The Wearables for Health (W4H) Integrated Toolkit Repository serves as a centralized hub for managing, analyzing, and visualizing wearable data in a seamless and efficient manner. This repository hosts a suite of open-source tools and frameworks meticulously engineered to facilitate end-to-end processing of wearable data, ranging from data ingestion from various sources to real-time and offline analytics, all the way to insightful visualization. At the core of the toolkit lies the novel Geospatial Multivariate Time Series (GeoMTS) abstraction, which enables streamlined management and analysis of wearable data. The repository encompasses key components such as StreamSim for simulating real-time data streaming, W4H ImportHub for integrating offline datasets, pyGarminAPI and pyFitbitAPI for efficient interaction with popular wearable device APIs, and an Integrated Analytics Dashboard for effective data extraction, presentation, and analysis. A video demonstration is available at: https://youtu.be/67a8kuMjSAU.

This toolkit comprises the following tools, each tailored to address specific needs in the wearable data lifecycle:

• StreamSim: A real-time data streaming simulator tool for tabular data, aiding in testing real-time applications.

• W4H ImportHub: Your gateway for seamlessly integrating stored datasets like CSV files into the W4H platform.

• pyGarminAPI: A Python library to interact with the Garmin API, facilitating the retrieval of activity and health data from Garmin devices.

• Integrated Analytics Dashboard: Core component for GeoMTS data extraction, presentation, and analysis, supporting both real-time and offline analytics on GeoMTS data. (Source code included in this repo)

• Approximate Aggregate Queries on GeoMTS: A PostgreSQL extension fft_approximate for quickly answering aggregate range queries on time series data using an approximate approach.

Ensure you have python and pip installed on your machine.

1. Install Required Packages: First, navigate to the project directory and install the necessary packages using pip:

   pip install -r requirements.txt

2. Configure Database Properties: Edit the conf/db_config.yaml to set up your own database with your desired properties and credentials. We defined a local instance by default. If you don't user docker compose for running W4H and Postgres, replace host_name='local' to access the database server.

3. Stream Simulation: To start the stream simulation service, run the following command:

   python stream_sim.py

4. Start the Dashboard: After the stream simulation service is up and running, initiate the dashboard using streamlit:

   streamlit run viz.py

   Once the dashboard is started, you can access it via the URL provided by streamlit in your terminal.

docker has been installed in you server. run:

docker

to check.

Run:

docker run -dp 8501:8501 uscimsc/w4h:latest 

Then access http://{your_server_ip}:8501/ to see the dashboard.

Make sure to have docker and docker compose installed on your system. Then, run:

docker compose up --build

You should be able to access the dashboard and database server through http://{your_server_ip}:8501.