Where Research Meets Care

We are a team of researchers and technical experts, working with hospitals and looking to expand collaboration in healthcare and rehabilitation.

Impact by the Numbers

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Patients Assessed

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Development Started

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Clinical Partners

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Tracked Motion Metrics

Objective monitoring

Objective Patient Monitoring

Tools designed for physicians and therapists to accurately track rehabilitation progress. Save time during consultations and support clinical decision-making with reliable data.

Rehabilitation support

Rehabilitation Support

We collaborate with rehabilitation specialists to restore facial function and balance. Our solutions provide feedback to both patients and professionals, improving adherence and therapy outcomes.

Research collaboration

Research Collaboration

We co-develop and validate diagnostic technologies with clinical partners. Join us for joint studies, co-authored publications, and translation of research into clinical practice.

Expansion Paths and New Clinical Frontiers

A sharper roadmap for where HABAPP can grow next: stronger core capabilities, smarter clinical workflows, and broader medical adoption.

Platform Expansion
  • HABAPP Core
  • Real-time Metrics
  • Angle Engine
  • Length Tracking
  • Rotation Mapping
  • AI Classification
  • Smart Assistant
  • Rehab Workflows
  • Flexion
  • Extension
  • Progress Slider
  • 3D Skeleton
  • Hand Tracking
  • Spine Analytics
Clinical Adoption Areas
  • Measurement Hub
  • Statistics Dashboard
  • Classification + LLM
  • Orthopedics
  • Physiotherapy
  • Neurology

The combination of objective metrics, AI classification, and a language assistant can accelerate clinical decisions, improve patient education, and support multidisciplinary teams.

Get in touch with us

Join forces with us

"We want to bring our results where they truly help."

More information

Selected Publications

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  1. Spark, A., Kohout, J., Verespejova, L., Chovanec, M., & Mares, J. (2025). Multi Path Heterogeneous Neural Networks: Novel comprehensive classification method of facial nerve function. Biomedical Signal Processing and Control, 101, 107152. DOI
  2. Shayestegan, M., Kohout, J., Trnkova, K., Chovanec, M., & Mares, J. (2024). Gait disorder classification based on effective feature selection and unsupervised methodology. Computers in Biology and Medicine, 170, 108077. DOI
  3. Shayestegan, M., Kohout, J., Verespejova, L., Chovanec, M., & Mares, J. (2024). Comparison of Feature Selection and Supervised Methods for Classifying Gait Disorders. IEEE Access, 12, 17876-17894. DOI
  4. Bohm, J., Chen, T., Sticha, K., Kohout, J., & Mares, J. (2024). Skeleton Detection Using MediaPipe as a Tool for Musculoskeletal Disorders Analysis. Lecture Notes in Networks and Systems, 909 LNNS, 35-50. DOI
  5. Kovarik, J., Schatz, M., Ciler, J., Kohout, J., & Mares, J. (2023). Kinect-Based Evaluation of Severity of Facial Paresis: Pilot Study. Lecture Notes in Networks and Systems, 596 LNNS, 127-138. DOI
  6. Kohout, J., Crha, J., Trnkova, K., Sticha, K., Mares, J., & Chovanec, M. (2018). Robot-Based Image Analysis for Evaluating Rehabilitation after Brain Surgery. MENDEL, 24(1), 159-164. DOI