Markerless Knee Joint Position Measurement Using Depth Data during Stair Walking

Abstract

Climbing and descending stairs are demanding daily activities, and the monitoring of them may reveal the presence of musculoskeletal diseases at an early stage. A markerless system is needed to monitor such stair walking activity without mentally or physically disturbing the subject. Microsoft Kinect v2 has been used for gait monitoring, as it provides a markerless skeleton tracking function. However, few studies have used this device for stair walking monitoring, and the accuracy of its skeleton tracking function during stair walking has not been evaluated. Moreover, skeleton tracking is not likely to be suitable for estimating body joints during stair walking, as the form of the body is different from what it is when it walks on level surfaces. In this study, a new method of estimating the 3D position of the knee joint was devised that uses the depth data of Kinect v2. The accuracy of this method was compared with that of the skeleton tracking function of Kinect v2 by simultaneously measuring subjects with a 3D motion capture system. The depth data method was found to be more accurate than skeleton tracking. The mean error of the 3D Euclidian distance of the depth data method was 43.2 ± 27.5 mm, while that of the skeleton tracking was 50.4 ± 23.9 mm. This method indicates the possibility of stair walking monitoring for the early discovery of musculoskeletal diseases.

Identification of Early Knee Osteoarthritis Based on Knee Joint Trajectory during Stair Climbing

“…The reason is that Kinect v2 does not contain the data acquired by the tilted sensor and needs to be tilted to capture the stair climbing [ 20 ]. Therefore, in this study, we used our previously suggested method to obtain the knee joint trajectories with Kinect v2 [ 13 , 14 ]. According to the previous method, we only used depth data captured from behind when climbing stairs.…”

“…As measuring activities at the house is delicate, an infrared-based depth sensor, which is non-contact and markerless, to measure kinematic parameters is reasonable at the point of considering privacy protection and body constraint-free. Our previously proposed measurement system, “IR-Locomotion” [ 13 ] acquires body joint trajectory based on a depth sensor. In this study, we used a modified version of “IR-Locomotion” [ 14 ] to reveal the kinematic characteristics of early KOA patients during stair climbing using for feasibility.…”

A SWOT Analysis of Portable and Low-Cost Markerless Motion Capture Systems to Assess Lower-Limb Musculoskeletal Kinematics in Sport

“…In such studies, markerless motion capture has shown great promise. Specifically, focusing on the ability to detect lower extremity movement, multiple studies have indicated that markerless motion capture can efficiently capture spatiotemporal joint kinematic variables (Clark et al, 2013;Sandau et al, 2014;Mentiplay et al, 2015;Rocha et al, 2018) with moderate-to-high agreement during tasks such as a single leg squat (Perrott et al, 2017;Kotsifaki et al, 2018;Tipton et al, 2019), vertical jump (Drazan et al, 2021), countermovement jump (Kotsifaki et al, 2018), stair climbing (Ogawa et al, 2017), walking (Ceseracciu et al, 2014;Sandau et al, 2014;Kanko et al, 2021;Pagnon et al, 2021;Stenum et al, 2021;Takeda et al, 2021;Vafadar et al, 2021), running (Corazza et al, 2006;Macpherson et al, 2016;Pagnon et al, 2021), gymnastics tasks (Corazza et al, 2006(Corazza et al, , 2010Mündermann et al, 2007), and clinical evaluations (Eltoukhy et al, 2017;Mauntel et al, 2021). To date, the highest accuracy with markerless motion capture has been achieved when fitting a prior articulated model to a 3D surface visual hull reconstruction using matching algorithms (Corazza et al, 2006(Corazza et al, , 2007(Corazza et al, , 2008(Corazza et al, , 2010Mündermann et al, 2006bMündermann et al, , 2007.…”