NEWS

Imagine a health companion that has the capability to analyze your health data and tailor recommendations specifically for you!

As part of a Hackathon at the Workshop on Interactive AI Systems for Digital Therapeutics, Champalimaud Foundation, we were working on a project focused on developing a real-time photoplethysmography (PPG)-based stress estimation and recommendation agent. To accomplish this goal, we integrated openCHA with a PsychoBIT Kit equipped with a PPG sensor.

The openCHA framework allows LLMs to connect with external data sources, enabling the collection of information and the creation of customized responses.
In our project, the integration was done by developing the following tasks and connecting them to openCHA.

1.  A Python code to get the latest PPG data (captured by the PsychoBIT kit) stored on our laptop.
2. A Python function designed for PPG analysis, capable of extracting cardiac cycles and calculating the Root Mean Square of Successive Differences (RMSSD).
3. A guideline for interpreting RMSSD values to assess stress levels, categorized into normal, high, and very high.
4. An Internet search tool to retrieve stress-related information from online resources.

Our chatbot successfully interfaced with the data gathered using the PsychoBIT Kit, analytical tools, and online resources, providing insights into our stress levels and delivering tailored recommendations (see images below). Despite the project being developed during a single-day hackathon, we demonstrated the capabilities of openCHA and the potential of harnessing data from wearable devices to create personalized and context-sensitive recommendation systems.

• Abbasian, M., Azimi, I., Rahmani, A.M. and Jain, R., 2023. Conversational health agents: A personalized LLM-powered agent framework. arXiv preprint arXiv:2310.02374
• GitHub repository
• User guide and quick start