Title : The importance of biomechanical assessments in musculoskeletal disorders
Abstract:
Biomechanical assessments play a crucial role in diagnosing and managing musculoskeletal disorders by enabling precise analysis of joint forces, muscle activity, movement patterns, and mechanical stress on different body structures. Many conditions, including osteoarthritis, movement disorders, muscular imbalances, flatfoot, and spinal deformities, stem from biomechanical alterations that often remain undetected without comprehensive analysis. Advanced techniques such as 3D motion analysis, plantar pressure measurement, electromyography (EMG), and force plate systems allow for early identification of subtle movement deviations, leading to personalized rehabilitation and treatment strategies.
One of the most critical applications of biomechanical assessment is in fall prevention among older adults. Falls are a major public health concern, leading to fractures, reduced mobility, loss of independence, and increased mortality rates. The World Health Organization (WHO) identifies falls as one of the leading causes of unintentional injury-related deaths in the elderly, imposing a significant economic burden on healthcare systems. Recent research has highlighted two key factors contributing to fall risk: (1) the effect of fear of falling on gait mechanics and (2) the application of machine learning models in identifying individuals at high risk of falls. Studies indicate that fear of falling alters gait parameters, reducing walking speed, increasing step time, and decreasing postural stability, all of which elevate fall risk. These effects are exacerbated under dual-task conditions, where cognitive or manual tasks are performed simultaneously with walking, leading to greater instability and gait modifications.
Another significant advancement in biomechanical research is the integration of artificial intelligence (AI) and machine learning in musculoskeletal assessments.
Machine learning models have demonstrated remarkable accuracy in predicting fall risk based on biomechanical gait parameters such as step length, horizontal propulsion force, and hip angle at heel strike. Interestingly, traditional assessments like the sit-to-stand test showed minimal improvement in model accuracy, underscoring the superior predictive value of dynamic gait analysis. The integration of AI in clinical settings offers a powerful tool for early risk detection, enabling tailored intervention programs for fall prevention.
Beyond fall risk, AI-driven biomechanical analysis is transforming joint force estimation, particularly in the knee joint. Wearable sensors combined with machine learning algorithms can accurately predict knee contact forces using non-invasive EMG signals. A recent study utilizing the TPOT (Tree-based Pipeline Optimization Tool) model achieved exceptional accuracy (R² > 0.99) in estimating knee joint forces in older adults with knee implants. This breakthrough suggests that machine learning can serve as a non-invasive alternative to direct force measurement techniques, paving the way for real-time musculoskeletal monitoring.
The findings from these studies emphasize the need for a comprehensive, interdisciplinary approach to biomechanical analysis, integrating cognitive, neuromuscular, and movement science perspectives. Future research should focus on wearable biomechanical monitoring systems, virtual reality-based balance training, and AI-driven rehabilitation programs to further optimize musculoskeletal health interventions. Ultimately, the integration of biomechanical assessments with advanced computational techniques holds great promise in enhancing clinical decision-making, reducing healthcare costs, and improving patient outcomes.
