3D location deduced by inertial measurement units: a challenging problem

Lepetit, Kevin; Hansen, Clint; Mansour, Khalil Ben; Marin, F.

doi:10.1080/10255842.2015.1070589

Cited by 3 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shuai et al used seven nine-axis inertial measurement units (IMUs) method to collect joint angle information of functional movements [3]; Vakanski et al used a Vicon motion capture system to collect joint angle and joint position information of functional movements [4]; and Wang et al collect video data of FMS assessment movements by using two 2D cameras with different viewpoints [5]. Although these devices have high-precision motion capture capabilities and enable fast and accurate assessments by analyzing large amounts of motion data, traditional motion capture systems such as IMU, Vicon, and OptiTrack require invasive operations such as tagging or wearing sensors on the subject [6][7][8], which are not only tedious but may also interfere with the subject's movements. At the same time, the high price of these devices limits their popularity and diffusion in fields such as sports medicine and rehabilitation therapy [9].…”

Section: Introductionmentioning

confidence: 99%

Effective Quantization Evaluation Method of Functional Movement Screening with Improved Gaussian Mixture Model

et al. 2023

View full text Add to dashboard Cite

Background: Functional movement screening (FMS) allows for the rapid assessment of an individual’s physical activity level and the timely detection of sports injury risk. However, traditional functional movement screening often requires on-site assessment by experts, which is time-consuming and prone to subjective bias. Therefore, the study of automated functional movement screening has become increasingly important. Methods: In this study, we propose an automated assessment method for FMS based on an improved Gaussian mixture model (GMM). First, the oversampling of minority samples is conducted, the movement features are manually extracted from the FMS dataset collected with two Azure Kinect depth sensors; then, we train the Gaussian mixture model with different scores (1 point, 2 points, 3 points) of feature data separately; finally, we conducted FMS assessment by using a maximum likelihood estimation. Results: The improved GMM has a higher scoring accuracy (improved GMM: 0.8) compared to other models (traditional GMM = 0.38, AdaBoost.M1 = 0.7, Naïve Bayes = 0.75), and the scoring results of improved GMM have a high level of agreement with the expert scoring (kappa = 0.67). Conclusions: The results show that the proposed method based on the improved Gaussian mixture model can effectively perform the FMS assessment task, and it is potentially feasible to use depth cameras for FMS assessment.

show abstract