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Found 7 papers, 3 papers with code
Towards Automated and Marker-less Parkinson Disease Assessment: Predicting UPDRS Scores using Sit-stand videos
For BRADY we find F1-scores of 0. 75 using our framework compared to 0. 50 for the video based rater clinicians, while for PIGD we find 0. 78 for the framework and 0. 45 for the video based rater clinicians.
DeepActsNet: Spatial and Motion features from Face, Hands, and Body Combined with Convolutional and Graph Networks for Improved Action Recognition
Existing action recognition methods mainly focus on joint and bone information in human body skeleton data due to its robustness to complex backgrounds and dynamic characteristics of the environments.
SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience
First, we present a human pose based fall representation which is invariant to appearance characteristics.
Ensemble Knowledge Distillation for Learning Improved and Efficient Networks
Ensemble models comprising of deep Convolutional Neural Networks (CNN) have shown significant improvements in model generalization but at the cost of large computation and memory requirements.
Ranked #7 on
Knowledge Distillation
on ImageNet
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification
Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease.
Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmark
On that note, in this paper, we explore the application of machine learning algorithms for multi-class seizure type classification.
Densely Supervised Grasp Detector (DSGD)
At the pixel-level, DSGD uses a fully convolutional network and predicts a grasp and its confidence at every pixel.
