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This research was inspired by the fact that almost everyone nowadays has a smartphone that they are constantly using, which makes our access to accessing valuable data to assess analyze and improve people's health an easy-to-achieve task using . With the multidisciplinary expertise in our team, we are developing and deploying state-of-the-art data analytics and machine learning .
Any additional information that can help show progress or movement
Purpose
Why are you doing the project? What do you aim to accomplish?
Benefits
How will the project be beneficial and to whom?
The possible applications of the outcome of your project
How
pipelines to provide tools for better mental health diagnostics.
Purpose
This project aims to make use of our smart devices to capture essential health-related data, analyze it, and provide the community with open-source tools for mental health diagnostics in specific, and human health in general.
Benefits
This project will set standards in smartphone sensor big data processing and analysis, and it will provide analytical tools for the correlation of human activity with mood and mental health.
In this era where all mankind is suffering from stress, anxiety, and mental instability, the introduction of such tools and analyses for the improvement of mental health is of great value to human beings in general, and more particularly for patients suffering from mental illnesses and their medical caregivers.
Once developed and implemented, the tools and analytical pipelines generated by this project may be embedded on edge-devices (e.g. smartphones, smart watches, microcontroller-based systems) for online mental health assessment, or linked to the healthcare facility's network for advanced EHR, or even on the cloud so that medical providers can monitor and diagnose treatment effectiveness over the web.
How
This project will be carried out as a series of processes: ETL → EDA → QA/QC → ML/AI model engineering → MLOps pipeline integration
To achieve the desired outcomes, advanced unsupervised analysis and anomaly detection algorithms will be implemented for QA/QC, machine learning (shallow and deep) models will be built, tested and tuned for optimal performance, and operation pipelines will be developed for system automation and integration in the infrastructure.How is the project being carried out? What methods are you using?
Further reading
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