Author

L. Needham

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· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:imaging· 3 Research Focus:benchmarking· 2 Approach:deep learning· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Organism:human· 1 Approach:statistical model· 1

Frequent Co-Authors

L. Wade· 3 shared M. Evans· 3 shared D. P. Cosker· 2 shared J. L. Bilzon· 2 shared M. P. McGuigan· 2 shared S. L. Colyer· 2 shared D. Cosker· 1 shared J. Bilzon· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 32 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

3 shared papersBioengineering

L. Wade

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Examination of 2D frontal and sagittal markerless motion capture: Implications for 2D and 3D markerless applications

L. Wade, L. Needham, M. Evans, M. P. McGuigan, S. Colyer, D. Cosker, J. Bilzon

This paper evaluates 2D markerless motion capture accuracy for joint angles, finding higher random differences for occluded joints and highlighting limitations in current pose estimation algorithms.

bioRxiv · Jan 17, 2023 · PreprintBioengineering

The Development and Evaluation of a Fully Automated Markerless Motion Capture Workflow

L. Needham, M. Evans, L. Wade, D. P. Cosker, M. P. McGuigan, J. L. Bilzon, S. L. Colyer

This paper develops a fully automated markerless motion capture workflow using deep learning and evaluates its accuracy against marker-based methods for lower limb kinematics during running, walking, and jumping.

bioRxiv · Feb 16, 2022Bioengineering

Human Movement Science in The Wild: Can Current Deep-Learning Based Pose Estimation Free Us from The Lab?

L. Needham, M. Evans, D. P. Cosker, L. Wade, P. M. McGuigan, J. L. Bilzon, S. L. Colyer

This study evaluates deep-learning pose estimation accuracy against marker-based motion capture in real-world settings, finding systematic errors in joint center locations.

bioRxiv · Apr 22, 2021 · PreprintBioengineering