TY  - JOUR
A1  - Prakash, Subash
A1  - Unnikrishnan, Vishnu
A1  - Pryss, Rüdiger
A1  - Kraft, Robin
A1  - Schobel, Johannes
A1  - Hannemann, Ronny
A1  - Langguth, Berthold
A1  - Schlee, Winfried
A1  - Spiliopoulou, Myra
T1  - Interactive system for similarity-based inspection and assessment of the well-being of mHealth users
JF  - Entropy
N2  - Recent digitization technologies empower mHealth users to conveniently record their Ecological Momentary Assessments (EMA) through web applications, smartphones, and wearable devices. These recordings can help clinicians understand how the users' condition changes, but appropriate learning and visualization mechanisms are required for this purpose. We propose a web-based visual analytics tool, which processes clinical data as well as EMAs that were recorded through a mHealth application. The goals we pursue are (1) to predict the condition of the user in the near and the far future, while also identifying the clinical data that mostly contribute to EMA predictions, (2) to identify users with outlier EMA, and (3) to show to what extent the EMAs of a user are in line with or diverge from those users similar to him/her. We report our findings based on a pilot study on patient empowerment, involving tinnitus patients who recorded EMAs with the mHealth app TinnitusTips. To validate our method, we also derived synthetic data from the same pilot study. Based on this setting, results for different use cases are reported.
KW  - medical analytics
KW  - condition prediction
KW  - ecological momentary assessment
KW  - visual analytics
KW  - time series
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-252333
SN  - 1099-4300
VL  - 23
IS  - 12
ER  - 
TY  - JOUR
A1  - Kraft, Robin
A1  - Birk, Ferdinand
A1  - Reichert, Manfred
A1  - Deshpande, Aniruddha
A1  - Schlee, Winfried
A1  - Langguth, Berthold
A1  - Baumeister, Harald
A1  - Probst, Thomas
A1  - Spiliopoulou, Myra
A1  - Pryss, Rüdiger
T1  - Efficient processing of geospatial mHealth data using a scalable crowdsensing platform
JF  - Sensors
N2  - Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.
KW  - mHealth
KW  - crowdsensing
KW  - tinnitus
KW  - geospatial data
KW  - cloud-native
KW  - stream processing
KW  - scalability
KW  - architectural design
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-207826
SN  - 1424-8220
VL  - 20
IS  - 12
ER  -