Prof. Dr.-Ing. Bert Arnrich


Two Conference Papers Got Accepted in August!

We are happy to announce that two poster papers from our chair have been accepted at the Pervasive Health Conference in August 2020! Both publications are listed at the Pervasive Health Conference website and will be available here soon.

Will You Be My Quarantine: A Computer Vision and Inertial Sensor Based Home Exercise System

Justin Albert, Lin Zhou

The quarantine situation inflicted by the COVID-19 pandemic has left many people around the world isolated at home. Despite the large variety of mobile device-based self exercise tools for training plans, activity recognition or repetition counts, it remains challenging for an inexperienced person to perform fitness workouts or learn a new sport with the correct movements at home. As a proof of concept, a home exercise system has been developed in this contribution. The system takes computer vision and inertial sensor data recorded for the same type of exercise as two independent inputs, and processes the data from both sources into the same representations on the levels of raw inertial measurement unit (IMU) data and 3D movement trajectories. Moreover, a Key Performance Indicator (KPI) dashboard was developed for data import and visualization. The usability of the system was investigated with an example use case where the learner equipped with IMUs performed a kick movement and was able to compare it to that from a coach in the video. 

Self-prediction of Seizures in Drug Resistance Epilepsy Using Digital Phenotyping: A Concept Study

Sidratul Moontaha, Nico Steckhan

Drug-resistance is a prevalent condition in children and adult pa- tients with epilepsy. The quality of life of these patients is pro- foundly affected by the unpredictability of seizure occurrence. Some of these patients are capable of reporting self-prediction of their seizures by observing their affectivity. Some patients report no signs of feeling premonitory symptoms, prodromes, or aura. In this paper, we propose a concept study that will provide objective information to self-predict seizures for both the patient groups. We will develop a model using digital phenotyping which takes both ecological momentary assessment and data from sensor tech- nology into consideration. This method will be able to provide a feedback of their premonitory symptoms so that a pre-emptive therapy can be associated to reduce seizure frequency or eliminate seizure occurrence.