Real-time
Measures and detects fatigue and related problems in real-time and in a non-invasive way.
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.
Measures and detects fatigue and related problems in real-time and in a non-invasive way.
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.
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.
Learn more about our 3 different plans and choose a plan that is right for your organization.
We want to create the best software, not for you to use but to serve you.
How we do it
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.
Our software detects mental fatigue through analysis of the user’s computer interactions via the keyboard and mouse.
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.
After the learning phase, the system then classifies the user’s mental fatigue state into seven different levels (USAFSAM Mental Fatigue Scale).
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 NowPimenta, 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.
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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.