Elham Dolatabadi
Elham Dolatabadi
York University; Vector Institute; University of Toronto
Verified email at - Homepage
Cited by
Cited by
Concurrent validity of the Microsoft Kinect for Windows v2 for measuring spatiotemporal gait parameters
E Dolatabadi, B Taati, A Mihailidis
Medical engineering & physics 38 (9), 952-958, 2016
An automated classification of pathological gait using unobtrusive sensing technology
E Dolatabadi, B Taati, A Mihailidis
IEEE Transactions on Neural Systems and Rehabilitation Engineering 25 (12 …, 2017
Artificial intelligence education programs for health care professionals: scoping review
R Charow, T Jeyakumar, S Younus, E Dolatabadi, M Salhia, ...
JMIR medical education 7 (4), e31043, 2021
Quantitative Mobility Assessment for Fall Risk Prediction in Dementia: A Systematic Review
E Dolatabadi, K Van Ooteghem, T Babak, I Andrea
Dementia Geriatric Cognitive Disorder 45 (5-6), …, 2018
Automatic detection of compensation during robotic stroke rehabilitation therapy
YX Zhi, M Lukasik, MH Li, E Dolatabadi, RH Wang, B Taati
IEEE journal of translational engineering in health and medicine 6, 1-7, 2017
The Toronto Rehab Stroke Pose Dataset to Detect Compensation during Stroke Rehabilitation Therapy
E Dolatabadi, Y Zhi, B Ye, G Lupinacci, A Mihailidis, R Wang, B Taati
11th EAI International Conference on Pervasive Computing Technologies for …, 2018
Vision-based assessment of gait features associated with falls in people with dementia
S Mehdizadeh, E Dolatabadi, KD Ng, A Mansfield, A Flint, B Taati, ...
The Journals of Gerontology: Series A 75 (6), 1148-1153, 2020
The feasibility of a vision-based sensor for longitudinal monitoring of mobility in older adults with dementia
E Dolatabadi, YX Zhi, AJ Flint, A Mansfield, A Iaboni, B Taati
Archives of Gerontology and Geriatrics, 2019
Mixture-model Clustering of Pathological Gait Patterns
E Dolatabadi, A Mansfield, KK Patterson, B Taati, A Mihailidis
IEEE Journal of Biomedical and Health Informatics, 2016
Vision-based approach for long-term mobility monitoring: Single case study following total hip replacement
E Dolatabadi, B Taati, A Mihailidis
Journal of Rehabilitation Research and Development (JRRD) 51 (7), 1165–1176, 2014
Automated classification of pathological gait after stroke using ubiquitous sensing technology
E Dolatabadi, B Taati, A Mihailidis
2016 38th Annual International Conference of the IEEE Engineering in …, 2016
Evaluating Knowledge Transfer in Neural Network for Medical Images
S Akbarian, L Seyyed-Kalantari, F Khalvati, E Dolatabadi
arXiv preprint arXiv:2008.13574, 2020
Accelerating the appropriate adoption of artificial intelligence in health care: protocol for a multistepped approach
D Wiljer, M Salhia, E Dolatabadi, A Dhalla, C Gillan, D Al-Mouaswas, ...
JMIR Research Protocols 10 (10), e30940, 2021
A markerless motion tracking approach to understand changes in gait and balance: A case study
E Dolatabadi, B Taati, GS Parra-Dominguez, A Mihailidis
Proceedings of the Rehabilitation Engineering and Assistive Technology …, 2013
Ubiquitous WBAN-based electrocardiogram monitoring system
E Dolatabadi, S Primak
2011 IEEE 13th International Conference on e-Health Networking, Applications …, 2011
Implementing AI in healthcare
E Drysdale, E Dolatabadi, C Chivers, V Liu, S Saria, M Sendak, J Wiens, ...
Toward mitigating pressure injuries: Detecting patient orientation from vertical bed reaction forces
G Wong, S Gabison, E Dolatabadi, G Evans, T Kajaks, P Holliday, ...
Journal of Rehabilitation and Assistive Technologies Engineering 7 …, 2020
Discovering social determinants of health from case reports using natural language processing: algorithmic development and validation
S Raza, E Dolatabadi, N Ondrusek, L Rosella, B Schwartz
BMC Digital Health 1 (1), 35, 2023
A smart system to generate and validate question answer pairs for COVID-19 literature
R Bhambhoria, L Feng, D Sepehr, J Chen, C Cowling, S Kocak, ...
Proceedings of the First Workshop on Scholarly Document Processing, 20-30, 2020
FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?
S Raza, S Ghuge, C Ding, D Pandya
arXiv preprint arXiv:2401.11033, 2024
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