Influence of Treadmill Design on Gait: Does Treadmill Size Affect Muscle Activation Amplitude? A Musculoskeletal Calculation With Individualized Input Parameters of Gait Analysis

Multibody dynamics-based musculoskeletal modeling for gait analysis: a systematic review

Beyond qualitative assessment, gait analysis involves the quantitative evaluation of various parameters such as joint kinematics, spatiotemporal metrics, external forces, and muscle activation patterns and forces. Utilizing multibody dynamics-based musculoskeletal (MSK) modeling provides a time and cost-effective non-invasive tool for the prediction of internal joint and muscle forces. Recent advancements in the development of biofidelic MSK models have facilitated their integration into clinical decision-making processes, including quantitative diagnostics, functional assessment of prosthesis and implants, and devising data-driven gait rehabilitation protocols. Through an extensive search and meta-analysis of over 116 studies, this PRISMA-based systematic review provides a comprehensive overview of different existing multibody MSK modeling platforms, including generic templates, methods for personalization to individual subjects, and the solutions used to address statically indeterminate problems. Additionally, it summarizes post-processing techniques and the practical applications of MSK modeling tools. In the field of biomechanics, MSK modeling provides an indispensable tool for simulating and understanding human movement dynamics. However, limitations which remain elusive include the absence of MSK modeling templates based on female anatomy underscores the need for further advancements in this area.

Physiological, perceptual, and biomechanical differences between treadmill and overground walking in healthy adults: A systematic review and meta-analysis

This systematic review and meta-analysis aims to compare physiological, perceptual and biomechanical outcomes between walking on a treadmill and overground surfaces. Five databases (CINAHL, EMBASE, MEDLINE, SPORTDiscus, Web of Science) were searched until September 2022. Included studies needed to be a crossover design comparing biomechanical, physiological, or perceptual measures between motorised-treadmill and overground walking in healthy adults (18-65 years) walking at the same speed (<5% difference). The quality of studies were assessed using a modified Downs and Black Quality Index. Meta-analyses were performed to determine standardised mean difference ± 95% confidence intervals for all main outcome measures. Fifty-five studies were included with 1,005 participants. Relative oxygen consumption (standardised mean difference [95% confidence interval] 0.38 [0.14,0.63]) and cadence (0.22 [0.06,0.38]) are higher during treadmill walking. Whereas stride length (-0.36 [-0.62,-0.11]) and step length (-0.52 [-0.98,-0.06]) are lower during treadmill walking. Most kinetic variables are different between surfaces. The oxygen consumption, spatiotemporal and kinetic differences on the treadmill may be an attempt to increase stability due to the lack of control, discomfort and familiarity on the treadmill. Treadmill construction including surface stiffness and motor power are likely additional constraints that need to be considered and require investigation. This research was supported by an Australian Government Research Training Program (RTP) scholarship. Protocol registration is CRD42020208002 (PROSPERO International Prospective Register of Systematic Reviews) in October 2020.

A novel attention-guided ECA-CNN architecture for sEMG-based gait classification

Gait recognition and classification technology is one of the essential technologies for detecting neurodegenerative dysfunction. This paper presents a gait classification model based on a convolutional neural network (CNN) with an efficient channel attention (ECA) module for gait detection applications using surface electromyographic (sEMG) signals. First, the sEMG sensor was used to collect the experimental sample data, and various gaits of different persons were collected to construct the sEMG signal data sets of different gaits. The CNN is used to extract the features of the one-dimensional input sEMG signal to obtain the feature vector, which is input into the ECA module to realize cross-channel interaction. Then, the next part of the convolutional layer is input to learn the signal features further. Finally, the model is output and tested to obtain the results. Comparative experiments show that the accuracy of the ECA-CNN network model can reach 97.75%.