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Success Stories by Application

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Illustration of a runner with a heart-rate waveform, representing sports performance tracking
Evalu@ in

Sports Applications

In this section we introduce applications that used Evalu@ as a data centralizer in the field of sports medicine, sports performance enhancement, and sports monitoring, among other applications we haven't envisaged yet.

Elite
players tracked
47
Biomarker
readings analyzed
1530
Highest fatigue
slope (mid/wide)
0.32
Scattered training data flowing into a configurable evaluation engine
The challenge
Lots of data, no flexible way to evaluate it.
CPK and Urea combined into a unique fatigue signature per player
Two biomarkers, one signature
47 professional soccer players, tracked across a full season.
Bar chart of fatigue accumulation slope by playing position
Fatigue by position
Midfielders and wide players accumulate fatigue fastest (0.32), goalkeepers slowest (0.10).
Results summary for the fatigue prediction study
The result
A personalized fatigue signature for every athlete, with no custom software built.
This application has a scientific publication
Evalu@ + Sports. Creatine Phosphokinase and Urea in High-Performance Athletes During Competition
Yepes Zuluaga, J.F., Gregory Tatis, A.D., Forero Arévalo, D.S., Yepes-Calderon, F. · ICAI 2021, Buenos Aires · Springer CCIS vol. 1455, pp. 290–302.
View the publication on SpringerLink
Timing
uncertainty
±3.8 ms
More precise than
a manual stopwatch
25×
Pendulum validation
deviation
6.8 ms
Ultrasonic sensors sending data to a local centralizer synced with Evalu@
From stopwatch to sensor
Biometric verification and real-time data, with no manual timing.
Accuracy comparison: manual stopwatch versus the IoT system
Validated accuracy
25 times more precise than a manual stopwatch.
Pendulum-based validation of IoT timing accuracy
Tested against physics
Benchmarked against a pendulum's known period — accurate to within 0.0068 seconds.
Results summary for the IoT field testing study
The result
Faster, more reproducible field tests, ready to scale across any club discipline.
This application has a scientific publication
Introducing an IoT-Based Strategy to Measure Athletes in Field Testing Automatically
Choco Carabali, D.A., Serrano Fontecha, J.S., Forero Arévalo, D.S., Barandica Lopez, A., Yepes-Calderon, F. · ICAI 2025 · Springer CCIS vol. 2667, pp. 131–143.
View the publication on SpringerLink
Illustration of a government building overlaid with a neural network, representing AI-assisted public decision-making
Evalu@ in

Government AI

In this section we introduce applications that used Evalu@ as a data centralizer in the field of public-sector decision-making, policy evaluation, and citizen services, among other applications we haven't envisaged yet.

This success story is coming soon — check back shortly.

Illustration of a podium leaderboard with a ranking list, representing personnel performance ranking
Evalu@ in

Personnel Ranking

In this section we introduce applications that used Evalu@ as a data centralizer in the field of personnel evaluation, performance ranking, and merit-based recognition, among other applications we haven't envisaged yet.

This success story is coming soon — check back shortly.

Calm illustration of a person at a desk with a gentle wellbeing pulse line, representing workplace mental health tracking
Evalu@ in

Mental Health Diagnosis in the Workplace

In this section we introduce applications that used Evalu@ as a data centralizer in the field of employee wellbeing, workplace mental health monitoring, and early support detection, among other applications we haven't envisaged yet.

This success story is coming soon — check back shortly.

Illustration of a connected team network around an efficiency gauge, representing institutional team performance evaluation
Evalu@ in

Institutional Team Efficiency & Valoration

In this section we introduce applications that used Evalu@ as a data centralizer in the field of institutional team performance, efficiency measurement, and collective valoration, among other applications we haven't envisaged yet.

This success story is coming soon — check back shortly.

Illustration of a tree with a declining emissions curve, representing carbon footprint tracking and reduction
Evalu@ in

Carbon Footprint

In this section we introduce applications that used Evalu@ as a data centralizer in the field of institutional carbon footprint tracking, emissions reduction, and sustainability reporting, among other applications we haven't envisaged yet.

This success story is coming soon — check back shortly.

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