Wearable Tech And AI To Predict Health Problem Onset
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Posted on May 16, 2018, 8 p.m.
Research team from the University of Waterloo have found that application of artificial intelligence to combination of data retrieved from wearable technology may detect failing health; data from these wearable devices and AI, Hexoskin, which assesses changes in aerobic responses could predict onset of respiratory or cardiovascular disease, as published in the Journal of Applied Physiology.
Onset of chronic diseases such as chronic obstructive pulmonary disease and type 2 diabetes has direct impact on aerobic fitness, it is believed that in the very near future it may be possible to continuously check health before realizing medical help is needed. Research has found a method to process biological signals and generate single number to track fitness.
Healthy men in their twenties were monitored for four days by wearing a shirt that had sensors incorporated into it for breathing, acceleration, and heart rate. Readings were compared with lab responses and it was found that it was possible to accurately predict health related benchmarks during daily activities using the smart shirt called Hexoskin.
This is a great example of a multi-disciplinary research of how AI can be a game changer in healthcare by turning data into predictive knowledge to help professionals gain better understandings of individual’s health. The team plans to test Hexoskin on mixed age groups and genders, as well as individuals with health issues to further investigate how wearing it may gauge whether their health is failing.
Materials provided by University of Waterloo.
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Journal Reference:
Thomas Beltrame, Robert Amelard, Alexander Wong, Richard L. Hughson. Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models. Journal of Applied Physiology, 2018; 124 (2): 473 DOI: 10.1152/japplphysiol.00299.2017
