AI Medical Compendium Topic:
Movement

Clear Filters Showing 631 to 640 of 999 articles

Deep Analysis of Mitochondria and Cell Health Using Machine Learning.

Scientific reports
There is a critical need for better analytical methods to study mitochondria in normal and diseased states. Mitochondrial image analysis is typically done on still images using slow manual methods or automated methods of limited types of features. Mi...

Learning-Based Quality Control for Cardiac MR Images.

IEEE transactions on medical imaging
The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artifacts, such as cardiac...

Intra-Slice Motion Correction of Intravascular OCT Images Using Deep Features.

IEEE journal of biomedical and health informatics
Intra-slice motion correction is an important step for analyzing volume variations and pathological formations from intravascular imaging. Optical coherence tomography (OCT) has been recently introduced for intravascular imaging and assessment of cor...

Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance.

Journal of sports sciences
Objective assessment of an athlete's performance is of importance in elite sports to facilitate detailed analysis. The implementation of automated detection and recognition of sport-specific movements overcomes the limitations associated with manual ...

Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model.

Sensors (Basel, Switzerland)
Movement analysis of infants' body parts is momentous for the early detection of various movement disorders such as cerebral palsy. Most existing techniques are either marker-based or use wearable sensors to analyze the movement disorders. Such techn...

Human Motion Recognition by Textile Sensors Based on Machine Learning Algorithms.

Sensors (Basel, Switzerland)
Wearable sensors for human physiological monitoring have attracted tremendous interest from researchers in recent years. However, most of the research involved simple trials without any significant analytical algorithms. This study provides a way of ...

Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities.

Journal of biomechanics
Traditional laboratory experiments, rehabilitation clinics, and wearable sensors offer biomechanists a wealth of data on healthy and pathological movement. To harness the power of these data and make research more efficient, modern machine learning t...

Real-time, simultaneous myoelectric control using a convolutional neural network.

PloS one
The evolution of deep learning techniques has been transformative as they have allowed complex mappings to be trained between control inputs and outputs without the need for feature engineering. In this work, a myoelectric control system based on con...

A hierarchical multimodal system for motion analysis in patients with epilepsy.

Epilepsy & behavior : E&B
During seizures, a myriad of clinical manifestations may occur. The analysis of these signs, known as seizure semiology, gives clues to the underlying cerebral networks involved. When patients with drug-resistant epilepsy are monitored to assess thei...

Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network.

Sensors (Basel, Switzerland)
Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition of motion data. Specifically, in human activity recognition (HAR), IMU sensor data collected from human motion are categorically combined to formulate datasets tha...