Integrative approach to pedobarography and pelvis-trunk motion for knee osteoarthritis detection and exploration of non-radiographic rehabilitation monitoring.

Background: Osteoarthritis (OA) is a highly prevalent global musculoskeletal disorder, and knee OA (KOA) accounts for four-fifths of the cases worldwide. It is a degenerative disorder that greatly affects the quality of life. Thus, it is managed through different methods, such as weight loss, physical therapy, and knee arthroplasty. Physical therapy aims to strengthen the knee periarticular muscles to improve joint stability. Methods: Pedobarographic data and pelvis and trunk motion of 56 adults are recorded. Among them, 28 subjects were healthy, and 28 subjects were suffering from varying degrees of KOA. Age, sex, BMI, and the recorded variables are used together to identify subjects with KOA using machine learning (ML) models, namely, logistic regression, SVM, decision tree, and random forest. Surface electromyography (sEMG) signals are also recorded bilaterally from two muscles, the rectus femoris and biceps femoris caput longus, bilaterally during various activities for two healthy and six KOA subjects. Cluster analysis is then performed using the principal components obtained from time-series features, frequency features, and time-frequency features. Results: KOA is successfully identified using the pedobarographic data and the pelvis and trunk motion with the highest accuracy and sensitivity of 89.3% and 85.7%, respectively, using a decision tree classifier. In addition, sEMG data have been successfully used to cluster healthy subjects from KOA subjects, with wavelet analysis features providing the best performance for the standing activity under different conditions. Conclusion: KOA is detected using gait variables not directly related to the knee, such as pedobarographic measurements and pelvis and trunk motion captured by pedobarography mats and wearable sensors, respectively. KOA subjects are also distinguished from healthy individuals through clustering analysis using sEMG data from knee periarticular muscles during walking and standing. Gait data and sEMG complement each other, aiding in KOA identification and rehabilitation monitoring. It is important because wearable sensors simplify data collection, require minimal sample preparation, and offer a non-radiographic, safe method suitable for both laboratory and real-world scenarios. The decision tree classifier, trained with stratified k-fold cross validation (SKCV) data, is observed to be the best for KOA identification using gait data.

Framework for early detection and classification of balance pathologies using posturography and anthropometric variables.

BACKGROUND: Early detection of balance-related pathologies in adults using Posturography, anthropometric and personal data is limited. Our goal is to address this issue. It will enable us to identify adults in early stages of balance disorders using easily accessible and measurable data. METHODS: Open-source data of 163 subjects (47 males and 116 females) is used to train and test classification algorithms. Features include mean and standard deviation of the center of pressure displacement, obtained through posturography, the anthropometric and personal variables (age, sex, body mass index, foot length), and Trail Making Test scores. 75% of the data is employed for training and 25% of the data is used for testing. It is then validated using an indigenously collected dataset of healthy individuals. FINDINGS: Accuracy and Sensitivity, both, increases when anthropometric and personal variables are included alongside center of pressure features for classification. Specificity decreases slightly with the addition of anthropometric and personal variables with center of pressure displacement feature, which also affects the classification algorithms' performance. Standard deviation of the center of pressure displacement is found to be more effective than the mean value. A similar trend of the increased performance is observed during validation, except when neural networks were used for the classification. INTERPRETATION: Posturography data, Anthropometric measurements, personal data and self-assessment scales can identify balance issues in adults, making it suitable for community health centers with limited resources. Early detection prompts timely medical care, improving the management of disorders and thus enhancing the quality of life through rehabilitation.

Engineering Aspects of Incidence, Prevalence, and Management of Osteoarthritis: A Review.

The knee is the biggest and complicated lower extremity joint that supports mobility and the entire weight of the human body and lies between the hip joint and ankle joint. Osteoarthritis (OA) is the most common joint disease in the knee among various musculoskeletal disorders globally, with an age-associated increase in incidence and prevalence. Health monitoring of the knee joints in daily life, and early OA diagnosis is challenging and draws attention to the various methods of diagnosis for this irreversible disease. In this review, electronic databases have been searched from inception for a detailed study about knee OA and its management. It focuses on various sensor technologies and different semi-invasive and non-invasive diagnosis methods with their limitations. In the last decade, various researchers have engrossed their attention to the potential of piezoelectric-based acoustic sensors to fabricate a wearable device for OA and its management. A sensor-based wearable device using vibroarthrography as a tool can be an appropriate solution for early-stage disease detection. We firmly believe that wearable technology for the detection of OA in daily life activities will play a significant role in managing this disease and help to reduce the chances of total knee replacements.

UHMWPE-MWCNT-nHA based hybrid trilayer nanobiocomposite: Processing approach, physical properties, stem/bone cell functionality, and blood compatibility.

The development of polymeric nanocomposites for biomedical applications remains a major challenge in terms of tailored addition of nanoparticles to realize the simultaneous enhancement of fracture resistance and cell/blood compatibility. To address this, the present work has been planned to determine whether small addition of surface functionalized multiwalled-carbon-nanotube, MWCNT (<1.5 wt%) and egg-shell derived nanosized hydroxyapatite, nHA (<10 wt%) to ultrahigh-molecular-weight-polyethylene (UHMWPE) can significantly improve the physical properties as well as biocompatibility. The difference in mouse osteoblast and human mesenchymal stem cell (hMSc) proliferation has been validated using both the monolithic composite and a trilayered composite with two different UHMWPE nanocomposites on either face with pure polymer at the middle. The combination of rheology and micro-CT with fractography reveals the homogeneous dispersion of nanofillers, leading to mechanical property enhancement. The quantitative analysis of cell viability and cell spreading by immunocytochemistry method, using vinculin and vimentin expression, establish significant cytocompatibility with hMSc and osteoblast cells onto the trilayer hybrid nanobiocomposite substrates. The hemocompatibility of the investigated composites under the controlled flow of rabbit blood in a microfluidic device reveals the signature of reduced thrombogenesis with reduction of platelet activation on UHMWPE nanocomposite w.r.t. unreinforced UHMWPE. An attempt has been made to discuss the blood compatibility results in the backdrop of the bovine serum albumin adsorption kinetics. Summarizing, the present study establishes that the twin requirement of mechanical property and cyto/hemo-compatibility can be potentially realized in developing trilayer composites in UHMWPE-nHA-MWCNT system.