Leverage mechanical alterations during walking at self-selected speed in patients with Parkinson's disease

Biomechanics Parameters of Gait Analysis to Characterize Parkinson's Disease: A Scoping Review

Parkinson's disease (PD) is characterized by a slow, short-stepping, shuffling gait pattern caused by a combination of motor control limitations due to a reduction in dopaminergic neurons. Gait disorders are indicators of global health, cognitive status, and risk of falls and increase with disease progression. Therefore, the use of quantitative information on the gait mechanisms of PD patients is a promising approach, particularly for monitoring gait disorders and potentially informing therapeutic interventions, though it is not yet a well-established tool for early diagnosis or direct assessment of disease progression. Over the years, many studies have investigated the spatiotemporal parameters that are altered in the PD gait pattern, while kinematic and kinetic gait parameters are more limited. A scoping review was performed according to the PRISMA guidelines. The Scopus and PubMed databases were searched between 1999 and 2023. A total of 29 articles were included that reported gait changes in PD patients under different gait conditions: single free walking, sequential motor task, and dual task. The main findings of our review highlighted the use of optoelectronic systems for recording kinematic parameters and force plates for measuring kinetic parameters, due to their high accuracy. Most gait analyses in PD patients have been conducted at self-selected walking speeds to capture natural movement, although studies have also examined gait under various conditions. The results of our review indicated that PD patients experience alterations in the range of motion of the hip, knee, and ankle joints, as well as a reduction in the power generated/absorbed and the extensor/flexor moments. These findings suggest that the PD gait pattern may be more effectively understood using kinematic and kinetic parameters.

Beyond Inverse Dynamics: Methods for Assessment of Individual Muscle Function during Gait

Three-dimensional motion analysis performed in the modern gait analysis laboratory provides a wealth of information about the kinematics and kinetics of human locomotion, but standard gait analysis is largely restricted to joint-level measures. Three-dimensional joint rotations, joint moments, and joint powers tell us a great deal about gait mechanics, but it is often of interest to know about the roles that muscles play. This narrative review surveys work that has been done, largely over the past four decades, to augment standard gait analysis with muscle-level assessments of function. Often, these assessments have incorporated additional technology such as ultrasound imaging, or complex modeling and simulation techniques. The review discusses measurements of muscle moment arm during walking along with assessment of muscle mechanical advantage, muscle-tendon lengths, and the use of induced acceleration analysis to determine muscle roles. In each section of the review, examples are provided of how the auxiliary analyses have been used to gain potentially useful information about normal and pathological human walking. While this work highlights the potential benefits of adding various measures to gait analysis, it is acknowledged that challenges to implementation remain, such as the need for specialized knowledge and the potential for bias introduced by model choices.

MINIMIZED FEATURE SELECTION FOR DETECTION OF PARKINSON’S DISEASE USING NEURO-FUZZY SYSTEM

This study presents a methodology for detecting Parkinson’s disease using a neuro-fuzzy system (NFS) with feature selection. From all the 22 features, the five most accurate minimized features were selected using neural networks with weighted fuzzy membership functions (NEWFMs), which supported the nonoverlapping region method (NORM). NORM eliminates the worst features and can select the minimized features constituting each interpretable fuzzy membership. As an input to the NEWFMs, all 22 features indicated a performance sensitivity, specificity and accuracy of 87.43%, 96.43% and 88.72%, respectively. In addition, at least five features of the NEWFMs showed performance sensitivity, specificity and accuracy of 95.24%, 85.42% and 92.82%, respectively.

Mechanical advantage and joint function of the lower limb during hopping at different frequencies

Mechanical output at a joint level could be influenced by its leverage characteristics and by its functional behaviour and both could change to accommodate the demands of a given locomotor task. In this study, the mechanical power generated at the knee and ankle joints and their functional indexes (i.e. damper, strut, spring and motor like-function) were calculated by using 3D kinematic and kinetic data during hopping at 2, 2.5, 3 and 3.5 Hz. The effective mechanical advantage (i.e. the ratio between internal and external moment arm) of the knee (EMA_K) and ankle (EMA_A) and joint stiffness were calculated as well. Joint stiffness increased with frequency whereas positive and negative joint power decreased with it, the ankle power values being always larger (20-50%) than those at the knee. EMA_A reached its highest value (0.4) during the propulsive phase at 3 Hz whereas no significant changes in EMA_K were observed as a function of frequency in both the absorption and propulsive phases. Knee joint-functional index shifted from a spring to a strut-like function with increasing frequency (from 56 to 8% and from 4 to 51%, respectively) while the ankle operated mainly as a spring (from 90 to 53%), its damper and motor-like indexes being negligible at all frequencies (<5%). Therefore, in hopping, the knee works to dissipate mechanical energy (the combination of its damper and strut indexes increase from 23 to 72% at these frequencies) and the primary source of mechanical power is attributable to the elastic function of the ankle.