- Customer story
How to assess impairment when safety is critical
As researchers strive to understand the extent and scope of cannabis’s impairing effects, the team at Gaize are on a mission to develop technology that accurately detects impairment from cannabis, and other altering substances. Ken Fichtler, CEO of Gaize, seeks to leverage the power of eye tracking technology and VR headsets to develop a solution that not only detects impairment caused by drugs like cannabis, but that also shifts the way we think about safety on the road and in the working environment.
Unlike alcohol, the effects of THC (or Delta-9 Tetrahydrocannabinol, the primary psychoactive component in cannabis) are not easily quantifiable through traditional chemical tests, since the amount of THC in the body is not correlated with impairment. THC and its metabolites can remain in the body for an extended period, oftentimes over a month since the last use, making it impossible to determine if impairment is currently being experienced or if someone has simply used cannabis previously.
For about 45 years, law enforcement officers have tested for impairment using a battery of physical and eye movement tests, which are conducted by specially trained officers called Drug Recognition Experts (DREs). While the tests they use have been shown to accurately detect impairment, human error and subjectivity are common arguments against their use. The accuracy of the assessment by drug recognition expert police officers is typically around 60-85%. The inability to scale human Drug Recognition Experts to the private sector and the accuracy challenges noted above necessitated an innovative approach.
Gaize is a software company dedicated to accurately assessing impairment for the purpose of safe driving and working enforcement to guarantee that cannabis legalization happens in a safe and sustainable way. At the heart of its innovation lies the Gaize Impairment Testing Platform, a rapid and portable automated detection system specifically designed to recognize the effects of cannabis, alcohol, opiates and other drug impairment. This platform is based on the Drug Recognition Expert eye tests and the decades of law enforcement expertise and research in detecting drug-impaired drivers.
The Gaize solution runs the same eye tests that DRE police officers do in an entirely automated fashion using a VR headset. With the help of Tobii eye tracking and Tobii Ocumen, these tests include high-precision ocular motion and pupillary reflex analysis, measuring subtle changes in eye movement that indicate impairment. Once the eye movement data is captured, machine learning algorithms analyze the data for any signs and symptoms of impairment. Within six minutes of starting the test, the results are available.
Not only is this process non-invasive and rapid, but it boasts 98% accuracy in detecting the same signs of impairment that law enforcement officers look for - much higher than the 60-85% accuracy of DRE police officers. This can be achieved with little extra training for police since it runs automatically on the VR headset, offers an easy user interface, and delivers results that are straightforwardly understood. If required, the high-quality video recording and other meta data can serve as evidence, making the case significantly stronger than relying on just officer testimony.
To ensure controlled and accurate testing environments, Gaize chose to use a VR headset; specifically, the Pico Neo 3 Pro Eye. This device was an obvious choice due to its portability, high precision Tobii eye tracking integration, the ability to control light conditions, and compatibility with Tobii Ocumen. Its portability and versatility make it perfect for use in roaming situations like at the roadside or worksites. The combination of powerful VR and accurate eye tracking technology paved the way for Gaize's comprehensive solution.
To ensure validity in their methods, Gaize has been diligent in collecting high quality data for the training of its machine learning algorithms. In November 2022, the company completed the world's largest clinical trial studying cannabis impairment and built the world’s largest impaired eye movement dataset, with 500 million data points. Since each class of drug has a unique way of impacting the eye movement, enriching Gaize’s database will allow it to analyze impairments of many other drugs besides cannabis. This milestone not only validated Gaize's technology but also helps refine and optimize their machine learning models. All this results in the exceptionally high accuracy of the test result.
Eyeing the sizeable market of law enforcement and B2B industries like construction, manufacturing, energy, transportation, and more, Gaize has built a business model to capture the rapidly growing desire for driving and working sobriety tests. The platform has already made its mark in three countries, and product’s capabilities only continue to expand. Initially focused on cannabis impairment, Gaize now detects signs of impairment caused by a range of substances, including opiates, stimulants, alcohol, depressants, and more.
In safety-sensitive environments, such as driving and operating heavy machinery, it's paramount to prevent accidents and ensure the well-being of all individuals involved. Even experienced cannabis users show reduced ability to react quickly and make split-second decisions while impaired. The historical lack of reliable impairment detecting methods has also led to improper treatment of legal cannabis users, including DUI cases and adverse employment. Gaize’s innovative solution solves this problem with the best possible technologies available today.
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Head of Marketing, XR, Tobii
As the head of marketing for the XR segment at Tobii, I get to tell amazing stories about our eye tracking sensor technology and how it is put to good use in VR and AR. I get inspired by the innovations that enhance understanding of ourselves, break the physical and financial barriers, help address incurable diseases, and fuel curiosity to explore new frontiers. It feels great to connect the magic of technology with the need of the users.
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