Développement d'une méthode accélérométrique tridimensionnelle pour l'évaluation d'une population atteinte d'arthrose du genou : application à un contexte de marche

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

Changes of concave and convex rib-vertebral angle, angle difference and angle ratio in patients with right thoracic adolescent idiopathic scoliosis

The aim of this study is to describe the radiological changes in rib-vertebral angles (RVAs), rib-vertebral angle differences (RVADs), and rib-vertebral angle ratios (RVARas) in patients with untreated right thoracic adolescent idiopathic scoliosis and to compare with the normal subjects. The concave and convex RVA from T1 to T12, the RVADs and the RVARas were measured on AP digital radiographs of 44 female patients with right convex idiopathic scoliosis and 14 normal females. Patients were divided into three groups: normal subjects (group 1), scoliotic patients with Cobb's angle equal or <30° (group 2) and scoliotic patients with Cobb's angle over 30° (group 3). Overall values (mean±SD) of the RVAs on the concave side were 90.5°±17° in group 1, 90.3°±15.8° in group 2 and 88.8°±15.4° in group 3. On the convex side, values were 90.0°±17.3° in group 1, 86.3°±13.7° in group 2 and 80.7°±14.4° in group 3. Overall values (mean±SD) of the RVADs at all levels were 0.5°±0.7° in group 1, 4.0°±4.8° in group 2 and 8.0°±4.0° in group 3. The RVARa values (mean±SD) at all levels was 1.008°±0.012° in group 1, 1.041°±0.061° in group 2 and 1.102°±0.151° in group 3. RVAD and RVARa values in the scoliotic segment were greater in patients with untreated scoliosis over 30° than in patients with an untreated deformity of <30° or normal subjects. A significant effect between groups was observed for the RVA, RVAD and RVARa variables. Measurement of RVA, RVAD and RVARa should not only be performed at and around the apex of a thoracic spinal deformity, but also extended to the whole thoracic spin

Cervical spine sagittal alignment variations following posterior spinal fusion and instrumentation for adolescent idiopathic scoliosis

The aim of this study is to quantify the changes in the sagittal alignment of the cervical spine in patients with adolescent idiopathic scoliosis following posterior spinal fusion. Patients eligible for study inclusion included those with a diagnosis of mainly thoracic adolescent idiopathic scoliosis treated by means of posterior multisegmented hook and screw instrumentation. Pre and post-operative anterior-posterior and lateral radiographs of the entire spine were reviewed to assess the changes of cervical sagittal alignment. Thirty-two patients (3 boys, 29 girls) met the inclusion criteria for the study. The average pre-operative cervical sagittal alignment (CSA) was 4.0°±12.3° (range −30° to 40°) of lordosis. Postoperatively, the average CSA was 1.7°±11.4° (range −24° to 30°). After surgery, it was less than 20° in 27 patients (84.4%) and between 20° and 40° in 5 patients (15.6%). The results of the present study suggest that even if rod precontouring is performed and postoperative thoracic sagittal alignment is restored, improved or remains unchanged after significant correction of the deformity on the frontal plane, the inherent rigidity of the cervical spine limits changes in the CSA as the cervical spine becomes rigid over tim

How toe-out foot positioning influences body-dynamics during a sit-to-stand task

BACKGROUND: Toe-out foot positioning is increasingly analyzed as a compensatory body-mechanical strategy to reduce pain and joint constraints in people with degenerative joint disease during gait. However, its influence during functional tasks, such as sit-to-stand, has not been reported. RESEARCH QUESTION: How uni- and bilateral toe-out foot positioning influence body-dynamics during a STS task? METHODS: The study was conducted on 15 healthy participants. Seven feet conditions were tested: neutral (N); right toe-out angle of 10° (U10), 20° (U20), and 30° (U30); bilateral toe-out angle of 10° (B10), 20° (B20), and 30° (B30). Execution time, trunk kinematic, vertical ground reaction force ratio as well as maximal knee and hip joint moments were analyzed and compared. RESULTS: A significant difference was found across conditions in the STS execution time (p = 0.036) showing a main effect on temporal parameters using both uni- and bilateral toe-out foot positioning. A significant difference between conditions was also obtained for the vertical force ratio (p = 0.018) and the maximal knee flexion moment (p = 0.008). Post-hoc tests demonstrated a significant difference on force ratio and on knee flexion moment only while using a more pronounced unilateral toe-out foot positioning. SIGNIFICANCE: The influence of toe-out foot positioning on body-dynamics during STS supports the idea of an alteration of body-mechanical strategy, as reported in literature gait studies. The results could have an impact on the management of patients using these strategies in order to reduce the onset of secondary joint diseases such as osteoarthritis

Improved markerless gait kinematics measurement using a biomechanically-aware algorithm with subject-specific geometric modeling

International audienceDespite the advancements in developing markerless gait analysis systems, they still demonstrate lower accuracy compared to gold-standard systems. Hence, in this research, a novel approach is presented to improve the lower limb kinematics accuracy in markerless gait analysis. This approach refines the 3D lower-limb skeletons obtained by AI-based pose estimation algorithms in a subject-specific geometric manner, preserves skeleton links’ length, benefits from gait phases information that adds biomechanical awareness to the algorithm, and utilizes an embedded trajectory smoothing. Validation of the proposed method shows that it reduces 12.6%-43.5% of root mean square error (RMSE) and significantly improves kinematic curves’ similarity to the gold-standard ones. Results also prove the feasibility of more accurate lower limb kinematics calculation using a single (2.02°-7.57° RMSE) or dual RGB-D camera (1.66°-7.25° RMSE). Development of such algorithms could result in requirement of fewer cameras that deliver comparable or even superior measurement accuracy compared to multi-camera approaches

Detecting Gait Events from Accelerations Using Reservoir Computing

Segmenting the gait cycle into multiple phases using gait event detection (GED) is a well-researched subject with many accurate algorithms. However, the algorithms that are able to perform accurate and robust GED for real-life environments and physical diseases tend to be too complex for their implementation on simple hardware systems limited in computing power and memory, such as those used in wearable devices. This study focuses on a numerical implementation of a reservoir computing (RC) algorithm called the echo state network (ESN) that is based on simple computational steps that are easy to implement on portable hardware systems for real-time detection. RC is a neural network method that is widely used for signal processing applications and uses a fast-training method based on a ridge regression adapted to the large quantity and variety of IMU data needed to use RC in various real-life environment GED. In this study, an ESN was used to perform offline GED with gait data from IMU and ground force sensors retrieved from three databases for a total of 28 healthy adults and 15 walking conditions. Our main finding is that despite its low complexity, ESN is robust for GED, with performance comparable to other state-of-the-art algorithms. Our results show the ESN is robust enough to obtain good detection results in all conditions if the algorithm is trained with variable data that match those conditions. The distribution of the mean absolute errors (MAE) between the detection times from the ESN and the force sensors were between 40 and 120 ms for 6 defined gait events (95th percentile). We compared our ESN with four different state-of-the-art algorithms from the literature. The ESN obtained a MAE not more than 10 ms above three other reference algorithms for normal walking indoor and outdoor conditions and yielded the 2nd lowest MAE and the 2nd highest true positive rate and specificity when applied to outdoor walking and running conditions. Our work opens the door to using the ESN as a GED for applications in wearable sensors for long-term patient monitoring

Inertial Sensor Location for Ground Reaction Force and Gait Event Detection Using Reservoir Computing in Gait

Inertial measurement units (IMUs) have shown promising outcomes for estimating gait event detection (GED) and ground reaction force (GRF). This study aims to determine the best sensor location for GED and GRF prediction in gait using data from IMUs for healthy and medial knee osteoarthritis (MKOA) individuals. In this study, 27 healthy and 18 MKOA individuals participated. Participants walked at different speeds on an instrumented treadmill. Five synchronized IMUs (Physilog®, 200 Hz) were placed on the lower limb (top of the shoe, heel, above medial malleolus, middle and front of tibia, and on medial of shank close to knee joint). To predict GRF and GED, an artificial neural network known as reservoir computing was trained using combinations of acceleration signals retrieved from each IMU. For GRF prediction, the best sensor location was top of the shoe for 72.2% and 41.7% of individuals in the healthy and MKOA populations, respectively, based on the minimum value of the mean absolute error (MAE). For GED, the minimum MAE value for both groups was for middle and front of tibia, then top of the shoe. This study demonstrates that top of the shoe is the best sensor location for GED and GRF prediction