"WISS provides proactive fatigue-risk prediction up to 5 hours before onset — the only solution offering predictive accuracy within the same work shift, without requiring wearables or cameras."
The WISS-APP enables voice analysis through an 8-15 second recording, which is transmitted to a cloud-based system. The system analyses the user's fatigue risk level and sends the results back to their device and email. Depending on the configured settings, email alerts and push notifications can also be sent to supervisors. Using WISS-APP three times per day allows the system to build and maintain a personalized data model for each user, enhancing its effectiveness in predicting fatigue risk. Additionally, the reporting dashboard provides real-time insights into each user's trends for immediate evaluation.
WISS Technology, LLC "WISS Technology.ai is the result of a joint venture between two industry leaders: Great Changes, Inc. (www.greatchanges.com), which holds the patent for the innovative fatigue detection and coaching technology, and Wombatt (www.wombatt.net), which brings cutting-edge algorithms and platform support. Together, they have combined their expertise to develop a voice-based Fatigue Risk Prediction system that helps to prevent fatigue on the road."
WOMBATT is a recognized expert in managing employee fatigue, sleep, and drowsiness in the industry. He founded WOMBATT 23 years ago at the University of South Australia's ITEK/ Centre for Sleep Research, later inventing and patenting highly advanced fatigue technologies at the European Space Agency's space technology incubator at ESA's ESTEC facility in Noordwyk, the Netherlands.
Great Changes is a Woman Owned Small Business holding a portfolio of 11 Patents. While traveling, a UPS driver once saved her life by communicating over a CB radio, which inspired her to incorporate GPS Exits and Conversational AI to Coach Drivers and prevent accidents. After selling her engineering company, she wanted to advance transportation safety and developed and patented this process.
The system initially achieves a 70% effectiveness in predicting fatigue risk for new users. This is possible because the algorithm relies on a standard baseline for evaluation while it develops a personalized baseline for each user. By following the protocol of three recordings per workday (start/middle/end), the system begins using a basic personalized baseline after 10 consecutive days of use, continuing to refine and adapt over time. Once the data set reaches 200 recordings, the system achieves 90% prediction effectiveness. Since the system is designed to continuously learn, it always uses the most recent 200 recordings to update the user baseline, with the potential to exceed the 90% effectiveness benchmark.
Our Voice Fatigue Risk Analysis API enables developers to seamlessly integrate advanced fatigue detection into their applications. Built for scalability and accuracy, our solution helps you monitor user fatigue through voice analysis with a simple monthly subscription model.
Once your Developer Portal is activated, you will be able to:
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Access our white papers on fatigue prevention, built on data analysis, predictive models, and real-world field experience. Content designed to drive smarter decision-making in critical environments.
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