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Enregistrement W7027252013

Collison avoidance with another individual: the influence of person-specific characteristics

2024· article· en· W7027252013 sur OpenAlexaboutno aff

Notice bibliographique

RevueScholars Commons (Wilfrid Laurier University) · 2024
Typearticle
Langueen
DomainePsychology
ThématiqueAction Observation and Synchronization
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCollision avoidancePerceptionAvoidance behaviourAvoidance responsePersonal spaceSpace (punctuation)CollisionVisual perceptionVirtual reality
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Collision avoidance during locomotion is an important skill that people must master to successfully navigate through dynamically changing environments. Environments are cluttered with objects that are either goals (attraction points) or obstacles (repulsion points), such that locomotion must be guided towards goals and away from obstacles. While navigating towards a goal, individuals maintain an elliptically shaped ‘personal space’ between their own body and that of the obstacle(s). The size of personal space (degree of repulsion) is modulated depending on the specific situation. Collision avoidance research has typically utilized inanimate objects such as poles, however recent findings suggest that the amount of personal space required with inanimate objects is distinct from that used to avoid another person. Historically, person-to-person collision avoidance research has examined avoidance behaviours during different situation-specific scenarios, such as mutual or joint-task avoidance, passing through apertures, and interacting on various angles. Person-person research has not, however, fully taken person-specific characteristics into consideration as factors that may contribute to different avoidance behaviours. It is unknown whether humans perceive all humans the same way or if certain external-characteristics (i.e., one’s perception of others) dictate how collisions are strategically avoided with another human. Furthermore, it is unknown if behaviours are robust across all individuals or if there are certain internal-characteristics (i.e., one’s perception of self) that influence collision avoidance behaviours. The overarching goal of my PhD was to identify the person-specific characteristics that mediate changes in avoidance behaviours, in a young adult population.\nTo address this, five collision avoidance studies were conducted in the real world or virtual reality. The first study (conducted at Wilfrid Laurier University) examined collision avoidance behaviours when circumventing two physically different sized individuals. Results from Study 1 revealed that individuals circumvent people with a larger body size with more space at the time of crossing from the center of mass compared to a person with a smaller body size. The second study (conducted at Wilfrid Laurier University) was a follow-up focused on avoidance behaviours when circumventing an individual whose body size was artificially increased using shoulder pads. Study 2 demonstrated that when an artificial extension (shoulder pads) is added to a stationary person, avoidance behaviours are unchanged. Instead, results determined a side-bias where clearance was smaller for left-side avoidance. The third study (conducted at University Rennes II, FR), focused on determining the influence of a virtual human’s age-related (older vs younger) appearance and gait characteristics on collision avoidance strategies. Study 3 found that young adults circumvent with a larger clearance when interacting with a virtual pedestrian who possesses older adult-like characteristics. Study 4 (conducted at Université Laval) determined differences in the avoidance and gaze behaviours of athletes and non-athletes during an avoidance task with a virtual pedestrian who unpredictably steered to a new direction. Avoidance and gaze behaviours were different between athletes and non-athletes during early planning, but similar during late planning. Unpredictable steering behaviours of an approaching pedestrian led to a larger clearance at the time of crossing. Study 5 (conducted at Wilfrid Laurier University) was aimed at determining if an acute nociceptive stimulus (via topical capsaicin cream) alters avoidance behaviours. Cutaneous discomfort on the lateral aspect of the arm did not alter young adults’ avoidance behaviours. However, a learning effect was observed as avoidance was different during the first trial.\nTogether, these studies suggest that additional perceptual factors are considered when avoiding collisions with other people. The results of this dissertation demonstrate that during obstacle avoidance tasks, one’s personal space is influenced by, 1) the external-characteristics of an opposing pedestrian, and 2) the internal-characteristics of oneself. Understanding the typical behaviours for circumventing pedestrians and knowing how specific external- and internal-factors influence personal space provides the necessary groundwork for understanding how these behaviours are altered with age or disease. Furthermore, this work provides valuable information about human inter-personal distances (i.e., proxemics), which may be applicable to re-designing the simulation models of interactions between pedestrians used by engineers to expand crowd simulations, to develop architectural plans for urban centers, design robots to safely navigate unfamiliar environments, or properly plan exit routes in buildings.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,016
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,016

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,016
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,029
Tête enseignante GPT0,240
Écart entre enseignants0,211 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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