Motion Analysis of Biological Systems

The first chapter examines neuromuscular and neuromechanical strategies in individuals with TFA, using the muscle synergy theory. This condition significantly limits movement, even with prosthetic use, necessitating an understanding of the effect of biomechanical constraints on muscle coordination during walking. The current literature lacks insights into whether TFAs exhibit muscle synergy alterations and whether these changes originate from centrally and/or peripherally organized circuits. The chapter addresses these gaps by providing theoretical evidence and proposing associated neural mechanisms for altered muscle synergies, which are crucial for postural stability in TFA. Overall, the chapter underscores the pivotal role of altered muscle synergies in stability maintenance and highlights how increased biomechanical constraints influence afferent drives among TFAs, leading to their alterations.

This chapter focuses on the neural basis of muscle synergy in the context of motor neuroscience. It discusses the historical significance of the concept and the contributions of both spinal circuitry and supraspinal regions in controlling muscle synergies. Despite substantial evidence and advancements in muscle synergy theory, alternate theoretical and methodological viewpoints in this field have also emerged, such as the UCM hypothesis and the EP hypothesis. The chapter also highlights the work of Nikolai Aleksandrovich Bernstein, a pioneer in muscle synergy research, and explores his extensive model that relates human coordination, kinematic degrees of freedom, and motor redundancy. The chapter further examines the influence of factors like aging, fatigue, and sports training on muscle synergies and introduces three popular dimensionality reduction techniques used in identifying patterns within muscle synergies. These techniques have applications in diverse fields such as spinal transection, stroke rehabilitation, gesture recognition, bio-robotics, and more.

This chapter discusses Renshaw cells, which are part of the recurrent feedback inhibition loop located in the spinal cord’s ventral horn. This loop regulates the firing of motoneuron signals for muscle contraction. The chapter outlines the various state feedback output responses required to lessen tremors and enhance the impacted population’s force-muscle activation response. Simulation model investigation reveals such frequency responses. Renshaw cells orchestrate the recurrent feedback inhibition by synchronizing oscillations and strengthening muscular action at frequencies over 20 Hz. They also eliminate oscillations and tremors in the muscles at about 10 Hz. This emphasizes how beneficial Renshaw cells are at reducing physiological tremors.

The chapter introduces a Hill-type simulation model, enhancing the comprehension of factors influencing force production, especially in patients with neuromuscular disorders. The chapter, using the model as a guide, explores factors such as muscle fiber length, muscle-tendon properties, and architecture. This chapter, employing the Hill-type model, facilitates understanding for nonphysiologists and nonanatomists by providing mathematical equations to comprehend the role of muscle-tendon unit mechanics in force production. Despite its benefits, the model has limitations, notably its inability to account for tendon viscoelastic properties and the challenge of representing overall muscle belly characteristics with a single muscle fiber. We discuss such constraints in this chapter as well.

For force and sEMG simulation, we introduce the motor unit recruitment and rate coding (Fuglevand) model in this chapter. This model has already been utilized in numerous research to replicate the force-sEMG relationship in patients suffering from neuromuscular weakness. Their research aims to identify the variables that significantly alter the force-sEMG curve’s slope. To investigate such changes, the Fuglevand model offers flexibility in manipulating different elements. However, building this model is a difficult undertaking. As a result, we offer a comprehensive tutorial on building this model with the R programming language in this chapter. We also provide an R platform open-source code for the Fuglevand model. With the help of this code, force-sEMG connections in both healthy people and the affected population can be studied.

This chapter provides a thorough analysis of the use of CFD in biomedical engineering, with a particular emphasis on the analysis of arterial dynamics and blood flow. The complexity of fluid flow in biological systems, governing equations, and the significance of meshing and discretization techniques for accurate simulations are covered in the first sections of the chapter. To guarantee a suitable depiction of fluid interactions, several boundary criteria are provided, including velocity inlets, outlets, wall conditions, and symmetry conditions. This chapter also includes useful examples for studying blood flow simulation in the brachial artery using 2D and 3D models. Additionally discussed is the effect of arterial constriction on bifurcating arteries and the analysis of the blood flow patterns, circulations, vortices, wall shear, and pressure contours that occur. Overall, the chapter highlights the potential of CFD to advance biomedical engineering for better medical diagnoses, therapies, and interventions, ultimately improving patient care and vascular health.

This chapter provides a comprehensive overview of gait analysis, focusing on spatial–temporal parameters and joint kinematics in the lower limb. It explores the significance of parameters in distinguishing gaits at different speeds and discusses the range of motion and muscle contributions in individuals with normal gait. The concept of stability is examined, covering factors such as center of mass components and base of support. The chapter concludes with an in-depth discussion of various musculoskeletal and neuromuscular gait abnormalities, including antalgic gait, vaulting gait, Trendelenburg gait, hemiplegic gait, diplegic gait, Parkinson’s gait, and ataxic gait, providing insights into their characteristics and underlying causes.

The chapter delves into a wide range of approaches employed by individuals to withstand perturbations, encompassing neuromuscular and neuromechanical methods. It also investigates responses to diverse perturbations, including somatosensory, vestibular, and visual stimuli. Additionally, the chapter offers insights into various training and techniques aimed at improving postural control, particularly among populations vulnerable to stability-related disorders like PD and the elderly.

In this chapter, the author provides a detailed exploration of postural disorders, focusing on conditions such as Camptocormia and Pisa Syndrome. This in-depth analysis offers readers a better understanding of the complexities of postural disorders, particularly in relation to PD. The chapter also discusses measures to assess the severity of PD, specifically addressing Camptocormia and Pisa Syndrome, and highlights the HIIT effect as a potential approach to alleviate symptoms. Additionally, the use of tests like the TUG test and the MoCA is emphasized to evaluate the impact of interventions on individuals with these conditions. The chapter concludes by identifying future research needs to enhance rehabilitation programs for individuals with postural disorders, aiming to improve their care and support.