AIMC Topic: Humans

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A Pragmatic Machine Learning Model To Predict Carbapenem Resistance.

Antimicrobial agents and chemotherapy
Infection caused by carbapenem-resistant (CR) organisms is a rising problem in the United States. While the risk factors for antibiotic resistance are well known, there remains a large need for the early identification of antibiotic-resistant infecti...

Deep learning-based photoplethysmography classification for peripheral arterial disease detection: a proof-of-concept study.

Physiological measurement
A proof-of-concept study to assess the potential of a deep learning (DL) based photoplethysmography PPG ('DLPPG') classification method to detect peripheral arterial disease (PAD) using toe PPG signals.PPG spectrogram images derived from our previous...

Multispectral co-occurrence of wavelet coefficients for malignancy assessment of brain tumors.

PloS one
Brain tumor is not most common, but truculent type of cancer. Therefore, correct prediction of its aggressiveness nature at an early stage would influence the treatment strategy. Although several diagnostic methods based on different modalities exist...

Deep-Learning-Based Emergency Stop Prediction for Robotic Lower-Limb Rehabilitation Training Systems.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Robotic lower-limb rehabilitation training is a better alternative for the physical training efforts of a therapist due to advantages, such as intensive repetitive motions, economical therapy, and quantitative assessment of the level of motor recover...

Short Time Delay Does Not Hinder Haptic Communication Benefits.

IEEE transactions on haptics
Haptic communication, the exchange of force and tactile information during dancing or moving a table together, has been shown to benefit the performance of human partners. Similarly, it could also be used to improve the performance of robots working ...

Quality Enhancement of Compressed Vibrotactile Signals Using Recurrent Neural Networks and Residual Learning.

IEEE transactions on haptics
We present a neural network-based compression artifact removal technique for vibrotactile signals. The proposed decoder-side quality enhancement approach is based on recurrent neural networks (RNNs) and the principle of residual learning. We use a to...

Flexible Assimilation of Human's Target for Versatile Human-Robot Physical Interaction.

IEEE transactions on haptics
Recent studies on the physical interaction between humans have revealed their ability to read the partner's motion plan and use it to improve one's own control. Inspired by these results, we develop an intention assimilation controller (IAC) that ena...

Human-Inspired Haptic Perception and Control in Robot-Assisted Milling Surgery.

IEEE transactions on haptics
Bone milling is one of the most widely used and high-risk procedures in various types of surgeries, and it is important to be noted that the experienced surgeon can perform such an operation safely. The objective of this article is to enhance the saf...

The Functional Rotational Workspace of a Human-Robot System can be Influenced by Adjusting the Telemanipulator Handle Orientation.

IEEE transactions on haptics
The handle design of telemanipulation master devices has not been extensively studied so far. However, the master device handle is an integral part of the robotic system through which the user interacts with the system. Previous work showed that the ...

Assessing the Accuracy and Reproducibility of PARIETAL: A Deep Learning Brain Extraction Algorithm.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Manual brain extraction from magnetic resonance (MR) images is time-consuming and prone to intra- and inter-rater variability. Several automated approaches have been developed to alleviate these constraints, including deep learning pipeli...