Scientists from the New York University and University of Western Ontario have created an AI algorithm for enhancing robotic solutions like exoskeletons to stop pathological hand tremors, associated with neurodegenerative diseases, like Parkinson’s disease.

In the article “PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models” published in Scientific Reports this February, researchers have reported that they have created a “deep recurrent model” that predicts and eliminates tremors:

“The PHTNet is the first intelligent systems model developed on this scale for PHT [Pathological Hand Tremor] elimination that maximizes the resolution of estimation and allows for prediction of future and upcoming sub-movements”

Pathological hand tremor severely affect patient quality of life, but AI-powered advanced model is “already at the ready-to-use stage, available to neurologists, researchers, and assistive technology developers”, shared co-author Farokh Atashzar. 

Since now the model “requires substantial computational power” the team is planning to apply cloud-computing approach that would make devices ready to be used at patients homes, added Atashzar:

“We also hope to develop models that require less computational power and add other biological factors to the inputs”

As Future Time previously reported, Canadian researchers have also started to test an AI system that predicts the progression of neurodegenerative diseases, like Parkinson’s disease.

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