This project is a close cooperation between:
- the Pattern Recognition Lab and
- the Department for Molecular Neurology of the University Hospital in Erlangen
Pattern Recognition Lab at the Department for Computer Sciences at FAU and the Department for Molecular Neurology of the University Hospital
The researchers in the Digital Sports group at the Pattern Recognition 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. Our motivation is generating a positive impact on human wellbeing, be it through increasing performance, maintaining health, improving rehabilitation, or monitoring disease.
Gait Analysis and Posturography
Parkinson`s disease (PD) is a chronic disorder of the central nervous system, characterized by degeneration of dopaminergic neurons leading to progressive gait dysfunction. To maintain patient’s quality of life, objective classification of gait symptoms in PD is crucial to adequately manage the individual treatment. We will establish a sensor based biometric gait-analysis that enables reproducible, objective, rater-independent assessment of gait symptoms. With a therapist independent rating completely comparable results can be reached. Different sensors (gyroscopes, accelerometers, in-sole pressure sensors,...) attached to a comfortable sport shoe detected motion signals assessed during standardized exercises while the subject is walking or sitting on a chair. Using pattern recognition methods, signal features should be analyzed from PD patients and healthy controls. Classification between patients and controls and a identification of different PD stages should be done. A pilot study suggests that biometric gait-analysis may be an important and complementary mean to support disease management in PD. Future biometric studies will help to monitor the disease course, to modify and adjust treatment thus rationalizing therapeutic decisions. To differentiate between PD specific and age dependent gait disorders also data from subjects in different decades of life should be analyzed.
Who are the people behind?
Prof. Dr. Björn Eskofier
„Be a part of the future of medicine: Predicting changes before they occur and Preventing serious harm to patients that way, Personalizing treatment and diagnosis to the individual rather than basing it on population averages, and letting the patient Participate in the management of his or her disease. This project adds another building block to this vision of the future of medicine, which is visualizing information from the home environment of the patient to the physician. We look forward to you contributions!"
Prof. Dr. Med. Jochen Klucken
„Digital Health equals technology meets medicine – if engineers can’t meet the clinicians’ requirements, we can’t solve patients’ needs in a digital healthcare future. Translational research requires communication and a joint understanding of the obstacles for healthcare technology solutions. Seeing is believing – doctors need to see what the patient did while they are not present. Successful telemedical homecare strategies require trust in the patient-therapist-machine interface. The project aims to visualize what the machine sees and you will help make this happen”