Oraigo Co-founder & CEO Michele Galetta: Integrating Neurotech and AI for Safer Driving

Michele Galetta (MG): Drowsy driving has always been a significant concern for me, particularly falling asleep at the wheel. This issue became particularly pronounced during long drives from our home in the North to visit my parents' hometown in the South of Italy. These journeys often spanned eight hours, typically necessitating night driving to avoid heavy traffic.

Given my personal experiences, I felt compelled to develop a solution. My initial approach involved developing an application for the Apple Watch. Securing funding from a business angel, we conducted tests on a circuit to simulate real-world driving conditions. However, we soon realized that conventional sensors were inadequate for reliably detecting drowsiness.

Undeterred, we revisited existing research and discovered the potential of brain signals, particularly those used in polysomnography. Collaborating with the University of Padua and a local hospital, we established a comprehensive protocol. This involved gathering various bodily signals, including EEG, EMG, and ECG data. Through rigorous analysis, we determined that brain signals offered the most accurate indicators of drowsiness.

(MG): Our approach involves leveraging EEG data through proprietary neural networks and AI. These systems are trained to detect specific patterns indicative of drowsiness at the wheel. Upon identifying such moments, we swiftly send alerts to the driver, allowing them two seconds to respond by blinking. This intervention effectively disrupts the monotony of driving tasks while also confirming the driver's wakefulness.

However, our efforts extend beyond mere alert systems. We've developed three core pillars to enhance road safety for transport companies. Firstly, our interventions address immediate risks like microsleep episodes. Secondly, we provide drivers with actionable insights gleaned from their data. By analyzing patterns in their driving behavior, we help them understand and rectify weaknesses, fostering continuous improvement.

Moreover, our web application, Fleets, empowers transport companies with comprehensive views of their drivers' performance. This includes aggregated data on driving behaviors, trouble spots, intervention frequency, and performance trends over time. Armed with these insights, companies can make informed decisions to mitigate risks associated with drowsiness and optimize fleet productivity.

(MG): Hardware plays a pivotal role in our system. It forms the foundation upon which everything else is built. If the hardware isn't reliable or comfortable for users, the entire system suffers. We've undergone numerous iterations to refine our hardware, ensuring it meets both functional and ergonomic standards.

Looking ahead, I envision a trend toward increasingly compact hardware. As devices shrink in size and become less conspicuous, adoption rates are likely to soar. Initially, introducing a novel product like ours requires time to establish credibility and garner user trust. However, as awareness grows and similar technologies become more commonplace, wearing brain-computer interface devices could become as routine as wearing any other wearable tech.

By consistently demonstrating the capabilities of our technology and observing advancements in other brain-computer interface companies, we anticipate a future where such wearables seamlessly integrate into everyday life.

(MG): When it comes to hardware, striking a balance between functionality and user comfort poses a significant challenge. On the one hand, maximizing the device's capabilities often involves incorporating numerous electrodes to enable a wide range of applications. However, this can lead to a bulky and uncomfortable design that consumers are reluctant to wear for extended periods.

To address this, careful consideration must be given to selecting the optimal number of channels that offer the broadest functionality while maintaining comfort. Additionally, material selection plays a crucial role in ensuring both comfort and signal quality. For instance, while fabric may be the most comfortable option, it might not yield sufficient signal quality for certain applications.

In our case, signal quality is paramount, particularly for tasks like microsleep and drowsiness detection. Our automated setup process further streamlines usage, allowing for quick and efficient data collection without compromising signal quality.

(MG): I hold the belief that autonomous driving will undoubtedly influence the trajectory of our developments. However, I also perceive a reciprocal relationship where our innovations will impact the evolution of autonomous driving technology. In my view, brain signals represent the most valuable data stream from the human body, offering profound insights into user behavior and cognition.

The ability to harness and interpret these signals to create applications marks the advent of a new era in wearable technology. Despite the challenges of ensuring user comfort and exploring additional applications, I foresee a revolutionary shift propelled by the utilization of brain signals. Our focus on addressing issues like drowsiness and microsleep not only showcases the potential of this technology but also underscores its ability to tackle some of the most pressing global challenges.

(MG): I can foresee practical applications for our technology, especially in industries like construction or factory settings, where it could be readily deployed to enhance safety and efficiency. Additionally, we're focusing on the productivity aspect, which holds significant potential for our clients. By providing insights to companies in an aggregated manner, we can help them identify areas for improvement and boost profitability while also prioritizing the well-being of their workforce.

Consider the scenario of a driver experiencing drowsiness or microsleep while on duty. Traditionally, raising such concerns with an employer might lead to personal judgment or scrutiny. However, by presenting this data in an aggregate format, we empower employers to address risks without singling out individuals. For instance, instead of pinpointing specific employees, our system highlights trends and distributions of drowsiness events across the fleet. This approach encourages a focus on systemic improvements rather than individual blame.

And so, by leveraging aggregated data, employers gain valuable insights into potential risks without compromising employee privacy. They can then use this information to refine their management strategies and create safer, more productive work environments for everyone involved.

(MG): Privacy holds paramount importance in our approach. We view it not just as a necessity but as a valuable asset that promotes trust and openness among users. Our goal is to empower drivers to communicate their situations to employers without fear of judgment or intrusion.

To uphold this principle, we've implemented stringent privacy measures. When drivers log in, they're assigned a unique code that we never associate with personal information like names or emails. This complete anonymization ensures that we never possess identifiable data, thereby safeguarding user privacy from the outset.

By adhering to this approach, we eliminate the possibility of employers requesting or accessing sensitive personal information. Instead, we focus solely on delivering functionality while preserving anonymity. Though organizing such systems without personal data presents logistical challenges, we've found innovative ways to provide essential features without compromising privacy.

(MG): Our process involves providing companies with our solution on a trial basis, during which we collect data without disclosing driver identities. This initial phase, referred to as phase two, allows us to measure various parameters such as events, interventions, and activity states. Our solution operates in a "ghost mode," gathering insights discreetly.

Once this data collection period concludes, we transition to phase three, activating all functionalities and comparing data from before and after implementation. This allows us to assess the effectiveness of our solution in improving driver behavior and overall safety.

The insights we provide to companies are straightforward to interpret, as they include statistical comparisons between pre- and post-implementation periods. By offering this tangible evidence of improvement, we empower companies to make informed decisions about the efficacy of our solution in enhancing driver safety and productivity.

(MG): We're currently in the process of fundraising, and the primary reason behind this initiative is to scale up our operations. With our first client onboard, we're eager to ramp up production and order hundreds of units. This expanded inventory will allow us to distribute our devices to a broader range of companies for trial purposes.

By increasing our inventory to hundreds of units, we aim to facilitate more extensive trials with potential clients. This approach is crucial for accelerating our growth, as it enables us to demonstrate the effectiveness of our solution to a larger audience.

By obtaining additional units, we can streamline this process and expedite our expansion efforts. In addition to scaling up operations, we plan to invest in our sales department and generate more awareness about the underestimated problem of drowsiness and fatigue and their impact on road safety and the efficiency of transport fleets.