IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
39302781
Muscle fatigue impacts upper extremity function but is often overlooked in biomechanical models. The present work leveraged a transfer learning approach to improve torque predictions during fatiguing upper extremity movements. We developed two artifi...
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
39150815
Electroencephalogram (EEG) signals play an important role in brain-computer interface (BCI) applications. Recent studies have utilized transfer learning to assist the learning task in the new subject, i.e., target domain, by leveraging beneficial inf...
Robotic assistance can improve the learning of complex motor skills. However, the assistance designed and used up to now mainly guides motor commands for trajectory learning, not dynamics learning. The present study explored how a complex motor skill...
Journal of neuroengineering and rehabilitation
38867287
BACKGROUND: In-home rehabilitation systems are a promising, potential alternative to conventional therapy for stroke survivors. Unfortunately, physiological differences between participants and sensor displacement in wearable sensors pose a significa...
Neural networks : the official journal of the International Neural Network Society
39208459
Utilizing large-scale pretrained models is a well-known strategy to enhance performance on various target tasks. It is typically achieved through fine-tuning pretrained models on target tasks. However, naï ve fine-tuning may not fully leverage knowle...
Neural networks : the official journal of the International Neural Network Society
39159536
Parameter efficient transfer learning (PETL) methods provide an efficient alternative for fine-tuning. However, typical PETL methods inject the same structures to all Pre-trained Language Model (PLM) layers and only use the final hidden states for do...
Neural networks : the official journal of the International Neural Network Society
39657530
In practice, collecting auxiliary labeled data with same feature space from multiple domains is difficult. Thus, we focus on the heterogeneous transfer learning to address the problem of insufficient sample sizes in neuroimaging. Viewing subjects, ti...
OBJECTIVE: To develop a machine learning (ML) model utilizing transfer learning (TL) techniques to predict hypertension in children and adolescents across South America.
This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used...
Neural networks : the official journal of the International Neural Network Society
40127577
Although deep transfer learning has made significant progress, its "black-box" nature and unstable feature adaptation remain key obstacles. This study proposes a multi-stage deep transfer learning method, called XDTL, which combines explainable featu...