April 22, 2023
 min read

Data Collection

Data collection performed using wearable sensors, third-party applications, and client applications.

Data collection begins with self-tracking and initial data collection.
This can be done using wearable sensors, third-party applications, and client applications.

Diagram showing data collection ranging from human and public data sources, ultimately ending up at the 30. Aggregation Layer.

Data Types

Ultimately, the system should be able to handle the following data types:

  • Omics data (e.g., genomics, proteomics, metabolomics, etc.)
  • Image and physiological data (e.g., CT, PET/SPECT, sMRI, fMRI, rMRI, DTI, EEG, MEG, ultrasound, cellular level imaging, multi-electrode recording, etc.)
  • Clinical data (e.g., lab tests, pathology, imaging, diagnosis, electronic health records, etc.)
  • Multiscale data (genomic, epigenomic, subcellular, cellular, network, organ, systems, organism, population levels)
  • Multiplatform data (desktop, cloud-based storage, etc.)
  • Data from multiple research areas and diseases (e.g., common inflammatory pathways in cancer, obesity, immune diseases, and neurodegenerative diseases)
  • Data with special considerations (e.g., sparse data, heterogeneous data, very large or very small datasets)
  • Human-computer interfaces and visualizations

Automated Data Acquisition

Currently, it requires a lot of effort and diligence on the part of the self-tracker to gather the data required to identify the triggers of illness and quantify the effectiveness of different treatments. Tracking one’s mood, diet, sleep, activity, and medication intake can be extremely time-consuming.
The Automated Data Acquisition automatically pulls data from a number of data sources (adding more all the time).

The data sources would include:

  • Biometric Devices: Measuring vital signs and biomarkers
  • Purchase Records: Data regarding consumption of foods and supplements are automatically collected by and inferred from receipts or other financial aggregation services like Mint.com.
  • Auditory Records: Voice recognition used to quantify emotion through conscious verbal expression, spectral analysis of the magnitudes of different frequencies of speech to better quantify unconscious human affect and thus providing more accurate data.
    CommonSense is a great example of a cloud-based platform for sensor data.
  • Visual Affect Data via Web-Cameras: By tracking hundreds of data points on the subjects’ faces, InSight can accurately capture emotional states.
  • Prescription Records: Microsoft HealthVault can automatically collect lab results, prescription history, and visit records from a growing list of labs, pharmacies, hospitals, and clinics.

Optomitron Data imports

My cat's breath smells like catfood.

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