Reliability of the kinematic theory parameters during handwriting tasks on a vertical setup

Laurent, Anaïs; Plamondon, Réjean; Begon, Mickaël

doi:10.1016/j.bspc.2021.103157

Cited by 3 publications

(2 citation statements)

References 71 publications

(105 reference statements)

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“…These features have been adopted by studies like [84][85][86] to discriminate between normal and abnormal age-related changes in handwriting. Moreover, recent investigations on this theory seem to find common parameters to quantify the deficits caused by Parkinson's disease in handwriting and voice [98] or shoulder fatigue detection [99].…”

Section: Representationmentioning

confidence: 99%

A survey of visual and procedural handwriting analysis for neuropsychological assessment

Moetesum

Diaz

Masroor

et al. 2022

Neural Comput & Applic

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To date, Artificial Intelligence systems for handwriting and drawing analysis have primarily targeted domains such as writer identification and sketch recognition. Conversely, the automatic characterization of graphomotor patterns as biomarkers of brain health is a relatively less explored research area. Despite its importance, the work done in this direction is limited and sporadic. This paper aims to provide a survey of related work to provide guidance to novice researchers and highlight relevant study contributions. The literature has been grouped into “visual analysis techniques” and “procedural analysis techniques”. Visual analysis techniques evaluate offline samples of a graphomotor response after completion. On the other hand, procedural analysis techniques focus on the dynamic processes involved in producing a graphomotor reaction. Since the primary goal of both families of strategies is to represent domain knowledge effectively, the paper also outlines the commonly employed handwriting representation and estimation methods presented in the literature and discusses their strengths and weaknesses. It also highlights the limitations of existing processes and the challenges commonly faced when designing such systems. High-level directions for further research conclude the paper.

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Section: Representationmentioning

confidence: 99%

A survey of visual and procedural handwriting analysis for neuropsychological assessment

Moetesum

Diaz

Masroor

et al. 2022

Neural Comput & Applic

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“…The Kinematic Theory of rapid human movements ( Plamondon, 1995 ), and its consequent sigma-lognormal model ( O’Reilly and Plamondon, 2009 ), suggest that complex human movements are the result of the time superimposition of elementary movements, each of which is commanded by the central nervous system and exhibits a lognormal velocity profile. Throughout the years, the sigma-lognormal model has proved to be effective at reproducing human-like movements in 2D ( Ferrer et al., 2020 ; Parziale et al., 2020 ; Laurent et al., 2022 ) and 3D conditions ( Fischer et al., 2020 ). On the other side, typical robotic/artificial velocity profiles are constant or trapezoidal ( Kavraki and LaValle, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Observation vs. interaction in the recognition of human-like movements

et al. 2023

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A crucial aspect in human-robot collaboration is the robot acceptance by human co-workers. Based on previous experiences of interaction with their fellow beings, humans are able to recognize natural movements of their companions and associate them with the concepts of trust and acceptance. Throughout this process, the judgment is influenced by several percepts, first of all the visual similarity to the companion, which triggers a process of self-identification. When the companion is a robot, the lack of these percepts challenges such a self-identification process, unavoidably lowering the level of acceptance. Hence, while, on the one hand, the robotics industry moves towards manufacturing robots that visually resemble humans, on the other hand, a question is still open on whether the acceptance of robots can be increased by virtue of the movements they exhibit, regardless of their exterior aspect. In order to contribute to answering this question, this paper presents two experimental setups for Turing tests, where an artificial agent performs human-recorded and artificial movements, and a human subject is to judge the human likeness of the movement in two different circumstances: by observing the movement replicated on a screen and by physically interacting with a robot executing the movements. The results reveal that humans are more likely to recognize human movements through interaction than observation, and that, under the interaction condition, artificial movements can be designed to resemble human ones for future robots to be more easily accepted by human co-workers.

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