INTRODUCTION

 

TCR App Mobility offers an autonomous medical diagnostic system (agentic AI), delivered to users via a SaMD-compliant 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 (acquired 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 tyres), environmental conditions (e.g., heavy rain, road irregularities), random events (e.g., in-seat movements of a passenger) and temporary or permanent vehicle changes.

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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.

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Once the collected raw data (IMU and GPS measurements) is cleaned and enriched via advanced feature engineering, it is processed through our proprietary "analytical neural network" architecture (a purpose-built LSTM with a tailored attention mechanism and a domain-specific cost function). Moving beyond standard supervised learning, we employ our novel adversarial inverse reinforcement learning (AIRL) framework — enhanced by latent behavioural embedding capabilities — to automatically infer the individual "driving reward function" of each user.

 

Central to our predictive methodology is an original mathematical construct established through two years of foundational scientific research — referred to as the "diagnostic function". This non-linear operator explicitly allows us to determine when the user's driving patterns are impacted by impending attention lapses by mapping AIRL-derived driving reward functions into a specialized, infinite-dimensional feature space associated with a positive-definite kernel (the "diagnostic space").

 

By treating driving as an optimization problem, we can mathematically model the underlying logic behind the patient's subconscious decisions. Even a marginal shift in the user's neurological state triggers a non-linear jump in the driving reward function's raw values — analogous to how a single character change completely alters a cryptographic hash. The mapping of driving reward functions into the diagnostic space yields what we define as the "driving phenotype". Our system tracks the kinematics of a patient's phenotype — its translational drift (the velocity and direction of its movement through the diagnostic space), its rotational dynamics (the specific axes and angular velocities), and the modalities by which its internal structure is reconfigured. This multifaceted metamorphosis acts as a neurological biomarker, allowing us to capture the metabolic cost of the brain's attempt to maintain apparent normality despite progressing anomaly.

 

Each neurodegenerative disorder (as well as other chronic diseases and transient states such as abrupt attention lapses) leaves its unique digital signature — a distinct evolutionary pattern of a driving phenotype within our diagnostic space — allowing us to perform a highly objective differential diagnosis across the entire global population.

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To distinguish between transient states (e.g., attention lapses) and long-term changes such as PD, AD, ALS, and Parkinson-plus syndromes, our system utilizes a proprietary Temporal Geometry-Aware Transformer (the "diagnostic neural network") trained on synchronized EEG, fNIRS, eye tracking (saccadic latency, fixation precision, microsaccade dynamics), sEMG, GSR, HRV, pressure mapping, and vehicle telemetry data acquired within our custom-engineered VR driving simulator. Therefore, the temporary alteration of an individual's driving phenotype serves as a unique representation of precisely defined neurophysiological processes (e.g., those detected during our ML training within the fronto-parietal attention networks, the default mode network, or the locus coeruleus-norepinephrine system).​

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​Moreover, our algorithms precisely quantify disease-specific alterations in entangled driving dynamics that are characteristic of neurodegenerative disorders (as well as confounding non-CNS conditions) 4–8 years before the onset of clinical symptoms — allowing the development and early start of disease-modifying therapies.​

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COMPETITION AND USP

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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, our technology establishes an entirely new diagnostic paradigm.

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Our technology:

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[1] achieves research-proven 94% effectiveness in prediction of road traffic accidents caused by inattention within just 3 months of contact with a given individual;

 

[2] is able to detect the presence of prodromal Parkinson's disease with clinical-grade sensitivity (>92%) and specificity (estimated 94%), approximately 5 years earlier than hospital brain scans;

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[3] can automatically describe the internal energy and attention allocation within the human brain, correlating advanced functional neuroimaging and built-in smartphone sensors;

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[4] requires no active participation, manual logging or specific engagement — removing the subjectivity bias and performance anxiety that often skew results in clinical tests;

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[5] is implemented as an autonomous, SaMD-compliant mobile application (agentic AI) that eliminates the need for lumbar punctures, radiopharmaceuticals and hospital visits during patient stratification;

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[6] transforms expensive, invasive, ineffective and inaccessible diagnostics into a seamless background process, providing unique digital biomarkers that meet the rigorous standards of pharma R&D;

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[7] distinguishes PD from Parkinson-plus syndromes (e.g., MSA), other neurodegenerative diseases (e.g., AD, ALS) and non-neurological conditions with the highest discriminatory power (specificity of 94%) by successfully mapping divergent digital signatures;

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[8] introduces an original neurological diagnostic paradigm characterized by medical-grade sensitivity and specificity, directly correlated with the way the human brain allocates its resources at the most fundamental level.