Author

E. Rashedi

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 4

Signature Assays, Organisms & Methods

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Assay:imaging· 4 Research Focus:cell type annotation· 3 Research Focus:clustering· 2 Approach:machine learning (classical)· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Organism:human· 1

Frequent Co-Authors

M. Parnianpour· 3 shared K. Khalaf· 2 shared M. Abdollahi· 2 shared M. Davoudi· 2 shared N. Meftahi· 2 shared M. Abedi· 1 shared M. Bagheri· 1 shared M. Bagheri Rouchi· 1 shared

Activity by Year

2019-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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3 shared papersBioengineering

M. Parnianpour

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Author profile →

4 papers

Recent·Most cited

Lumbar time-varying muscle synergies in trunk flexion and bending movements at different velocities

M. Bagheri, M. Davoudi, N. Meftahi, M. Parnianpour, K. Khalaf, E. Rashedi

This study analyzes time-varying muscle synergies in trunk movements using a musculoskeletal model, finding four dominant synergies that explain 75% of variance in muscle activity.

bioRxiv · Apr 4, 2022 · PreprintBioengineering

Lumbar time-varying muscle synergies in trunk flexion and bending movements at different velocities

M. Bagheri Rouchi, M. Davoudi, N. Meftahi, E. Rashedi, M. Parnianpour, K. Khalaf

This study investigates time-varying muscle synergies in trunk movements using a musculoskeletal model, finding four dominant synergies that explain 75% of variance in muscle activity.

bioRxiv · Apr 3, 2022 · PreprintBioengineering

Using a Motion Sensor to Categorize Low Back Pain Patients: A Machine Learning Approach

M. Abdollahi, S. Ashouri, M. Abedi, N. Azadeh-Fard, M. Parnianpour, E. Rashedi

This paper develops a machine learning model using kinematic data from motion sensors to classify low back pain patients into high and low-medium risk groups, achieving ~75% accuracy with SVM.

bioRxiv · Oct 13, 2019Bioengineering

A Data-Driven Model to Identify Fatigue Level Based on the Motion Data from a Smartphone

S. Karvekar, M. Abdollahi, E. Rashedi

This paper develops machine learning models to classify fatigue levels using smartphone motion data, achieving 91% accuracy for binary classification and 61% for four-level classification.

bioRxiv · Oct 8, 2019 · PreprintBioengineering