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Machine Learning and Data Analytics

The researchers in the Machine Learning and Data Analytics (MaD) lab conduct theoretical and applied research for wearable computing systems and machine learning algorithms for engineering applications at the intersection of sports and health care.

Projects

Walking is a key element of human mobility and independence. For persons aged 70 or above, the number of falls per year increases drastically. Physiological consequences are bone fractures, traumas or death. In conjunction with psychological consequences, such as post-fall anxiety, falls lead to a decreased quality of life. Most falls could be prevented if an early detection of fall risk was available, thus maintaining a high quality of life.

This project will focus on assessing gait in geriatric…

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In this project, we investigate musculoskeletal modeling and simulation to analyze and understand human movement and performance. Our objective is to reconstruct human motion from measurement data for example for medical assessments or to predict human responses for virtual product development.

 

Reconstruction of Human Motion: Biomechanical analysis using wearable systems

Inertial sensor systems provide the possibility of cheap gait analysis in everyday life.  One…

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The aim of this project is the development of a digital, visual perceptual learning system (D-VPL) with gesture recognition and telemedical link to ophthalmologists for visually impaired elderly people, dementia prevention, and patients with traumatic brain injury. The users will react by using gestures to moving objects that will be presented in virtual reality or on a 3-D display. The combination of D-VPL and gesture control leads to a dual task training, and the telemedical link enables applications…

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Autonomous applied logistics, especially in context of Industry 4.0, is an important factor for fully automated systems. These systems involve autonomous operation of loading and unloading processes, safety measures by detecting persons in danger zones and the general optimization of the logistical processes.

Especially, the application of these systems in a harbor environment, where different systems from all over the world interact, increases the complexity of the loading and unloading processes. The aim of this research project is to determine the feasibility of automating the unloading process by segmenting, classifying and fitting laser range data by using Machine Learning techniques.

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HOOP is an mHealth platorm for Parkinson's disease patients' training and rehabilitation, based on music and haptic stimulation, to be used in the patient’s home employing a set of sensors and exercises to evaluate the performance. HOOP will become a commercial product which will enable Parkinson's patients' rehabilitation at home in a cost-effective basis. It is based on the use of acoustic and haptic stimulation techniques during the performance of motor (upper and lower limbs) and nonmotor exercises (cognitive tests). According to the high versatility of the product, HOOP will create a sustainable business plan which will introduce into several target markets, including but not limited to Parkinson’s patients.

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The project ‚Schlafkur mit Begleitanalyse‘ aims to close a major gap in the market and offer citizens with sleep difficulties an accessible, effective and digitally supported solution. According to the Health Report 2017 of the DAK Gesundheit, 80% of the working population in Germany sleep poorly. One in ten workers suffer from sleep disorders, thus affecting over 1.5 million people in Bavaria.

We scientifically evaluate the combination of intelligent sleep intervention technology for sleep support, an intelligent sensor for sleep diagnostics and an individualized, IT-based intervention program for each participant with sleep problems. We plan to offer an efficient, low cost service to improve sleep as part of the digital health care industry in Bavaria.

The target group is mainly those in the 30-65 year-old age range of working persons suffering from non-organic sleep disorders. Because there are many types of non-organic sleep disorders: from severe, long-term insomnia to sleep disorders during menopause to mild, recurring problems with falling asleep or remaining so. Usually, these symptoms are independent of a true sleep disorder. Despite a considerable number of affected people, there is a lack of appropriate (self-) analysis and insight, due, in part, to poor social recognition of sleep disorders as well as a lack of diagnosis of the widespread disease "insomnia". This means that the mild to moderate limitations of the affected people do not have access to the appropriate medical or psychological treatment. This project now addresses this population aiming to improve the quality of life and to prevent chronic sleep disorders in the medium term; we provide the patient with individual and professional coaching to recover a restful and peaceful sleep.

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Objective health data about subjects outside of the laboratory is important in order to analyse symptoms that cannot be reproduced in the laboratory. A simple daily life example would be how stride length changes with tiredness or stress. In order to investigate this we must be able to accurately segment a stride from daily living data in order to have an accurate measure of duration and distance. State-of-the-art methods use separate segmentation and classification approaches. This is inaccurate for segmentation of an isolated activity, especially one that is not repeated. This could be solved using a model that is based on the sequence of phases within activities. Such a model is a graphical model. Currently we are working with Conditional Random Fields and Hierarchical Hidden Markov Models on daily living data. The applications will include sports as well as daily living activities.

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