This master thesis will be performed with the Innovative Digital Endpoint Analytics (IDEA) group at Novartis in Basel. The IDEA group is working across different therapeutics areas to develop novel analytical approaches for next-generation mobile medical device data. In particular, it helps the clinical teams to leverage device-derived data within their studies with the goal to show the efficacy of Novartis drugs in proof-of-concept (POC or Phase 2b) clinical trials.

The aim of this master thesis is to develop and validate a device-agnostic web tool (DDD, preferably in R Shiny) for

• ingestion (read raw- and pre-processed data, labels and patient reported outcomes),
• integration (combine multimodal streams into one data structure),
• data annotation (offer the opportunity to manually label data),
• preparation and analysis (make the data “machine-learnable”), and
• visualization (show daily or monthly summaries of interesting features), of clinical trial data.

In the second part of the thesis the student with focus on multimodal human activity recognition. Different methods from literature will be evaluated, including recent approaches using deep learning. The evaluation should result in recommendations on sensor placement, which sensors need to be included and how much data has to be collected in order to get acceptable results.

The data needed to develop DDD has already been collected. For the human activity recognition part the student will have the opportunity to collect his own data from wearable sensors available in the lab.