Project Overview
The MaD Lab is organized into seven research groups working on the following topics:
Applied Machine Learning
- Applications of Deep Learning for Signal Analysis
- Online Handwriting Recognition
- Tracking in the Deutsche Museum Nürnberg
- Machine Learning for Predictive Analytics
- Holistic customer-oriented service optimization for fleet availability
- Tool Tracking
- Digital Twin of Rheuma
- Machine Learning for CT-Detector Production
- VERA (Video-based Re-Identification for Animals)
- Multimodal Machine Learning for Decision Support Systems
- Biologically-inspired self-supervised systems
- Automated German Crossword Resolution
- Monitoring of urban water demand for city trees
- TEF-Health
- TEAM-X
- SELFCURE: Evolutionary and cognitive processes underlying self-medication of immune-challenged bats
Biomechanical Motion Analysis and Creation
- Applications of Biomechanical Simulations
- Fundamentals of Biomechanical Simulations
- Personalization of Muscoskeletal Models
- Digital Twin of the Musculoskeletal System
- Machine Learning for Personalisation of Biomechanical Movement Simulations (C01)
- Machine Learning for Neuromusculoskeletal Modelling (C03)
- Individual Performance Prediction Using Musculoskeletal Modeling
- Biomechanical Assessment of Big Wave Surfing
- TEF-Health
Digital Health – Biosignals
- DIAMond
- DigiOnko
- Federated Machine Learning for Electronic Health Records
- Security for Future Patient-centered Healthcare Ecosystem
- Sensor-based movement and sleep analysis in Parkinson’s syndrome (D04)
- SHIELD
- SMART Start
- SWODDYS: Smart WOund Dressing incorporating Dye-based Sensors
- TEAM-X
Digital Health – PsychoSense
- Empathokinesthetic sensors for biofeedback in depressed patients (D02)
- Contactless measurement of stress, its determinants, and consequences (D03)
- Empathokinaesthetic measurement and movement pattern recognition as biomarkers for health status and prognosis of palliative care patients (D05)
- Machine Learning in Health Psychology
- Novel Methods for (Remote) Acute Stress Induction
- Interventions for Acute Psychosocial Stress Responses
- BioPsyKit – A Python Package for the Analysis of Biopsychological Data
- CARWatch – An Open-Source Framework for Improving Cortisol Awakening Response Sampling
- bidt-Digitalisierungskolleg – Applied Data Science in Digital Psychology
Digital Health – Gait Analytics
- Mobility_APP
- Mobilise-D
- Activity Recognition using IMU Sensors integrated in Hearing Aids
- Biomarkers for immunotherapy in multiple sclerosis patients
- Parkinson’s disease symptom detection and prediction
- TEF-Health
Empatho-Kinaesthetic Sensor Technology (EmpkinS)
- Machine Learning for Personalisation of Biomechanical Movement Simulations (C01)
- Machine Learning for Neuromusculoskeletal Modelling (C03)
- Empathokinesthetic sensors for biofeedback in depressed patients (D02)
- Contactless measurement of stress, its determinants, and consequences (D03)
- Sensor-based movement and sleep analysis in Parkinson’s syndrome (D04)
- Empathokinaesthetic measurement and movement pattern recognition as biomarkers for health status and prognosis of palliative care patients (D05)
Sports Analytics
Group page
Completed Projects
- FallRiskPD
- Mobile GaITLab
- EFI Moves
- Information Management System for Automated Quality Assessment in Radiotherapy
- MotionLab@Home
- MoveIT
- Smart Annotation using Semi-supervised Techniques
- BayMED Sleep Project
- HOOP: mHealth tOol for parkinsOn’s disease training and rehabilitation at Patient’s home
- Gait Analysis in Rodents
- Assessment and Improvement of Mental Health
- Connected Movement
- Development of Sensor System for Dry Training in Biathlon
- Diagnostic Imaging in Virtual Reality
- Digital Sports Bavaria
- Digital Sports Hub
- Digital Twin
- Digital Vision Trainer
- ESI@Fitness
- Match data-based performance indicators in professional football
- miLife
- Performance Analysis in Team Sports
- VR Amblyopia Trainer
- VR – Oculomotor Test System