Predicted Threshold for Seated Stability: Estimation of Margin of Stability Using Wearable Inertial Sensors
Notice bibliographique
Résumé
Individuals with spinal cord injury suffer from seated instability due to impaired trunk neuromuscular function. Monitoring seated stability toward the development of closed-loop controlled neuroprosthetic technologies could be beneficial for restoring trunk stability during sitting in affected individuals. However, there is a lack of (1) a biomechanical characterization to quantify the relationship between the trunk kinematics and sitting balance; and (2) a validated wearable biomedical device for assessing dynamic sitting posture and fall-risk in real-time. This study aims to: (a) determine the limit of dynamic seated stability as a function of the trunk center of mass (COM) position and velocity relative to the base of support; (b) experimentally validate the predicted limit of stability using traditional motion capture; (c) compare the predicted limit of stability with that predicted in the literature for standing and walking; and (d) validate a wearable device for assessing dynamic seated stability and risk of loss of balance. First, we used a six-segment model of the seated human body for simulation. To obtain the limit of stability, we applied forward dynamics and optimization to obtain the maximum feasible initial velocities of the trunk COM that would bring the trunk COM position to the front-end of the base-of-support for a set of initial COM positions. Second, experimental data were obtained from fifteen able-bodied individuals who maintained sitting balance while base-of-support perturbations were applied with three different amplitudes. A motion capture system and four inertial measurement units (IMUs) were used to estimate the trunk COM motion states (i.e., trunk COM position and velocity). The margin of stability was calculated as the shortest distance of the instantaneous COM motion states to those obtained as the limit of stability in the state-space plane. All experimentally obtained trunk COM motion states fell within the limit of stability. A high correlation and small root-mean-square difference were observed between the estimated trunk COM states obtained by the motion capture system and IMUs. IMU-based wearable technology, along with the predicted limit of dynamic seated stability, can estimate the margin of stability during perturbed sitting. Therefore, it has the potential to monitor the seated stability of wheelchair users affected by trunk instability.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».