Dataset Information

Dynamic Frequency Analyses of Lower Extremity Muscles during Sit-To-Stand Motion for the Patients with Knee Osteoarthritis.

ABSTRACT:

Objective

Muscle activities during the sit-to-stand motion (STS) are characterized by coordinated movements between hip extensors and knee extensors. However, previous reports regarding the STS and lower extremity muscle activities have focused on some quantitative assessment, but little qualitative research. This study aimed to examine the muscle activities of the lower extremity both quantitatively and qualitatively.

Methods

Study participants included 13 patients with knee osteoarthritis (knee OA) and 11 age-matched asymptomatic controls. The task was STS from a chair with a height-adjustable seat. EMG activities were acquired using surface electromyogram. The root mean square signals normalized as a percentage of maximum voluntary isometric contraction values (RMS%MVC) and the mean power frequency (MPF) were calculated.

Results

During STS, knee OA patients had increased RMS%MVC of the vastus medialis and raised MPF of the rectus femoris before buttocks-off.

Conclusion

These findings suggest that STS of knee OA patients not only increased relative muscle activity of the vastus medialis, but also enlisted the rectus femoris in knee extension to improve muscle contraction force by activating more type II fibers to accomplish buttocks-off.

SUBMITTER: Anan M

PROVIDER: S-EPMC4726819 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Dynamic Frequency Analyses of Lower Extremity Muscles during Sit-To-Stand Motion for the Patients with Knee Osteoarthritis.

Anan Masaya M Shinkoda Koichi K Suzuki Kentaro K Yagi Masahide M Kito Nobuhiro N

PloS one 20160125 1

<h4>Objective</h4>Muscle activities during the sit-to-stand motion (STS) are characterized by coordinated movements between hip extensors and knee extensors. However, previous reports regarding the STS and lower extremity muscle activities have focused on some quantitative assessment, but little qualitative research. This study aimed to examine the muscle activities of the lower extremity both quantitatively and qualitatively.<h4>Methods</h4>Study participants included 13 patients with knee oste ...[more]

PMID: 26807578

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Project description:IntroductionKnee osteoarthritis (KOA) is a prevalent condition often associated with a decline in patients' physical function. Objective self-assessment of physical conditions poses challenges for many advanced KOA patients. To address this, we explored the potential of a computer vision method to facilitate home-based physical function self-assessments.MethodsWe developed and validated a simple at-home artificial intelligence approach to recognize joint stiffness levels and physical function in individuals with advanced KOA. One hundred and four knee osteoarthritis (KOA) patients were enrolled, and we employed the WOMAC score to evaluate their physical function and joint stiffness. Subsequently, patients independently recorded videos of five sit-to-stand tests in a home setting. Leveraging the AlphaPose and VideoPose algorithms, we extracted time-series data from these videos, capturing three-dimensional spatiotemporal information reflecting changes in key joint angles over time. To deepen our study, we conducted a quantitative analysis using the discrete wavelet transform (DWT), resulting in two wavelet coefficients: the approximation coefficients (cA) and the detail coefficients (cD).ResultsOur analysis specifically focused on four crucial joint angles: "the right hip," "right knee," "left hip," and "left knee." Qualitative analysis revealed distinctions in the time-series data related to functional limitations and stiffness among patients with varying levels of KOA. In quantitative analysis, we observed variations in the cA among advanced KOA patients with different levels of physical function and joint stiffness. Furthermore, there were no significant differences in the cD between advanced KOA patients, demonstrating different levels of physical function and joint stiffness. It suggests that the primary difference in overall movement patterns lies in the varying degrees of joint stiffness and physical function among advanced KOA patients.DiscussionOur method, designed to be low-cost and user-friendly, effectively captures spatiotemporal information distinctions among advanced KOA patients with varying stiffness levels and functional limitations utilizing smartphones. This study provides compelling evidence for the potential of our approach in enabling self-assessment of physical condition in individuals with advanced knee osteoarthritis.

Project description:Musculoskeletal conditions impede patient biomechanical function. However, clinicians rely on subjective functional assessments with poor test characteristics for biomechanical outcomes because more advanced assessments are impractical in the ambulatory care setting. Using markerless motion capture (MMC) in clinic to record time-series joint position data, we implemented a spatiotemporal assessment of patient kinematics during lower extremity functional testing to evaluate whether kinematic models could identify disease states beyond conventional clinical scoring. 213 trials of the star excursion balance test (SEBT) were recorded by 36 subjects during routine ambulatory clinic visits using both MMC technology and conventional clinician scoring. Conventional clinical scoring failed to distinguish patients with symptomatic lower extremity osteoarthritis (OA) from healthy controls in each component of the assessment. However, principal component analysis of shape models generated from MMC recordings revealed significant differences in subject posture between the OA and control cohorts for six of the eight components. Additionally, time-series models of subject posture change over time revealed distinct movement patterns and reduced overall postural change in the OA cohort compared to the controls. Finally, a novel metric quantifying postural control was derived from subject specific kinematic models and was shown to distinguish OA (1.69), asymptomatic postoperative (1.27), and control (1.23) cohorts (p = 0.0025) and to correlate with patient-reported OA symptom severity (R = -0.72, p = 0.018). Time series motion data have superior discriminative validity and clinical utility than conventional functional assessments in the case of the SEBT. Novel spatiotemporal assessment approaches can enable routine in-clinic collection of objective patient-specific biomechanical data for clinical decision-making and monitoring recovery. Author summary Osteoarthritis (OA) is a leading cause of disability in the United States. Despite the relevance of biomechanical function as a marker of disease severity and as a target for therapeutic interventions, clinical assessments of biomechanical function are significantly limited by clinician subjectivity and poor test characteristics while more advanced methods are not feasible due to the need for specialized equipment and trained personnel. Coupling a single markerless motion capture camera with statistical modeling of posture change, we developed a practical system to perform advanced biomechanical assessments of lower extremity function during routine clinic visits. To validate our system, OA patients and healthy controls were assessed performing a functional balance task by clinicians according to conventional scoring and separately by our motion capture system using kinematic posture modeling. Although clinical scoring failed to distinguish OA patients and healthy controls, our kinematic modeling and dimensionality reduction techniques identified significant differences in both subject posture and motion trajectories throughout the assessment. Furthermore, OA patients reporting more severe symptoms exhibited worse postural control. Our results imply that novel motion capture approaches can enable routine in-clinic collection of objective patient-specific biomechanical data for clinical decision-making and monitoring recovery.

Dataset Information

Dynamic Frequency Analyses of Lower Extremity Muscles during Sit-To-Stand Motion for the Patients with Knee Osteoarthritis.

Objective

Methods

Results

Conclusion

Publications

Dynamic Frequency Analyses of Lower Extremity Muscles during Sit-To-Stand Motion for the Patients with Knee Osteoarthritis.

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