This study uses advanced technologies such as Neural Trainer and smartwatch to analyze the psychophysical performance of Systems Engineering university students from Arequipa, Peru. The main objective was to evaluate the relationship between reaction times and accuracy in motor tasks, considering key metrics: time to complete 10 repetitions with both hands (P1), left hand (P2) and right hand (P3); number of correct responses in 15 seconds with both hands (P4), left hand (P5) and right hand (P6); and performance in agility tasks in two-color sequences (PA).

A sample of 25 students was used, and the data was processed using descriptive statistics, correlation analysis, and hypothesis testing. The results show significant correlations between different metrics, such as total execution time (P1_time) and accuracy (P4_hits) (r=0.81, p<0.001), suggesting that longer times may be associated with a higher number of hits, indicating a more meticulous approach to task execution. Likewise, a significant relationship was found between P1_time and P3_time (r=0.64, p=0.001), highlighting the consistency between the dominant hand and both hands in reaction tasks.

Graphical analysis through heat maps and scatter plots allowed for identifying essential trends. The strong relationships between time and hit variables reinforce the hypothesis that speed and accuracy are intrinsically connected, which has implications for the design of educational programs and physical and cognitive training strategies.
These results are relevant for assessing psychomotor skills, highlighting the potential of using interactive and wearable technologies in the educational and sports fields.

This approach offers an effective tool to monitor and improve physical and cognitive performance, allowing for the design of personalized interventions. Furthermore, the study opens opportunities for future research exploring similar applications in broader populations or different contexts, such as physical rehabilitation or developing specialized training programs.

In conclusion, the combined use of advanced technologies and sound statistical methods allows us to understand better the dynamics between speed and precision in motor tasks. It contributes to developing practical tools for analyzing human performance. This work highlights the importance of integrating technology and quantitative analysis in studying psychomotor abilities.

Keywords: Neural Trainer, Wearables, University education, Psychomotor assessment, Educational technology, Physical-cognitive performance.