Predictive models

Stable Responsive EMG Sequence Prediction and Adaptive Reinforcement with Temporal Convolutional Networks

Author(s): Joseph L. Betthauser, John T. Krall, Shain G. Bannowsky, Gyorgy Levay, Rahul R. Kaliki, Matthew S. Fifer, Nitish V. Thakor
Stable Responsive EMG Sequence Prediction and Adaptive Reinforcement with Temporal Convolutional Networks 170 177 IEEE Transactions on Biomedical Engineering (TBME)
Movement prediction from EMG can be performed by compressing a short window of EMG into a feature-encoding that is meaningful for classification— an approach that can cause erratic prediction behavior. Temporal convolutional networks (TCN) leverage temporal information from EMG to achieve superior predictions for 3 simultaneous degrees-of-freedom that are more accurate and stable, have a very low response delay, and allow for novel types of interactive training. Addressing EMG decoding as a sequential prediction problem requires a new set of considerations that will lead to enhancements in the reliability, responsiveness, and movement complexity available from prosthesis control systems. read more
Screening for Cognitive Impairment by Model Assisted Cerebral Blood Flow Estimation

Screening for Cognitive Impairment by Model Assisted Cerebral Blood Flow Estimation

Author(s): Toni Lassila, Luigi Yuri Di Marco, Micaela Mitolo, Vicenzo Iaia, Giorgio Levedianos, Annalena Venneri, Alejandro F. Frangi
Screening for Cognitive Impairment by Model Assisted Cerebral Blood Flow Estimation 170 177 IEEE Transactions on Biomedical Engineering (TBME)

Alzheimer’s disease is a progressive and debilitating neurodegenerative disease; one in ten people aged 65 and older have it. As there is no cure for Alzheimer’s disease, early diagnosis is…

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