Seiter, Julia

Julia Seiter

ETH Zürich
Julia Seiter
Institut f. Elektronik
ETZ H 97
Gloriastrasse 35
8092 Zürich

Phone: +41 44 632 27 44
Fax: +41 44 632 12 10

Julia studied Electrical Engineering and Information Technology at Karlsruhe Institute of Technology (KIT) in Germany. In 2008 she absolved an internship at Continental Automotive in Penang, Malaysia. During her studies she worked as teacher assistant in higher mathematics and linear electric networks and was a research assistant at the department of theoretical electrical engeneering and system optimization. In 2011 she completed her studies with a master thesis on the topic 'GPS integration for a pedestrian navigation system in urban environment'. Julia joined the Wearable Computing Group in June 2011

Research interests

Research Projects



Context awareness has the potential to revolutionise the way people interact with information technology. Whereas conventional computers merely interpret explicit user input, context-aware systems analyse and automatically respond upon users’ behaviour and situation he/she is in. This enables electronic systems to assist users in situations in which the use of conventional computers and mobile devices is out of question. Research on context awareness has continued to intensify in the last decade due to the availability of cheap sensing technologies and mobile systems. Still, building reliable context-aware systems that can deal with complex real-life situations and environments remains an open research challenge and requires a multi-disciplinary effort.
iCareNet focuses on the areas of healthcare, wellness, and assisted living (HWA) applications to make a decisive contribution towards solutions, leveraged through an interdisciplinary perspective ranging from sensing and sensor integration, to human-computer interaction and social factors involved in the deployment of context-aware applications.

Long-term, comprehensive monitoring of daily routine for assisted living applications: We contribute to iCareNet by developing an ambient-wearable ubiquitous context-aware sensor system that infers activity routines in daily life. Tracking daily routine of rehabilitation patients in their home environment can provide valuable progress information for therapists and patients and contribute to an optimized rehabilitation process. We work on discovery methods such as topic models to infer daily routine patterns from sensor data without prior model learning. We develop smart sensor systems, wearable sensors and smartphone applications for patient monitoring.

Topic models originate from text mining and are used to discover hidden themes from word statistics in a corpus of documents. We investigate the application of topic models to activity routine discovery from sensor data. We focus on the development of discovery methods and evaluate our approach in a simulated environment as well as in real-world studies. In particular, we monitored hemiparetic rehabilitation patients in a day care rehabilitation center using wearable motion sensors. Patient daily routine patterns such as lunch, socialising, rest and cognitive training were successfully inferred from sensor data using topic models.


I was assistant for the lecture Digitaltechnik taught by Prof. Tröster during HS11 and HS12.


2015 2014 2013 2012


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