However, UPDRS is an onsite physical examination method that offers only a glimpse of the tremors that patients experience in their everyday activities.
FAU department of computer and electrical engineering and computer science assistant professor Behnaz Ghoraani said: “A single, clinical examination in a doctor’s office often fails to capture a patient’s complete continuum of tremors in his or her routine daily life.
“Wearable sensors, combined with machine-learning algorithms, can be used at home or elsewhere to estimate a patient’s severity rating of tremors based on the way that it manifests itself in movement patterns.”
Researchers have investigated the application of two machine-learning algorithms, gradient tree boosting and LSTM-based deep learning.
Results from the study showed that the gradient tree boosting method estimated total tremor and resting tremor sub-score with high accuracy and in majority cases with the same results determined through the UPDRS.
Patients also experienced a decline in tremors after taking medication, even in cases where results did not match with the total tremor sub-scores recorded through the UPDRS assessments.
Out of all existing UPDRS task-dependent methods and task-independent tremor estimation methods, this process is claimed to have provided the highest performance results.
Meanwhile, in a separate development, Medtronic has launched its advanced Patient Programmer technology for Deep Brain Stimulation (DBS) therapy at the Samsung Developers Conference in San Jose, California.
https://www.medicaldevice-network.com/news/algorithms-parkinsons-patients/
No comments:
Post a Comment