ALPS: A Web Platform for Analysing Multimodal Sensor Data in the Context of Digital Health

The Internet of Things (IoT) enables us to record a vast amount of information about activities, the environment and the physiological state of a person. In particular, wearables promise the development of new methods for prevention and treatment of diseases. Clinical studies often involve multiple devices from different manufacturers, which make use of different data formats and usually offer no way to synchronize them. Additionally, existing analysis tools are often tailored to a very specific use case. Thus, professionals working with data collection and analysis execute a lot of manual work to gather and combine the recorded data. This paper presents ALPS, an extensible web platform with an integrated event-based synchronization method that enables researchers with clinical and engineering background to analyze multimodal sensor data. Plug-ins for new devices, filtering and analysis methods allow the customization for different research scenarios. A case study on Heart Rate Variability (HRV) shows that the platform simplifies the comparative analysis of multiple signals and supports the exploration of data from different wearables.