Gait analysis is an essential tool for detecting biomechanical irregularities, designing personalized rehabilitation plans, and enhancing athletic performance. Currently, gait assessment depends on ...either visual observation, which lacks consistency between raters and requires clinical expertise, or instrumented evaluation, which is costly, invasive, time-consuming, and requires specialized equipment and trained personnel. Markerless gait analysis using 2D pose estimation techniques has emerged as a potential solution, but it still requires significant computational resources and human involvement, making it challenging to use. This research proposes an automated method for temporal gait analysis that employs the MediaPipe Pose, a low-computational-resource pose estimation model. The study validated this approach against the Vicon motion capture system to evaluate its reliability. The findings reveal that this approach demonstrates good (ICC
> 0.75) to excellent (ICC
> 0.90) agreement in all temporal gait parameters except for double support time (right leg switched to left leg) and swing time (right), which only exhibit a moderate (ICC
> 0.50) agreement. Additionally, this approach produces temporal gait parameters with low mean absolute error. It will be useful in monitoring changes in gait and evaluating the effectiveness of interventions such as rehabilitation or training programs in the community.
Marker Free Gait Analysis using Pose Estimation Model Tony Hii, Chang Soon; Gan, Kok Beng; Zainal, Nasharuddin ...
2022 IEEE 20th Student Conference on Research and Development (SCOReD),
11/2022
Conference Proceeding
In recent years, gait analysis has gained prominence among scholars. Gait analysis is used extensively in medical diagnoses, rehabilitation, and biometric identification. Clinical gait analysis is ...often conducted in a gait lab employing a 3D motion capture system and a pressure sensing walkway. Using wearable inertial sensors, the gait assessment can also be conducted outside the gait lab. However, these methods are limited by expensive equipment and the need for specialized knowledge to conduct a reliable gait evaluation. Thus, a marker-free deep learning based pose estimation method is suggested to assess the lower limb joint kinematics robustly and accurately during gait analysis. This study seeks to determine the pose estimation model that provide reliable and accurate lower limb joint kinematics evaluation in real-world applications. In the conclusion, the average inference speeds for OpenPose, MediaPipe Pose, and MMPose are 17.00, 30.19, and 2.82 frames per second, respectively, with average correlations of 0.896, 0.944, and 0.942 between the calculated lower limb joint kinematics and baseline. Therefore, MediaPipe Pose is the best pose estimation model for assessing the kinematics of the lower limb joints in real-world applications.
Historically, gait analysis has been performed through visual observation or assisted by tools such as wearable sensors, motion capture systems, and force plates. However, the accuracy of visual ...observation can be affected by the subjective judgment and experience of clinicians, and instrumented gait analysis requires trained operators to achieve precise results. In recent years, deep learning technology has advanced, and researchers have turned their attention towards deep learning-based pose estimation models, which have shown promise in assessing walking and running gait with encouraging outcomes. However, although markerless gait analysis based on pose estimation has potential, it is usually confined to the sagittal plane and is not suitable for use in a clinical or home setting. This research proposes a new method for analyzing gait in the frontal plane using pose estimation models such as OpenPose, YOLOv7 Pose, and MediaPipe Pose and a single camera. The main objective of the study is to evaluate the reliability of these models for frontal plane gait analysis by comparing them to the widely accepted 3D Vicon motion capture system. Additionally, the study aims to determine the best pose estimation model for automated gait analysis in the frontal plane. The MediaPipe Pose model was found to be the best model for frontal gait assessment due to its moderate inference speed (17.58 fps) and strongest correlation of gait outcomes with the Vicon motion capture system (r: 0.924, ICC(2,1): 0.919).
