Regularized K-SVD based Dictionary Learning Approaches for PIR Sensor based Detection of Human Movement Direction

2020 ◽  
pp. 1-1
Author(s):  
Pubali De ◽  
Amitava Chatterjee ◽  
Anjan Rakshit
Keyword(s):  
Dictionary Learning ◽  
Human Movement ◽  
Movement Direction ◽  
Learning Approaches

scholarly journals Shift Invariant Dictionary Learning for Human Action Recognition

2019 ◽  
Vol 9 (2) ◽  
pp. 2107-2111
Keyword(s):  
Sparse Representation ◽  
Action Recognition ◽  
Dictionary Learning ◽  
Human Action Recognition ◽  
Human Action ◽  
Signal Denoising ◽  
Learning Approaches ◽  
Training Time ◽  
Initial Stage ◽  
Shift Invariance

Sparse representation is an emerging topic among researchers. The method to represent the huge volume of dense data as sparse data is much needed for various fields such as classification, compression and signal denoising. The base of the sparse representation is dictionary learning. In most of the dictionary learning approaches, the dictionary is learnt based on the input training signals which consumes more time. To solve this issue, the shift-invariant dictionary is used for action recognition in this work. Shift-Invariant Dictionary (SID) is that the dictionary is constructed in the initial stage with shift-invariance of initial atoms. The advantage of the proposed SID based action recognition method is that it requires minimum training time and achieves highest accuracy.

scholarly journals Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease

2021 ◽  
Author(s):  
Timon Merk ◽  
Victoria Peterson ◽  
Witold Lipski ◽  
Benjamin Blankertz ◽  
Robert S. Turner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Local Field ◽  
Motor Impairment ◽  
Local Field Potentials ◽  
Human Movement ◽  
Learning Approaches ◽  
Field Potentials ◽  
Neural Implants

SummarySmart brain implants will revolutionize neurotechnology for improving the quality of life in patients with brain disorders. The treatment of Parkinson’s disease (PD) with neural implants for deep brain stimulation (DBS) presents an avenue for developing machine-learning based individualized treatments to refine human motor control. We developed an optimized movement decoding approach to predict grip-force based on sensorimotor electrocorticography (ECoG) and subthalamic local field potentials in PD patients undergoing DBS neurosurgery. ECoG combined with Bayesian optimized extreme gradient boosted decision trees outperformed multiple state of the art machine learning approaches. We further developed a whole brain connectomics approach to predict decoding performance in invasive neurophysiology, relevant for connectomic targeting of distributed brain networks for neural decoding. PD motor impairment deteriorated decoding performance, suggestive of a role for dopamine in human movement coding capacity. Our study provides an advanced neurophysiological and computational framework to aid development of intelligent adaptive DBS.

Sign in / Sign up

Export Citation Format

Share Document