openCHA and interaction with wearable data.
This is an example of how the hashtagopenCHA framework enables the integration of patients’ data with hashtagLLMs (via agents) to provide personalized responses. In this scenario, CHA has access to one-year data from multiple users, collected in 2020 via ŌURA rings. The users were asked to wear the rings continuously.
In this example, three agents (i.e., tasks) were defined and connected to the Planner to 1) interact with local storage, 2) retrieve sleep data, and 3) perform basic analysis.
The next questions delve deeper into the sleep parameters, querying if the parameters are enough. The responses provided are personalized, comparing the patient’s data against typical sleep norms. CHA utilizes both personalized data and general knowledge about sleep parameters to generate these responses.
Stay tuned for more updates and examples.
