Technology
We offer a fully autonomous medical diagnostic system (agentic AI) delivered to users via a mobile application. It can predict and prevent road traffic accidents caused by tiredness and inattention (achieving research-proven 94% effectiveness within just 3 months of contact with a given individual). Our technology is also used for presymptomatic medical diagnostics of prevalent neurodegenerative diseases (e.g., Parkinson's disease approximately 5 years before the first tremor) on the basis of subtle driving anomalies.
SUMMARY
Fundamental Neuroscience
Two years of scientific research (including the foundational model training workflow) with novel findings about human cognition. This process combined mathematical discoveries, VR simulations, real-world testing and neuroscience-related experiments — allowing us to introduce an original mathematical diagnostic paradigm characterized by unprecedented sensitivity and effectiveness
Mathematical Ground Truth
One of the largest proprietary datasets of digital biomarkers (focused on attention stability and high-dimensional health phenotyping), directly correlated with the way the human brain allocates its resources at the most fundamental level
Technological Engine
Proprietary ML models (custom LSTMs with a tailored attention mechanism and a bespoke cost function that operate within a novel adversarial inverse reinforcement learning framework) and a diagnostic-grade training workflow that effectively combined non-invasive functional neuroimaging (e.g., EEG, fNIRS) and eye tracking with micro-adjustments of high-level driving attitudes and in-vehicle behavioural patterns
Proprietary Signal Processing
Feature engineering and data collection methods designed to successfully separate relevant signals (related to the user's health phenotypes and digital biomarkers) from high-noise environmental artefacts — optimized for older mobile devices and inherently enabled to suppress external factors, such as road unevenness, weather, worn vehicle components (e.g., brake discs, tires), driver respiration, arterial blood flow, and even random events like in-seat movements of a passenger
Edge Personalization
On-device individual cognitive decoder implemented as a static behavioural adapter (a compact set of algorithms tailored to the user's brain, requiring no local ML computing) and regularly actualized after data processing in the cloud
Custom Data Flywheel
Continuous evolution of the model thanks to raw, anonymized data from multiple users of our mobile application — aggregated in the cloud and utilized either to update the foundational architecture or to create its specialized variant
Seamless User Journey
No action is required from the user beyond installing our fully autonomous mobile application that operates in the background (regardless of whether the phone is mounted on the dashboard or kept in a pocket) and, whenever necessary, [1] emits sound alerts designed to avert attention lapses at least three minutes before any discernible cognitive impairment, or [2] provides a comprehensive medical assessment or a conclusive diagnosis of neurodegenerative disorders (PD, AD, ALS) 4–8 years before the onset of clinical symptoms
INTRODUCTION
TCR App Mobility offers a fully autonomous medical diagnostic system (agentic AI) delivered to users via a mobile application. It can predict and prevent road traffic accidents caused by tiredness and inattention (achieving research-proven 94% effectiveness within just 3 months of contact with a given individual). This process is made possible by converging raw IMU and GPS measurements (registered both inside and outside the vehicle through built-in smartphone sensors) with infinite-dimensional modelling – leveraging our proprietary, empirically-derived mathematical constructs that are entirely independent of driving experience, advanced driver assistance systems (ADAS), mechanical issues (e.g., worn tires), environmental conditions (e.g., heavy rain, road unevenness), random events (e.g., in-seat movements of a passenger) and temporary or permanent vehicle changes.
Our solution emits sound alerts whenever a dangerous data pattern is detected — averting the natural lapses in attention at least three minutes before any discernible cognitive impairment. It ensures sustained awareness throughout the entire journey.
Our ML models are rooted in two years of scientific research with novel findings about human cognition. Leveraging this expertise, we have built one of the largest datasets of digital biomarkers (focused on attention stability and high-dimensional health phenotyping) — combining non-invasive functional neuroimaging (e.g., EEG, fNIRS) with micro-adjustments of high-level driving attitudes and in-vehicle behavioural patterns.
Our VR-based training workflow with diagnostic-grade feature engineering allowed us to correlate actual brain activity with physiological signals and behavioural anomalies manifested while driving a vehicle. We utilize our proprietary deep neural networks (custom LSTMs with a tailored attention mechanism and a bespoke cost function that operate within a novel adversarial inverse reinforcement learning framework) to translate collected data into personalized mathematical representations of the user's cognitive states. The predictive power of our system lies in its ability to monitor the evolution of AIRL-derived driving reward functions over time. By analysing the longitudinal drifts of their distribution and the fluctuations of their internal structure within our infinite-dimensional feature space, we can precisely determine when the user's driving patterns are impacted by approaching attention lapses.
Moreover, our algorithms are able to identify subtle changes in driving habits that are characteristic of prevalent neurodegenerative diseases (PD, AD, ALS) approximately 5 years before the onset of clinical symptoms (allowing the development and early start of disease-modifying therapies).
COMPETITION AND USP
The vast majority of the population is not protected in any systematic way against tiredness and inattention while driving. Our indirect competitors primarily include the vehicle-embedded safety systems (developed by car manufacturers), which utilize eye and lane-tracking cameras to detect excessive tiredness once it has already begun (without any real predictive abilities). These systems can also be acquired independently as either a device or a mobile application. In terms of medical diagnostics, similar methodologies are limited to theoretical literature.
In contrast, our technology:
[1] is based on science-backed custom deep learning models that are kept as trade secrets (allowing 94% effectiveness in prediction of road traffic accidents caused by inattention and guaranteeing a 5-year PD diagnostic lead time needed to deter future market entrants);
[2] can automatically describe the user's individual brain activity while driving a vehicle (correlating non-invasive functional neuroimaging and standard smartphone sensors);
[3] maps complex neurophysiological impairments (e.g., prodromal stages of PD) onto longitudinal drifts of AIRL-derived driving reward functions in our infinite-dimensional feature space (our original discovery that allows us to both identify the functional impact of the first 5–10% of neuronal degeneration and predict approaching attention lapses caused by standard tiredness) — embedding a clinical-grade tool in a consumer-grade device;
[4] is implemented as a fully autonomous mobile application (agentic AI) with a proprietary data flywheel and a unique multi-modal dataset of digital biomarkers;
[5] operates in the background of the user's daily routine and transforms regular driving into a part of the medical diagnostic system, ensuring long-term compliance without requiring any lifestyle changes or conscious interaction from the user (eliminating the engagement decline and individual biases);
[6] introduces an original mathematical diagnostic paradigm characterized by unprecedented sensitivity and effectiveness, directly correlated with the way the human brain allocates its resources at the most fundamental level.