diff --git a/UseCases/SmartWatch_Healthcare/Smartwatch_Report.ipynb b/UseCases/SmartWatch_Healthcare/Smartwatch_Report.ipynb new file mode 100644 index 00000000..2209c434 --- /dev/null +++ b/UseCases/SmartWatch_Healthcare/Smartwatch_Report.ipynb @@ -0,0 +1,123 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c3ac6379-96d4-4aa8-b45f-da0d92996f44", + "metadata": {}, + "source": [ + "
\n", + "

\n", + " Stress Detection Using Smartwatch Data
\n", + " \"Teradata\"\n", + "

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" + ] + }, + { + "cell_type": "markdown", + "id": "d556bd95-c3d1-405c-8289-81c3d626e362", + "metadata": {}, + "source": [ + "

Introduction:

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\n", + "With rising stress levels globally, there's a growing need for scalable, continuous, and non-invasive stress monitoring solutions. Traditional methods like self-reported surveys and periodic clinical assessments are subjective, episodic, and not feasible for real-time monitoring. Smartwatches and other wearable devices are increasingly equipped with sensors capable of capturing physiological data, offering a powerful alternative. However, key challenges still remain:\n", + "

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\n", + "Proposed Solution:
\n", + "Smartwatch-Based Stress Detection Using AI/ML and Vantage Analytics
\n", + "Leverage data from widely available smartwatch sensors (e.g., ECG, BVP, Accelerometer) to detect stress using machine learning models. Extract block-wise statistical features using Vantage’s in-database processing capabilities to ensure scalable and parallelized analysis. Augment stress detection by integrating sleep stage classification, enabling holistic health insights. The end-to-end solution allows for identifying high-stress intervals, correlating them with sleep and activity behavior, and ultimately enabling personalized wellness interventions.\n", + "

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\n", + "Benefits:\n", + "

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\n" + ] + }, + { + "cell_type": "markdown", + "id": "ef27510d-45f1-46a9-ae97-ba0f89d64cb0", + "metadata": { + "tags": [] + }, + "source": [ + "

Dashboard & Insights:

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\n", + "This section showcases a visual summary of our Smartwatch Stress Detection analysis. The interactive dashboard provides insights into how stress (detected via physiological signals like heart rate and BVP) correlates with sleep behaviors such as deep sleep (SWS), insomnia, and restlessness.\n", + "

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\n", + "Key features of the dashboard:\n", + "

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Live Dashboard Link:\n", + "Smart Watch Dashboard\n", + "

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Dashboard Snapshot:

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Insights:

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\n", + "Together, these visuals provide compelling evidence that smartwatch data can reliably reflect real-world stress and sleep interactions, opening pathways for personalized health monitoring.\n", + "

\n" + ] + }, + { + "cell_type": "markdown", + "id": "c5c30f77-0da7-48c0-a8a9-e69b0c4b3b51", + "metadata": {}, + "source": [ + "" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/UseCases/SmartWatch_Healthcare/images/smartwatch.jpg b/UseCases/SmartWatch_Healthcare/images/smartwatch.jpg new file mode 100644 index 00000000..862025bf Binary files /dev/null and b/UseCases/SmartWatch_Healthcare/images/smartwatch.jpg differ