Eni Halilaj

Assistant Professor

Carnegie Mellon University

United States

Research Interests

Explore related searches

Professors in United States

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Eni Halilaj is an Assistant Professor at Carnegie Mellon University in the United States. His research focuses on biomechanics, motion tracking, and advanced mesh generation techniques, as evidenced by his recent articles on topics such as hybrid octree-based mesh generation and the fusion of video and inertial sensing data. Dr. Halilaj has also contributed to gait analysis methodologies, exploring the accuracy of kinematics estimation and the effectiveness of wearable technologies in clinical settings.

Articles (13)

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Year:

2020

Collaborators (5)

Elka Rubin

Research Professional

Stanford University Medical Center

UNITED STATES

View details

Tian Tan

Stanford University

UNITED STATES

View details

Hua Tong

Carnegie Mellon University

UNITED STATES

View details

Reed Ferber

CANADA

View details

Yongjie Jessica Zhang

George Tallman Ladd and Florence Barrett Ladd Professor

Carnegie Mellon University

UNITED STATES

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Eni Halilaj

Research Interests

Explore related searches

Contact this professor

About

Articles (13)

HybridOctree_Hex: Hybrid octree-based adaptive all-hexahedral mesh generation with Jacobian control

Seven Things to Know About Exercise Classification With Inertial Sensing Wearables

IMU-Based Kinematics Estimation Accuracy Affects Gait Retraining Using Vibrotactile Cues

IMU and Smartphone Camera Fusion for Knee Adduction and Knee Flexion Moment Estimation During Walking

SRL-assisted AFM: Generating planar unstructured quadrilateral meshes with supervised and reinforcement learning-assisted advancing front method

Markerless Motion Tracking With Noisy Video and IMU Data

Fusion of video and inertial sensing data via dynamic optimization of a biomechanical model

Predicting knee adduction moment response to gait retraining with minimal clinical data

American society of biomechanics early career achievement award 2020: Toward portable and modular biomechanics labs: How video and IMU fusion will change gait analysis

Assessment of Extractability and Accuracy of Electronic Health Record Data for Joint Implant Registries

Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework

Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning

Automated classification of radiographic knee osteoarthritis severity using deep neural networks

Collaborators (5)

Elka Rubin

Tian Tan

Hua Tong

Reed Ferber

Yongjie Jessica Zhang

Make your next move.