Clinical Movement Control and real-life Behavior Analysis for Assistive Systems
Description
This research area addresses the theoretical and experimental understanding of motor and balance dysfunction and the effect of disorders on patients in their daily activities. We develop a wide range of multi-modal assistive tools to support people with neurological movement disorders and psychological disorders. We address the preclinical and clinical phases of various neurological disorders, including Cerebellar Ataxia, Hereditary Spastic Paraplegia, Parkinson’s disease, and Apraxia. Furthermore, multi-modal systems are used to improve therapeutic interventions for mentally ill subjects, e.g., obsessive-compulsive disorders.
Current Projects
SSTeP KiZ: smart sensor technology in tele-psychotherapy for children and adolescents with obsessive-compulsive disorder
With sensors that can be worn in everyday life and an intelligent analysis of multi-modal sensor data, SSTeP KiZ aims to significantly improve the treatment options for patients with obsessive-compulsive disorder. We support telemedical treatment of affected children and adolescents in their home environment by integrating data collected with wearables.
Detecting and Quantifying Ataxia-Related Motor Impairments in Rodents Using Markerless Motion Tracking With Deep Neural Networks
Animal models of adult-onset neurodegenerative diseases have significantly enhanced the understanding of the molecular (patho-)mechanisms and have offered enormous potential for therapeutic target evaluation in many neurodegenerative diseases.
Gait in hereditary spastic paraplegia – from axonal degeneration to movement disorder
In Hereditary Spastic Paraplegia (HSP) type 4 (SPG4 / SPAST) a length-dependent axonal degeneration in the cortico-spinal tract leads to progressing symptoms of hyperreflexia, muscle weakness, and spasticity of lower extremities. The therapeutical potential for future intervention is likely most promising in the early stages of HSP. Therefore, it is crucial to identify and quantify first changes already in the prodromal phase of HSP patients.
Real-life gait assessment in degenerative cerebellar ataxia: Towards ecologically valid biomarkers
In order to establish ecologically valid biomarkers evaluating treatment-responses really in the patients’ everyday life, we develop multi-variate measures of ataxic gait using wearable sensors, which demonstrate high sensitivity to small differences in disease severity in real-life walking.
Finished Projects
The influence of focal cerebellar lesions on the coordination in walking
In this study we examined patients with focal cerebellar lesions in order to investigate the influence of different regions of the cerebellum on the performance in a working memory task (n-back task), as well as on gait variability and gait stability during dual task walking.
Smart sensor technology in telepsychotherapy for children and adolescents SStep-KiZ
Through the use of sensors that can be worn in everyday life and an intelligent analysis of multi-modal sensor data, SSTeP-KiZ aims to significantly improve the treatment options for mentally ill children and adolescents with obsessive-compulsive disorders.
Rehabilitation training exploiting physiotherapy, computer games and biofeedback
The symptoms o movement disorders, such as cerebellar ataxia or Parkinon’s disease, can be partially improved y motor training. We have shown that (opposed to the classical view) physiotherapy results in substantial and enduring benefits for patients with cerebellar ataxia, if such training is continuously administered. We exploit biofeedback and computer games to improve such training.
Quantification of subtle motor changes in preclinical stages of neurodegenerative diseases
Movement disorders such as cerebellar ataxia or Parkinon’s disease result in subtle degradations of motor behavior already long time before the become clinically manifest. Using motion capture technology and machine learning, we try to identify such subtle preclinical motor symptoms.
Motor learning and the functional role of the cerebellum
The cerebellum plays an essential role in motor learning. Combining psychophysics and neuropsychological studies in patients we investigate different types of motor learning mechanisms and the role of the cerebellum.
Publications
Beichert, L., Seemann, J., Kessler, C., Traschütz, A., Müller, D., Dillmann-Jehn, K. et al. (2024). Towards patient-relevant, trial-ready digital motor outcomes for SPG7: a cross-sectional prospective multi-center study (PROSPAX). MedRxiv preprint. [More]
Pellerin, D., Seemann, J., Traschütz, A., Brais, B., Ilg, W. & Synofzik, M. (2024). Reduced Age-Dependent Penetrance of a Large FGF14 GAA Repeat Expansion in a 74-Year-Old Woman from a German Family with SCA27B. Movement Disorders, n/a(n/a). [More]
Beichert, L., Ilg, W., Kessler, C., Traschütz, A., Reich, S., Santorelli, F. M. et al. (2024). Digital gait outcomes for ARSACS: discriminative, convergent and ecological validity in a multi-center study (PROSPAX) accepted for Movement Disorders. IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics. [More]
Casas, J. P., Wochner, I., Schumacher, P., Ilg, W., Giese, M. A., Maufroy, C. et al. (2024). Generating Realistic Arm Movements in Reinforcement Learning: A Quantitative Comparison of Reward Terms and Task Requirements. IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics. [More]
Sapounaki, M., Schumacher, P., Ilg, W., Giese, M. A., Maufroy, C., Bulling, A. et al. (2024). Quantifying human upper limb stiffness responses based on a computationally efficient neuromusculoskeletal arm model. IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics. [More]
Seemann, J., Daghsen, L., Cazier, M., Lamy, J.-C., Welter, M.-L., Giese, M. A. et al. (2024). Digital gait measures capture 1-year progression in early-stage spinocerebellar ataxia type 2. Movement disorders : official journal of the Movement Disorder Society. [More]
Christensen, A., Taubert, N., in ’t Veld, E. M., de Gelder, B. & Giese, M. A. (2024). Perceptual encoding of emotions in interactive bodily expressions. iScience. VOLUME 27, ISSUE 1, 108548, JANUARY 19, 2024. [More]
Nemeth, A., Antoniades, C., Dukart, J., Minnerop, M., Rentz, C., Schuman, B.-J. et al. (2023). Using Smartphone Sensors for Ataxia Trials: Consensus Guidance by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers. The Cerebellum, 1-12. [More]