ORCID Profile
0000-0002-4591-2412
Current Organisations
Monash University
,
Nanyang Technological University
,
Anna University
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2014
Publisher: IEEE
Date: 11-2017
Publisher: Elsevier BV
Date: 12-2021
Publisher: IEEE
Date: 10-2014
Publisher: IEEE
Date: 11-2016
Publisher: IEEE
Date: 08-2014
Publisher: IEEE
Date: 09-2012
Publisher: Springer Science and Business Media LLC
Date: 24-06-2015
DOI: 10.1038/NCLIMATE2692
Publisher: IEEE
Date: 12-2015
Publisher: Frontiers Media SA
Date: 08-06-2021
DOI: 10.3389/FNEUR.2021.650024
Abstract: Background: Gait dysfunction or impairment is considered one of the most common and devastating physiological consequences of stroke, and achieving optimal gait is a key goal for stroke victims with gait disability along with their clinical teams. Many researchers have explored post stroke gait, including assessment tools and techniques, key gait parameters and significance on functional recovery, as well as data mining, modeling and analyses methods. Research Question: This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings. Methods: A comprehensive literature search was conducted within Google Scholar, PubMed, and ScienceDirect using a set of keywords, including lower extremity, walking, post-stroke, and kinematics. Original articles that met the selection criteria were included. Results and Significance: This scoping review aimed to shed light on tools and technologies employed in post stroke gait assessment toward bridging the existing gap between the research and clinical communities. Conventional qualitative gait analysis, typically used in clinics is mainly based on observational gait and is hence subjective and largely impacted by the observer's experience. Quantitative gait analysis, however, provides measured parameters, with good accuracy and repeatability for the diagnosis and comparative assessment throughout rehabilitation. Rapidly emerging smart wearable technology and AI, including Machine Learning, Support Vector Machine, and Neural Network approaches, are increasingly commanding greater attention in gait research. Although their use in clinical settings are not yet well leveraged, these tools promise a paradigm shift in stroke gait quantification, as they provide means for acquiring, storing and analyzing multifactorial complex gait data, while capturing its non-linear dynamic variability and offering the invaluable benefits of predictive analytics.
Publisher: Springer Science and Business Media LLC
Date: 20-11-2020
Publisher: Public Library of Science (PLoS)
Date: 11-02-2016
Publisher: IEEE
Date: 06-2016
Publisher: IEEE
Date: 11-2019
Publisher: MDPI AG
Date: 08-06-2020
DOI: 10.3390/S20113260
Abstract: In this work, we propose a practical approach to estimate human joint stiffness during tooling tasks for the purpose of programming a robot by demonstration. More specifically, we estimate the stiffness along the wrist radial-ulnar deviation while a human operator performs flexion-extension movements during a polishing task. The joint stiffness information allows to transfer skills from expert human operators to industrial robots. A typical hand-held, abrasive tool used by humans during finishing tasks was instrumented at the handle (through which both robots and humans are attached to the tool) to assess the 3D force/torque interactions between operator and tool during finishing task, as well as the 3D kinematics of the tool itself. Building upon stochastic methods for human arm impedance estimation, the novelty of our approach is that we rely on the natural variability taking place during the multi-passes task itself to estimate (neuro-)mechanical impedance during motion. Our apparatus (hand-held, finishing tool instrumented with motion capture and multi-axis force/torque sensors) and algorithms (for filtering and impedance estimation) were first tested on an impedance-controlled industrial robot carrying out the finishing task of interest, where the impedance could be pre-programmed. We were able to accurately estimate impedance in this case. The same apparatus and algorithms were then applied to the same task performed by a human operators. The stiffness values of the human operator, at different force level, correlated positively with the muscular activity, measured during the same task.
No related grants have been discovered for Dhanya Menoth Mohan.