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AI clinical

Comment: Individuals have unique muscle activation signatures.

We used a machine learning approach to test the uniqueness and robustness of muscle activation patterns. Our results show that activation patterns not only vary between individuals, but are unique to each individual. Individual differences should, therefore, be considered relevant information for addressing fundamental questions about the control of movement.

Hug, F. et al. (2019). Individuals have unique muscle activation signatures as revealed during gait and pedaling. Journal of Applied Physiology.

Machine learning algorithms have been able to identify unique individuals based on their gait pattern for a while. Now we have this study showing that ML can identify individuals from their unique muscle activation patterns. For me the main takeaway is that technology has a level of insight into our bodies that is just going to keep getting better.

As much as we may think that our observations, palpation, and special tests give us useful information to integrate into patient management it’s not even close to the level of detail we can get from machines. I’m fairly convinced that pretty soon we’ll start seeing studies exploring what aspects of physiotherapy assessment are more accurate when conducted by algorithms.

See also, What AI means for the physical exam.

By Michael Rowe

I'm a lecturer in the Department of Physiotherapy at the University of the Western Cape in Cape Town, South Africa. I'm interested in technology, education and healthcare and look for places where these things meet.