Conservative interventions to improve foot progression angle and clinical measures in orthopedic and neurological patients - A systematic review and meta-analysis

Biomechanical Analysis of Human Gait When Changing Velocity and Carried Loads: Simulation Study with OpenSim

Walking is one of the main activities of daily life and gait analysis can provide crucial data for the computation of biomechanics in many fields. In multiple applications, having reference data that include a variety of gait conditions could be useful for assessing walking performance. However, limited extensive reference data are available as many conditions cannot be easily tested experimentally. For this reason, a musculoskeletal model in OpenSim coupled with gait data (at seven different velocities) was used to simulate seven carried loads and all the combinations between the two parameters. The effects on lower limb biomechanics were measured with torque, power, and mechanical work. The results demonstrated that biomechanics was influenced by both speed and load. Our results expand the previous literature: in the majority of previous work, only a subset of the presented conditions was investigated. Moreover, our simulation approach provides comprehensive data that could be useful for applications in many areas, such as rehabilitation, orthopedics, medical care, and sports.

Three-dimensional gait analysis of orthopaedic common foot and ankle joint diseases

Walking is an indispensable mode of transportation for human survival. Gait is a characteristic of walking. In the clinic, patients with different diseases exhibit different gait characteristics. Gait analysis describes the specific situation of human gait abnormalities by observing and studying the kinematics and dynamics of limbs and joints during human walking and depicting the corresponding geometric curves and values. In foot and ankle diseases, gait analysis can evaluate the degree and nature of gait abnormalities in patients and provide an important basis for the diagnosis of patients' diseases, the correction of abnormal gait and related treatment methods. This article reviews the relevant literature, expounds on the clinical consensus on gait, and summarizes the gait characteristics of patients with common ankle and foot diseases. Starting from the gait characteristics of individuals with different diseases, we hope to provide support and reference for the diagnosis, treatment and rehabilitation of clinically related diseases.

Pedobarographic evaluations in physical medicine and rehabilitation practice

The feet are complex structures that transmit loads transferred by other parts of the body to the ground and are involved in many static and dynamic activities, such as standing and walking. The contact area and pressure changes between the feet and the ground surface can be measured using pedobarographic devices. With pedobarographic examinations, it is possible to obtain a wide range of information needed to support clinical evaluation and diagnostic tests in physical medicine and rehabilitation practice. Foot structure and function, postural stability, lower extremity biomechanics, and gait analysis are among the areas that can be further investigated using pedobarography.

A Deep Learning Method for Foot Progression Angle Detection in Plantar Pressure Images

Foot progression angle (FPA) analysis is one of the core methods to detect gait pathologies as basic information to prevent foot injury from excessive in-toeing and out-toeing. Deep learning-based object detection can assist in measuring the FPA through plantar pressure images. This study aims to establish a precision model for determining the FPA. The precision detection of FPA can provide information with in-toeing, out-toeing, and rearfoot kinematics to evaluate the effect of physical therapy programs on knee pain and knee osteoarthritis. We analyzed a total of 1424 plantar images with three different You Only Look Once (YOLO) networks: YOLO v3, v4, and v5x, to obtain a suitable model for FPA detection. YOLOv4 showed higher performance of the profile-box, with average precision in the left foot of 100.00% and the right foot of 99.78%, respectively. Besides, in detecting the foot angle-box, the ground-truth has similar results with YOLOv4 (5.58 ± 0.10° vs. 5.86 ± 0.09°, p = 0.013). In contrast, there was a significant difference in FPA between ground-truth vs. YOLOv3 (5.58 ± 0.10° vs. 6.07 ± 0.06°, p < 0.001), and ground-truth vs. YOLOv5x (5.58 ± 0.10° vs. 6.75 ± 0.06°, p < 0.001). This result implies that deep learning with YOLOv4 can enhance the detection of FPA.