Elsevier

Gait & Posture

Volume 41, Issue 2, February 2015, Pages 694-698
Gait & Posture

The measurement of in vivo joint angles during a squat using a single camera markerless motion capture system as compared to a marker based system

https://doi.org/10.1016/j.gaitpost.2015.01.028Get rights and content

Highlights

  • A single Kinect holds great potential as a clinical tool to assess 3D motion.

  • We compared joint angles between a Kinect and a marker-based motion capture system.

  • The Kinect reliability and accuracy agreed well with the marker-based system.

Abstract

Markerless motion capture may have the potential to make motion capture technology widely clinically practical. However, the ability of a single markerless camera system to quantify clinically relevant, lower extremity joint angles has not been studied in vivo. Therefore, the goal of this study was to compare in vivo joint angles calculated using a marker-based motion capture system and a Microsoft Kinect during a squat. Fifteen individuals participated in the study: 8 male, 7 female, height 1.702 ± 0.089 m, mass 67.9 ± 10.4 kg, age 24 ± 4 years, BMI 23.4 ± 2.2 kg/m2. Marker trajectories and Kinect depth map data of the leg were collected while each subject performed a slow squat motion. Custom code was used to export virtual marker trajectories for the Kinect data. Each set of marker trajectories was utilized to calculate Cardan knee and hip angles. The patterns of motion were similar between systems with average absolute differences of <5 deg. Peak joint angles showed high between-trial reliability with ICC > 0.9 for both systems. The peak angles calculated by the marker-based and Kinect systems were largely correlated (r > 0.55). These results suggest the data from the Kinect can be post processed in way that it may be a feasible markerless motion capture system that can be used in the clinic.

Introduction

Altered hip and knee mechanics have been attributed to numerous conditions across the lifespan. For example, hip and knee mechanics are routinely measured in children with cerebral palsy [e.g. [1]], adults who have their anterior cruciate ligament reconstructed [e.g. [2]], adults with patellofemoral pain [e.g. [3]], and older individuals with osteoarthritis [e.g. [4]]. Defining the alterations in hip and knee mechanics has been enhanced by the development of sophisticated marker-based motion capture systems. The ability of the clinician to have a reliable and accurate means of quantifying the same types of altered mechanics that researchers use is still lacking. This limits the translation of laboratory based findings of abnormal mechanics to the diagnosis and treatment of lower extremity injuries in the clinic. However, recent developments in markerless motion capture technology may offer the potential for such systems to be available to clinicians in the near term.

Marker-based systems have limitations that have driven the development of markerless motion capture technology. The main limitations being the use of many cameras that make it impractical to use in a variety of settings such as a patient's home, a clinic, or a sports field as well as the high cost of marker-based cameras. To address these limitations, single camera markerless systems have been evaluated and shown promise in their ability to measure finger kinematics [5], trunk lean [6], [7], and foot posture [8]. However, the measurement of hip and knee kinematics during movement is lacking and necessary to establish the Kinect as a clinical tool to aid in diagnosis and treatment. Therefore, the goal of this study was to compare the ability of a single camera markerless motion capture system to measure hip and knee angles during movement to those measured by a marker-based system.

Section snippets

Data collection

After completing an informed consent approved by an institutional review board, 15 healthy people participated in this study (8 male, 7 female, height 1.702 ± 0.089 m, mass 67.9 ± 10.4 kg, age 24 ± 4 years, BMI 23.4 ± 2.2 kg/m2). First, 28 retro-reflective markers were placed on the participant (Fig. 1). Next, the participant was scanned using a projector-camera setup [9], [10], which creates a three-dimensional surface model of the person. Then, marker trajectories were measured with a 10 camera motion

Results

The patterns of motion were similar between systems where the difference between systems was greatest at the peak flexed position (i.e. bottom of the squat) (Fig. 3). The Kinect underestimated peak hip flexion by 4.3 degrees (9% of the total range of hip flexion motion) (Table 1) where the bias of hip flexion angles was −6.5 deg (Table 2). No significant differences were found in peak hip adduction, axial rotation, or knee angles (Table 1), in which the bias was <7 deg (Table 2). Peak joint

Discussion

The use of a single Microsoft Kinect camera holds potential as a clinical surrogate for the assessment of 3D motion. In this study, we compared the ability of a Kinect to measure joint angles with current state of the art, a marker-based motion capture system. Both systems agreed well for the shape of the motion calculated (Fig. 3); had high between trial reliability (Table 1); and exhibited a strong correlation between systems for the peak angles calculated (Fig. 4).

The average absolute

Acknowledgement

This work was funded by the Division of Information and Intelligent Systems of the National Science Foundation, grant 1231545.

Conflict of interest statement: There are no conflicts of interest to disclose.

References (26)

  • C.D. Metcalf et al.

    Markerless motion capture and measurement of hand kinematics: validation and application to home-based upper limb rehabilitation

    IEEE Trans Biomed Eng

    (2013)
  • B.F. Mentiplay et al.

    Reliability and validity of the Microsoft Kinect for evaluating static foot posture

    J Foot Ankle Res

    (2013)
  • D. Lanman et al.

    Build your own 3D scanner: optical triangulation for beginners

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