Navigation

Research Groups

The MaD Lab is organized in five research groups working on different interdisciplinary topics:

 

Applied Machine Learning

The Applied Machine Learning group aims to develop and apply novel Machine Learning methods for real-world applications. Emerging digitalization allows companies from different fields of industry to produce and collect data from various resources. This is realized by technologies like the Internet of Things (IoT), cyber-physical systems and cloud-computing. All of which is summarized by the fourth industrial revolution, called Industry 4.0, with an increasing demand on research in the area of data analytics. Since these ever-growing amounts of data are difficult to process by conventional methods, machine learning and artificial intelligence provide a powerful and promising approach to handle Big Data. Thus, topics like predictive maintenance, process optimization and process automation benefit from new intelligent algorithms that are developed in the Industry 4.0 environment.

Research areas: Machine Learning, Signal Processing, Wearables, Experimental Studies

Group head:

More about the group

 

Biomechanical Motion Analysis and Creation

The Biomechanical motion analysis and creation (BioMAC) group aims to develop methods for accurate analysis and simulation of human motion, focused on gait. Movement simulations are created by solving trajectory optimization problems, using an objective related to energy, a musculoskeletal model to model the body and muscle dynamics, and constraints to define the movement task. Experimental studies are used to capture human motion for tracking and as reference. With our research, we aim to better understand human motion, and design better devices, such as prostheses, exoskeletons, and running shoes, as well as prevent injuries, such as knee osteoarthritis. Thereby, we focus on wearables and the combination of physics-based models with machine learning methods.

Group head:

More about the group

 

Digital Health – Biosignals

Introduction…

Group head:

More about the group

 

Digital Health – Gait Analytics

Our group focuses on the development and application of novel hardware and software tools for movement and gait assessment. To optimally assess functional limitations in real-world conditions, we develop unobtrusive wearable sensor systems that can acquire movement and other physiological data in the clinic as well as in the patients’ home environment. The obtained clinical data in general and home monitoring data in particular are sensitive with regard to privacy and require new storage, handling, and access concepts. As a basis for safe and efficient data processing we investigate appropriate data management concepts and contribute to novel medical data infrastructures. From an analytical perspective, the efficient data processing of those large data sets requires sophisticated signal processing and machine learning tools to efficiently derive clinically relevant parameters, such as interpretable spatio-temporal parameters. This allows us to gain insight into disease related symptoms and mechanisms in order to provide feedback to patients, researchers and physicians regarding clinical interventions, treatment efficacy, or disease progression. Artificial intelligence tools are used to create decision support systems that may support clinicians to optimize and individualize treatments. By developing novel movement analysis algorithms based on signal processing and machine learning and by improving existing state-of-the-art methods we aim to shape a healthy digital future.

Group head:

More about the group

 

Sports Analytics

The Sports Analytics group applies different methods in the fields of Machine Learning, Wearables and Human-Computer Interaction to analyze and predict the performance of athletes. For gaining deeper insights into the behavior of athletes in specific sports like running, soccer or volleyball, we conduct in-the-wild and lab studies using inertial measurement units (IMUs), motion capture systems and extended realities. The group also utilizes extended realities to simulate training scenarios and applies them to various fields of application like therapy or performance improvement. Our research contributes to the development of more precise analysis tools in sports and thus makes the assessment and training of athletes more efficient. This can lead to an increase in performance, but also help to recognize harmful movement patterns for the prevention of injuries.

Research areas: Experimental Studies, Human-Computer Interaction, Machine Learning, Signal Processing, Wearables

Group head:

More about the group