Smart devices and smart data processing
Background
Smart devices have become an integral part of modern life, driving innovation across industries. These devices range from everyday items like smartphones and wearable fitness trackers to more complex systems like smart home appliances and industrial IoT sensors. With their ability to connect, collect, and transmit data, they have transformed how we interact with technology and data systems. The real power of smart devices comes from their ability to process vast amounts of data in real-time. This data, often referred to as "smart data," is essential for optimizing operations, predicting behaviors, and enhancing user experiences. Smart data processing uses advanced algorithms and machine learning models to extract actionable insights from the raw data generated by smart devices.
?
At DSENS, we work on novel algorithms to handle data from existing smart devices to make analyses more robust and enable more far-reaching statements, e.g. by sensor data fusion. In addition, together with partners DSENS designs, implements and characterizes novel devices and sensors to capture physiological data.
?
Potential topics (student works)
Possible topics include (but are not limited to) the following aspects
- Development of algorithms to predict future health outcomes based on patient data
- Investigation on the usability of radar sensors for unobtrusive health assessment
- Development of algorithms for robust processing of wearable photoplethysmographic data or impedance data, e.g. by sensor data fusion or deep networks
- Automation of preparation and cleaning of health-related datasets to improve the quality and efficiency of further analysis and decision-making
- Design, implementation and characterization of multimodal sensor units to capture physiological parameters
Note that an individual adjustment is done based on students’ interest and the work to be accomplished (seminar works cover literature research and preparation of the state of the art; project modules, practical modules and theses cover own implementations as well).
?
Further reading
P. H. Charlton et al, “The 2023 wearable photoplethysmography roadmap,” Physiol. Meas., vol. 27, no. xxxx, pp. 0–31, Jul. 2023.
?
G. J. Williams, et al., “Wearable technology and the cardiovascular system: the future of patient assessment,” Lancet Digit. Heal., vol. 5, no. 7, pp. e467–e476, 2023.
?