Machine Learning and Data Analytics Lab, FAU Erlangen-Nürnberg

What Machine Learning and Data Analytics Lab, FAU Erlangen-Nürnberg does

The Machine Learning and Data Analytics (MaD) Lab is an academic research laboratory within the Department of Artificial Intelligence in Biomedical Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) in Erlangen, Bavaria, Germany. It employs roughly 43 people organized into nine research groups (AI in Medicine, Digital Health Gait Analytics, Empatho-Kinaesthetic Sensor Technology, Health Data Science, Sports Analytics, Digital Health Biosignals, Biomechanical Motion Analysis, Applied Machine Learning, and Digital Health PsychoSense) and runs more than 100 active research projects. Its core technology applies machine learning and data analytics to wearable-sensor and video-based biosignals for sports and healthcare, including IMU-based gait analysis, deep-learning time-series modeling, markerless video pose estimation, biosignal processing (HRV, cortisol, respiratory signals), and speech-based Parkinson's monitoring. The lab is currently in a transitional phase following Prof. Dr. Björn Eskofier's October 2025 transfer of his main professorship to Ludwig-Maximilians-Universität München, while research and teaching are described as continuing without interruption.

The lab's product portfolio consists of open-source Python libraries and frameworks (Gaitmap, imucal, tpcp, MaD GUI, BioPsyKit, CARWatch, openTSST, Stress+) distributed via GitHub, plus research applications such as Apkinson (smartphone telemonitoring for Parkinson's via speech, gait, and hand movement) and Rheum-Yoga/YogiTherapy (yoga video plus pose estimation for rheumatology rehabilitation). Select collaborators include University Hospital Erlangen (Center for AI in Medicine), WS Audiology (ear-worn motion sensors), ProCarement GmbH (ProHerz heart-failure app), Helmholtz München, Fraunhofer IIS, and the City of Erlangen.

As a public-university research entity, the MaD Lab does not generate commercial product revenue. Operations are funded through research grants, collaborative research projects, and university funding, with research dissemination occurring via peer-reviewed publications, conference presentations, and open-source software distribution rather than commercial go-to-market channels.

Name

Machine Learning and Data Analytics Lab, FAU Erlangen-Nürnberg

Legal name

Machine Learning and Data Analytics Lab at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

Website

https://mad.tf.fau.de

Company type

Private

Founded year

2012

Operating status

Operating

Headcount range

11–50 employees

Short description

The MaD Lab at FAU Erlangen-Nürnberg is an academic research laboratory of about 43 staff organized into nine research groups, applying machine learning and data analytics to wearable-sensor and video biosignals for sports, gait, and digital-health applications, and distributing its work as open-source Python packages and research applications.

Ownership category

akta.pro rank

Product category

Machine Learning Research Software

NAICS

Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology) (541715), Research and Development in Biotechnology (except Nanobiotechnology) (541714)

SIC

Services-Commercial Physical & Biological Research (8731)

akta.pro primary industry

Digital & AI-Enabled Diagnostics (algorithmic interpretation, decision support tied to assays) (HLAAALAO)

akta.pro secondary industries

Clinical Trial Informatics (EDC/CTMS/eCOA/eTMF) (HLAGAJAM), Medical Imaging AI (HDAAAEAH)

Keywords

• Machine learning research
• Biomedical signal processing
• Gait analysis algorithms
• Digital health applications
• Open source software

Headquarters

HQ city

Erlangen

HQ country

Germany

HQ region

Europe

Offices1 record

Markets served

GTM type

B2B

Offering type

Software

Cost components

Personnel, Technology or R&D, Operations

Distribution channels1 record

Marketing channels5 records

Core offering

The MaD Lab conducts academic research and development of machine learning and data analytics methods applied to sports and healthcare, with a focus on wearable sensor-based gait analysis, biosignal processing, and digital health. It produces open-source Python libraries, research platforms, and digital health applications that enable IMU-based human movement analysis, stress detection, disease telemonitoring (Parkinson's, multiple sclerosis, heart failure, rheumatology), and reproducible biomedical research. The lab's outputs are designed for the research community and clinical collaborators rather than commercial deployment.

Product overview

The Machine Learning and Data Analytics Lab at FAU is an academic research laboratory that develops open-source software libraries and frameworks for biomedical signal processing, gait analysis, and digital health research rather than commercial products. Their portfolio consists of Python libraries (Gaitmap, imucal, tpcp, MaD GUI, BioPsyKit) for IMU-based movement analysis and biopsychological data processing, open-source research platforms (CARWatch for cortisol analysis, openTSST for stress research, Stress+ for stress induction), and mobile applications for patient monitoring (Apkinson, Rheum-Yoga). These tools are designed for reproducibility and benchmarking in academic research rather than clinical deployment.

Differentiator

Problem solved

Functional benefit

Products and services

• Gaitmap An open ecosystem for IMU-based human gait analysis and algorithm benchmarking, providing standardized tools for gait parameter extraction and validation. Aimed at researchers and clinicians working on wearable-sensor movement analysis.
• imucal A Python library for calibrating 6 DOF IMUs (inertial measurement units), enabling accurate sensor data preprocessing for gait analysis applications.
• tpcp (Tiny Pipelines for Complex Problems) A set of framework-independent helpers for algorithm development and evaluation, designed for creating portable and reproducible processing pipelines.
• MaD GUI An open-source Python package for annotation and analysis of time-series data, supporting manual labeling workflows for sensor-based movement analysis.
• BioPsyKit A Python package for the analysis of biopsychological data, supporting integration of physiological and psychological measurement data for health research.
• CARWatch An open-source framework for objective cortisol awakening response assessment, providing standardized methodology for stress hormone measurement analysis.
• openTSST An open web platform for large-scale, video-based motion analysis during acute psychosocial stress induction, enabling standardized stress research protocols.
• Stress+ A web framework for the remote induction of acute psychosocial stress, enabling contactless stress research studies.
• Rheum-Yoga (YogiTherapy) A mobile application for rheumatology patients combining yoga video tutorials with pose-estimation for home-based rehabilitation therapy, developed in collaboration with Rheumatology at University Hospital Erlangen.
• Apkinson A smartphone application for multimodal telemonitoring of Parkinson's disease patients through speech, gait, and hand movement analysis.

Named customers4 records

Segments3 records

Ideal customer profiles3 records

Technology focussed Yes

API detail

Has API

No

API docs

API detail

Core technology

AI maturity

App detail

AI capability8 records

Feature5 records

Partnerships

Five partnerships are on record, tiered core and minor.

• WS AudiologycoreStrategic or Co-development Partner · 29 April 2025Collaboration on study exploring how hearing aid amplification affects gait performance using hearing-aid integrated motion sensors, demonstrating potential of earables for health monitoring.
• ProCarement GmbHminorStrategic or Co-development Partner · 1 April 2025Collaboration on digital health application ProHerz for heart failure patients, exploring better knowledge transfer and patient engagement in digital health apps.
• University Hospital ErlangencoreStrategic or Co-development PartnerThrough the Center for AI in Medicine, the lab establishes collaborations with partners at University Hospital Erlangen for interdisciplinary research at the intersection of machine learning and clinical medicine.
• Helmholtz MünchencoreStrategic or Co-development PartnerProf. Eskofier remains affiliated with Helmholtz München after assuming main position at LMU München, indicating ongoing research collaboration.
• City of ErlangenminorStrategic or Co-development PartnerCollaboration on monitoring of urban water demand for city trees using sensor technology and data-driven algorithms.

Scale indicators4 records

Recent moves6 records

Expansion highlights5 records

Direct peers

• Kinesis Health Technologies: Commercial provider of body-worn sensor systems for falls risk assessment and gait analysis. Comparable to MaD Lab in using inertial sensors for clinical-grade movement analysis in older adults and patient populations.
• Mobilise-D Consortium: Multi-institutional research consortium developing digital mobility outcomes for regulatory-grade assessment. The MaD Lab is an active partner, making this a directly comparable peer in real-world IMU gait analysis.
• ProtoKinetics: Developer of the Zeno Walkway and PKMAS gait analysis systems used in clinical and research settings. Directly comparable in producing standardized gait/movement analysis outputs for clinical decision support.
• APDM Wearable Technologies (now part of Clario): Provider of wearable inertial sensors and gait/mobility analysis platforms for clinical trials and research. Highly comparable to MaD Lab's IMU-based gait analytics focus, with strong overlap in Parkinson's and movement disorder endpoints.

Emerging players

• Imperial College London – Hamlyn Centre: Leading UK research centre for wearable robotics, bio-sensors, and AI in healthcare. Comparable peer academic institution working at the intersection of ML, wearable sensors, and clinical applications.
• ETH Zürich – Sensory-Motor Systems Lab: Academic research group applying ML and sensor technologies to movement analysis and rehabilitation. Comparable as a peer academic lab with overlapping focus on wearable sensing for gait and motor function.

Broad incumbents

• BioStamp (MC10 / Medidata): Wearable biosensor platform for clinical research and digital biomarkers, now part of Medidata. Comparable in providing wearable sensor infrastructure used in clinical trials for movement and physiological monitoring.
• Sword Health: Digital musculoskeletal care company using AI and motion sensors to deliver remote physical therapy. Comparable in leveraging ML on movement/sensor data for rehabilitation, though operating at commercial scale rather than research-only.
• Withings: Consumer-grade connected health devices including smart scales, watches, and sleep monitors. Comparable as a player in wearable health sensing with gait/activity analysis capabilities, though serving consumer rather than clinical research markets.
• Hinge Health: Large digital MSK and chronic condition platform using motion sensing and computer vision for at-home rehabilitation. Comparable in combining AI with motion analysis for therapeutic outcomes, but at significantly greater commercial scale.

Market position

Strengths4 records

Weaknesses4 records

Competitive moat4 records

Key risks6 records

Key highlights6 records

Customer concentration

• Xx.com/FAU_MaD_Lab
• GitHubgithub.com/mad-lab-fau
• Facebookfacebook.com/Digital-Sports-Group-328888277318493
• LinkedInlinkedin.com/company/mad-lab-fau-erlangen-nuernberg

Revenue estimate

Valuation estimate

Number of profiles

Profiles1 record

Funding overview

Funding rounds

Investors

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M&A

Investments

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Frequently asked questions about Machine Learning and Data Analytics Lab, FAU Erlangen-Nürnberg