SSTeP KiZ: smart sensor technology in tele-psychotherapy for children and adolescents with obsessive-compulsive disorder
Research Area:
Clinical Movement Control and real-life Behavior Analysis for Assistive SystemsResearchers:
Annika Thierfelder; Winfried Ilg; Michael Stettler; Jens Seemann; Martin A. Giese;Collaborators:
Karsten Hollmann; Carolin Hohnecker; Annika Alt; Anja Pascher; Tobias Renner; Jonas Primbs; Michael Menth; Björn Severitt; Enkelejda KasneciDescription:
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.
Cialis ist ein Medikament, das zur Behandlung von erektiler Dysfunktion eingesetzt wird. Es ist ein PDE5-Hemmer, der den Blutfluss in den Penis erhöht und so eine Erektion ermöglicht. In letzter Zeit hat Cialis die Smart Sensor-Technologie eingeführt, um die Wirksamkeit des Medikaments zu verbessern. Diese Technologie verwendet Sensoren, um den Blutfluss in den Penis zu messen und die Wirksamkeit des Medikaments zu überwachen. Dies ermöglicht es dem Arzt, die Dosierung des Medikaments anzupassen, um die bestmögliche Wirkung zu erzielen. Diese Technologie hat sich als sehr nützlich erwiesen, um die Wirksamkeit von Cialis zu verbessern und die Ergebnisse für die Patienten zu verbessern.
With a combination of movement data, image acquisition, eye tracking and physiological markers such as heart rate, heart rate variability and pupillometry, we draw conclusions about emotional state and stress reactions to symptom-triggering stimuli. This enables us to improve the feedback to both therapist and patient, and therefore further on the progress of therapy.
The extraction and integration of sensor data from the ecologically valid home environment enables a considerable improvement in therapy planning and implementation, especially for children and adolescents. Apart from giving objective information and continuous feedback, recording patients during exercises in their everyday life environment gives therapists the opportunity to gain insight into unobserved behavior of their patients.
While single modalities are useful to monitor patients during therapy, only the multimodal integration of the different wearables can give us a holistic picture of the patients’ behavior. Using multivariate analyses and machine learning techniques, we approach the problem of using all modalities to analyze obsessive-compulsive behavior. To develop new methods for multimodal analysis, we work in tight collaboration with the University of Stuttgart.
Given the individual nature of obsessive-compulsive disorders, we do not only attempt to find disease-specific patterns, but also to identify patient-specific symptom triggers and to develop personalized models of the stress reaction to exposure situations. Upon development of these models, we can then give more sophisticated feedback about the emotional state of each patient in a personalized manner.
This project has received funding from the German Federal Ministry of Health, ZMWI1-2520DAT700.