MULTI MUSCLE PATTERNS IN POST SURGICAL TOTAL KNEE ARTHROPLASTY
Notice bibliographique
Résumé
INTRODUCTION Total Knee Arthroplasty (TKA) is a common surgical intervention for end stage Osteoarthritis (OA). It is implemented for pain relief and joint function restoration. There is an increasing expectation from patients concerning post-operative performance [1]. However, TKA patients often experience functional impairment such as movement, loading, and muscle activation pattern abnormalities [2]. This project focused on identifying temporal differences in muscle activations in lower limb electromyographs (EMG) between healthy persons and TKA patients, using wavelet patterns and machine learning classification. METHODS Ten post-surgical female TKA patients (TKA: 19±3 months post-surgery; 61.9±8.8 yrs; BMI 28.0±5.3) and 9 healthy age matched female controls (CON: 61.4±7.4 yrs; BMI 25.6±2.4) participated in this study. EMGs for 7 lower leg muscles during both level walking and stair climbing were collected. Muscles of interest were: tibialis anterior (TA), gastrocnemius (GAS), semitendinosus (SEM), biceps femoris (BF), rectus femoris (RF), vastus medialis (VM) and vastus lateralis (VL). Five acceptable EMG trials per subject were selected for analysis, normalized in time (stance phase ±30%) and processed in Matlab using a wavelet transform [3]. EMG data were normalized to the total signal intensity. Support Vector Machine (SVM) classification was performed for all subjects using an iterative thresholding approach and leave-one-out cross-validation. Rates of classification (called recognition rates) were deemed significant if they were greater than or equal to 68.4% (according to binomial test). SVM discriminants were visualized to aid in the identification of functional differences between CON and TKA groups. RESULTS Mean multi muscle patterns (MMPs) for walking (Figure 1) demonstrate substantive differences between groups. The muscles that gave significant recognition rates in level walking were: VM (68.4%) and BF (73.7%); and in stair climbing were: BF (84.2%), SEM (73.7%), GAS (68.4%), and TA (68.4%). DISCUSSION Application of a SVM with iterative thresholding provided significant recognition rates between groups, for both walking and stair-stepping tasks. The stepping task was characterized by a greater number of muscles with significant recognition rates, as well as the highest recognition rates. Temporal activation differences, indicative of muscle co-activation by TKA subjects, were observable in the discriminant pattern. In walking, BF and VM were active in mid-stance, illustrated as a red activity pattern in the discriminant. BF activity shifted from pre-HS to post-HS to coincide with the main activity in the quadriceps muscles (VL, VM, RF). Similarly, in stair climbing, TA displayed a co-activation with GAS at mid-stance. Further, SEM and BF displayed pronounced activation patterns at mid-stance and at TO and early swing. This may indicate an activation strategy to assist in hip extension for the TKA group. CONCLUSION The analysis approach chosen in this study identified functional differences between healthy subjects and TKA patients. There is evidence for the employment of co-activation strategy by the TKA group in both walking and stair climbing.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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) | 0,002 | 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 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 ».