Connected Health (CoHe)

The Connected Health research group aims at providing health monitoring services and assisted independent living for senior citizens and patients.

With knowledge graphs, scientists have now the means to aggregate semantic information from various sources. The challenge is not only to extract meaningful information from this data, but to gain knowledge, to discover previously unknown insight, look for patterns, and to make sense of the data.

The research of the CoHe group focuses on:

Sensor data
processing
Pattern
detection
Abnormality
detection

The Kiduku Project

The Kiduku Project (Collaborating Research partners Fujitsu Laboratories Japan, Fujitsu Ireland, Casala DKIT and Insight@UCD) commenced July 2013 with an initiative to provide health monitoring services and assisted independent living for senior citizens and patients who live in smart houses in Ireland. This project uses approximately 110 ambient sensors in a residence, along with body wearable sensors, to collect a vast array of data relating to a person’s daily routine.

In the past, there was no easy way to extract data related to signs of health decline and risks such as abnormal motor functions from vast amounts of data in a way that was meaningful to medical practitioners; it had also been difficult to make detections suited to individual circumstances.

From the results of the first year’s research generated by this project, Fujitsu Laboratories and its partners have been able to develop technology to discover abnormalities that previously could have gone unnoticed by medical practitioners, by extracting “opened door” or “walked” that match an individual’s way of walking from sensing data, and observing whether the events happen simultaneously or sequentially. It is now possible to identify a change in behaviour that might indicate a decline in health, e.g. a patient who walks with a limp is prone to lose balance while walking and opening a door.

The project continues to research, widening its reach and looking at other conditions e.g. COPD.

Activities

  • Development of clinicians’ interface
  • Development of data collection tools and utilities
  • Run data collection tools and utilities during study assessments
  • Collection of multi modal sensor data from an elderly cohort
  • Visualisation of the processed sensor data using a clinicians’ interface