Eliminate subjective clinical diagnostics

Automate movement-based neurological tests
with our machine learning powered analysis

We are
human movement

As the e-Health spin-off of Cursor Insight, we are a prominent expert in the field of scientific analysis of digitally recorded human movement. 

Our best-in-class algorithm can process all kinds of movement data gathered with a wide range of devices. It can be applied to analyse and classify movement data, predict behaviour and health conditions.

AI-powered, automated analysis

Standardised assessment of movement with our award-winning, deep learning algorithm

Elimination of subjectivity

Our solution addresses both intra- and inter-rater reliability problems inherent in neurological tests

Comparable results

Longitudinal quantitative data allows for precise tracking of the progression of disease as well as the effects of medication

Computer vision aided tests

Wearable sensor- and marker-free solution guarantees the assessment of patients' unhindered movements

Secure cloud platform

All video and patient data is stored in GDPR and HIPAA compliant, secure cloud infrastructure

30% time saving

Spend less time on data collection and more time on patient care

View in action

What does NurologIQ do and how does it eliminate subjectivity from clinical diagnostics? Watch this short video to find out more.

Our focus areas

Sample results from NurologIQ UPDRS analysis


A simple video recording.

That is all we need to analyse your patients' conditions by extracting biomarkers from the video. Our focus is automating standard, movement-based neurological tests with the aid of machine learning.  

  • Markerless biomarker extraction
  • Quantitative test results
  • Objective and comparable data
Precise, easy cognitive test with digital pen


Perform a writing task on a digital tablet or simply move your mouse on a computer.

We are developing a fast and simple screening method for the early detection of cognitive impairment. Our solution identifies warning signs earlier than existing methods without the need for specialists and expensive tests. 

  • Based on hand movement biomarkers
  • Clinically proven prediction model
  • It only takes few minutes to perform and delivers results instantly
Dokivideo telemedicine platform for monitoring ill patients remotely


No need to sync calendars with patients.

Our two-way, asynchronous telemedicine platform enables you to monitor chronically ill patients in the comfort of their homes at a time most convenient for them. Easy-to-perform video-based tasks that allow you to track aspects of their health.

For more information, visit dokivideo.hu now.

  • Works with computer webcams or smartphones
  • Runs in the browser, no downloads required
  • High-quality videos, even with bad internet connection

For an in-depth look at our solutions, book a demo now:

Potential use cases

Clinical trials need objective and quantifiable test results for neurological tests based on body movements

Clinical trials

Our computer vision-based solution ensures precise movement measurements without the use of cumbersome sensors. Uncover and track the effects of pharmaceuticals through objective, quantitative and comparable data.

Regular check-ups of chronically ill patients is faster and more precise with AI assisted screening tools

Regular check-ups

Automate standard tests to gain more objective and granular information about your patients. Not only will you save time, but you will have deeper insights into the progression of your patients' conditions.

Simple, yet precise cognitive screening test can be conducted on an everyday tablet

Cognitive screening

Conduct fast and easy cognitive testing for your patients via our tablet or computer-based solutions. Tasks are easy to perform for all ages and provide instant results. Accessible, early detection is the key to mitigating the most serious symptoms.

Dokivideo telemedicine platform helps doctors perform check-ups of their patients in the comfort of their homes

Remote monitoring

Check-ups in the comfort of your patients' home. Let your patients and their caregivers record tasks at the time most convenient for them. Asses your patients automatically and remotely with the aid of our telemedicine platform.

Our award winning technology

Winner of SigWiComp signature verification competition of the German Research Center for AI

Signature Verification
Digital Alliance Prize Innovation and Technology Ministry of Hungary

Save Life

Winner of the Mouse Dynamics Challenge competition of Balabit, a leading cyber security company

Mouse Dynamics Challenge

Movement Reconstruction

Our technology recreates original human movement by cleaning and smoothing digital data


Our mathematical meta-language transforms the reconstructed movement into tens of thousands of different features

Movement Classification

Our machine learning tool builds prediction models using movement features

What our partners say

“A video about the patient based on a pre-choreographed motion sequence could help support patient care”

“An online communication platform would be a great support to us, where patients with physical disabilities could continue to be cared for, using videos recorded by family members”

Core team

Gabor Toth Patient Record CEO

Gábor Tóth


Gábor is a business strategist and experienced leader with 20+ years experience in driving digital transformation

Gergely Hanczar PhD Patient Record Head of R&D

Gergely Hanczár PhD

Head of R&D

Gergely is a human motion expert and serial entrepreneur with a background in mathematical modelling

Erika Griechisch PhD

Erika Griechisch PhD

Data Scientist

Erika is an academic researcher with 10+ years focus on AI assisted biometric signature and handwriting verification

Bence Borbély PhD AI/ML research engineer

Borbély PhD

AI/ML Research engineer

Bence is a machine learning and visual image processing specialist with a focus on AI powered human arm movement analysis

Do you have questions?
We are here to answer.

Objective and quantitative neurological examinations with the help of machine learning

Eliminate subjective clinical diagnostics