithms for estimating knockdown time and matching the human interpretation. The computational methods had reduced standard deviations relative to human-derived measurements, indicating our new method improves precision. Rank order and significant differences between experimental groups were generally consistent among methods, but computer-generated estimates of knockdown time tended to be shorter. Taken together, these results indicate that computer-assisted video analysis of time-to-knockdown assays can reduce measurement error and increase throughput, which can be beneficial for applications such as genetic association studies or niche-modelling.