Editorial: Current state of postural research - Moving beyond the balance platform
Notice bibliographique
Résumé
Assessment of postural control with a dynamic Balance Platform was a disruptive technology at its time (1). Findings emerging from research with the dynamic platform significantly altered not only the approach to studying control of posture, but also shaped theories of postural control over subsequent decades. In this special issue, Keshner et al. argue that the most dominant paradigm shift since adoption of the posture platform was that reactive postural control is not simply a reflexive process but one that is highly dependent on the demands of the specific task as well as the state of the performer. Hall et al. review evidence for a strong linkage between emotional state and the somatic nervous system in the control of posture. Our prior special issue (2) focused on postural research that challenged the view of postural control as a reflexive mechanism. The aim of this special issue was to solicit articles that focused on the concept of posture control as a complex and adaptable motor act and were not necessarily reliant upon the dynamic balance platform as an experimental protocol. Wang et al. explored a substantial component of cortically controlled behavior known as perceptual motor style They quantified the variability that emerges both within and across individual performers and identified both static and dynamic markers that could specify the perceptual motor style. Significant heterogeneity was found across individuals performing locomotion in a height threatening virtual environment. Other markers of asymmetry in gait were explored in the lateral plane of motion, Dusane et al. showed that the ability to control lateral COM motion during walking could be a contributing factor to imbalance in people with incomplete spinal cord injury. Peterka et al. identified four major gait asymmetries (involving step width, ankle torque, stance duration and swing duration) as determinants of a separate mechanism in the control of mediolateral stability in gait. The variability of these asymmetry measures is shown to better distinguish between young and old performers than conventional measures associated with poor balance and fall risk.Results from the research presented in this issue provide further evidence to support a significant contribution of both cortical and subcortical processing, attention control networks, and sensory-motor perceptual systems to the control of posture and balance under both perturbed and unperturbed (static and dynamic) conditions. The work done initially by Nashner and colleagues has, in fact, laid the foundation to a better understanding of control of posture and gait, using newly emerging technologies (e.g., mobile imaging and wearable sensors) and commercially available equipment that mimics the dynamic platform paradigm (i.e., motorized treadmills). The adoption of these approaches to postural control research in no way negates the continued value of the dynamic platform as a crucial paradigm that could be used both in stance and gait to examine cortical control. Instead, the evidence expands the importance of the study of reactive balance control by considering the contribution of cortical networks to posture and balance.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,003 |
| Bibliométrie | 0,005 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,008 | 0,006 |
| Science ouverte | 0,005 | 0,002 |
| Intégrité de la recherche | 0,016 | 0,016 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,012 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».