noscript facebook
Product details

Mental Fatigue Management Software

Track and detect mental fatigue in real-time using machine learning to analyze the user interaction patterns with the computer. It's a one-click installation with no extra hardware required.

Performetric’s Patent Pending System has been validated and correlated against accepted measures of fatigue and cognitive performance:

  1. Automated Neuropsychological Assessment Metric (ANAM) [1]
  2. United States Air Force School of Aerospace Medicine (USAFSAM) Mental Fatigue Scale [2]
  3. Electroencephalography (EEG) [3]



Measures and detects fatigue and related problems in real-time and in a non-invasive way.

Non-invasive & Non-intrusive

Runs in background and doesn't require data entry or frequent inputs from the user.


With its machine learning algorithm, Performetric’s short learning phase creates a unique profile for each user.

Data Analysis

Allows users to analyze their individual metrics and provides recommendations based on user’s profile; provides collective and aggregated data analyses for the company.


Alerts users at the onset of fatigue and burnout, providing recommendations that improve performance and mental health.


The user's privacy is 100% protected. Performetric does not collect private user data and privacy is not invaded.

Check out our 3 different plans

Learn more about our 3 different plans and choose a plan that is right for your organization.

How Performetric Works

We want to create the best software, not for you to use but to serve you.

How we do it

Performetric Process


Keyboard & Mouse

The keyboard and mouse are the sensors and only inputs needed for the desktop app. No external sensors are needed for the system to work.


Behavioral Analysis

Our software detects mental fatigue through analysis of the user’s computer interactions via the keyboard and mouse.


Machine Learning

Our system adapts to different kinds of users through machine learning algorithms. Each app is unique for each user and learning is ongoing with more interactions.


Mental Fatigue Classification

After the learning phase, the system then classifies the user’s mental fatigue state into seven different levels (USAFSAM Mental Fatigue Scale).


Create an account

Whether your aim is to be more productive, reduce stress, prevent accidents, improve performance, reduce errors, and/or increase employee engagement, let Performetric help you!

Sign up today and start reaping the benefits of an alert, efficient and engaged workforce.

Signup Now


Performetric resulted from 5 years of research and development

Pimenta, André, et al. "Monitoring mental fatigue through the analysis of keyboard and mouse interaction patterns." International Conference on Hybrid Artificial Intelligence Systems. Springer Berlin Heidelberg, 2013.

Pimenta, André, et al. "Detection of distraction and fatigue in groups through the analysis of interaction patterns with computers." Intelligent Distributed Computing VIII. Springer International Publishing, 2015. 29-39.

Pimenta, André, et al. "Analysis of human performance as a measure of mental fatigue." International Conference on Hybrid Artificial Intelligence Systems. Springer International Publishing, 2014.

Pimenta, André, et al. "A neural network to classify fatigue from human–computer interaction." Neurocomputing 172 (2016): 413-426.

Integration with

Slack Customization
Add to Slack
Slack Notification

1. Customize your notifications

Take control of you team's mental fatigue and get real-time notifications about the overall state of your workforce.

2. Integrate with your Slack channels

Get Performetric inside your Slack and your team to get notifications right away.

3. Get real-time notifications

Know when your users are getting extremely fatigued, entering in a state of burnout or team exhaustion.

Slack Bot Commands

/helpme lets you know more about Performetric and how our bot interacts with Slack.

/givefeedback lets you send us some words regarding feedback or any doubt you have about Performetric.

1. Miller, J. C. (2013). Cognitive Performance Research at Brooks Air Force Base, Texas, 1960-2009.

2. Reeves, D. L., Winter, K. P., Bleiberg, J., & Kane, R. L. (2007). ANAM genogram: historical perspectives, description, and current endeavors. Archives of Clinical Neuropsychology: The Official Journal of the National Academy of Neuropsychologists, 22 Suppl 1, S15–37.

3. Ongoing with preliminary correlation and validation.