Evaluating Lower Limb Kinematics Using Microsoft's Kinect: A Simple, Novel Method.

Area of Science:

• Biomechanics
• Motion Analysis
• Rehabilitation Technology

Background:

• Marker-based systems (MBS) are the gold standard for motion capture but are costly and time-consuming.
• Markerless motion capture offers a potentially more accessible and efficient alternative for clinical applications.

Purpose of the Study:

• To evaluate the Microsoft Kinect sensor (MKS) as a markerless system for lower limb motion analysis.
• To compare the accuracy of MKS with a state-of-the-art marker-based system (MBS).
• To investigate MKS accuracy in capturing multi-planar lower limb joint movements during walking.

Main Methods:

• Participants walked while gait data were simultaneously recorded by both MKS and MBS.
• Custom software using Microsoft Visual Studio and Kinect for Windows SDK was developed for MKS data analysis.
• 3D joint angles from MBS were calculated using Visual 3D C-Motion software.
• Root-mean-square error (RMSE) was used to quantify deviations between MKS and MBS joint angle measurements.

Main Results:

• The average RMSE between MKS and MBS was less than 5°, indicating minimal clinical significance.
• Quantitative measurements of knee and hip joint angles were acquired with acceptable accuracy using MKS.
• The MKS demonstrated capability in simultaneously capturing lower limb joint movements across multiple planes during walking.

Conclusions:

• The Microsoft Kinect sensor (MKS) provides a reasonably accurate and cost-effective solution for markerless lower limb motion capture.
• MKS shows promise for clinical use, particularly in pre- and post-treatment rehabilitation stages.
• Further validation with larger study populations is recommended to fully establish MKS reliability.