AWRC Wearables
Background
This project is developing a digital health solution that integrates wearable technology with behaviour change techniques to support people with long-term health conditions in managing fatigue.
Using data from devices such as Fitbit and Apple, the project explores how activity, sleep, and physiological measures can be used to better understand and predict fatigue in daily life.
Partners and Funding
For this project which is being funded by the South Yorkshire Innovation Programme , we will be partnering with Sheffield Hallam University and more specifically the Advanced Wellbeing Research Centre (AWRC)
Outcomes
Through this project we (ELAROS) are looking to achieve 3 main outcomes :
- A machine learning model that predicts fatigue levels using wearable data.
- Wireframes and UI/UX concepts for how fatigue information can be displayed to users.
- A proof-of-principle prototype , providing the foundation for future clinical validation and funding applications.
Frequently Asked Questions
What do the wearables measure ?
Devices such as Fitbit and Apple Watch track activity levels , heart rate , sleep , and heart variability (HRV). These data points are used to understand and predict fatigue
How does the fatigue prediction work ?
A machine learning model analyses wearable data to classify fatigue into three levels (traffic light system) , helping users understand when they might need to rest or conserve energy
What stage is the project at ?
We are at the proof-of-principle stage. The model has been trained on real-world data , and initial prototypes for the user interface have been developed
What will happen next ?
Future work will focus on validating the model with larger datasets , refining the app interface with feedback from patients and clinicians , and preparing for clinical trials.