Author

M. Abdollahi

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

Biology Disciplines

Click to filter author papers

Bioengineering· 2

Signature Assays, Organisms & Methods

Click to filter · or open tag

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

Frequent Co-Authors

E. Rashedi· 2 shared M. Abedi· 1 shared M. Parnianpour· 1 shared N. Azadeh-Fard· 1 shared S. Ashouri· 1 shared S. Karvekar· 1 shared

Activity by Year

2019-2019 · peak 2/yr

Co-Author Collaboration Network

6 top collaborators · 11 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioengineering

E. Rashedi

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

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