Full length articleImproved kinect-based spatiotemporal and kinematic treadmill gait assessment
Introduction
Gait analysis is an effective clinical tool used for a wide range of applications including evaluating neurological diseases [1], [2], fall risk [3], orthopedic disability [4], and progress during rehabilitation [5]. While optoelectronic motion capture systems are the gold standard for dynamic movement assessment, the high financial cost and technical expertise required to operate these systems and analyze the data, make them an unrealistic option for clinical use. Although movement patterns are commonly used to assess injury risk, progress during rehabilitation, and functional performance, these assessments are often subjective limiting their effectiveness, reliability and sensitivity to change [6], [7]. The objective, quantitative nature of motion analysis could provide a clear improvement over these techniques and be of significant value within the clinical environment. Therefore, a publically available, cost-effective and clinician-friendly motion capture solution, allowing valid and reliable assessments of kinematic and spatiotemporal variables during functional movements, represents a logical step toward improved patient care.
Newly developed motion analysis technologies afford researchers and clinicians multiple options for assessing functional movement characteristics; however, many of these solutions have significant limitations. For example, wearable electromagnetic sensors are readily available [8], [9]; but data are affected by gravity noise and signal drift [10]. Additionally, this technology is costly and requires technical expertise for data analysis. Alternatively, the Kinect sensor is a commercially-available, cost-effective video game accessory [11], [12] capable of extracting data from 3D skeletal modeling [13]. The Kinect’s validity, and its use with various biomechanical applications, has been examined previously [14], [15], [16], [17]. While the first version of the Kinect (v1) had poor accuracy and tracking capacity [18], the new version (Kinect v2) may provide improved skeletal tracking with its higher camera and depth resolutions. The color (1920 × 1080 pixels) and depth (512 × 424 pixels) resolution of the Kinect v2 was significantly higher than the v1, allowing more precise joint trajectory tracking [2], [3], [4], [5]. The Kinect’s technological advancement, ease of data acquisition and processing software, increase its potential for accurately analyzing gait.
Poor agreement between Kinect v1 and optoelectronic motion capture systems for sagittal plane kinematic variables has been reported during treadmill lower extremity gait [19] and other functional movements [20]. These differences are not unexpected given the technological limitations of the Kinect v1. Studies have also demonstrated that the Kinect v1 and v2 are significantly better at assessing spatiotemporal parameters compared to lower extremity kinematic variables [21]. With the technological advancement of the v2 over the v1 camera, it is logical that tracking of sagittal plane joint range of motion during functional movement would significantly improve. But before the Kinect v2 can be utilized clinically for applications like gait analysis, its validity when assessing lower extremity kinematics must be established and compared to previous findings. To our knowledge, no studies have done this; therefore this study seeks to establish the validity of the Kinect v2 in assessing lower extremity sagittal plane kinematic and spatiotemporal parameters during treadmill gait analysis.
Section snippets
Subjects
Ten healthy subjects (5 males, 5 females, age: 26.7 ± 5.4 years, height: 174.4 ± 7.9 cm, mass: 71.8 ± 11.4 kg) participated in this study. All subjects were free of previous lower extremity surgery and were free of current injury that resulted in limitation of physical activity level. The study was approved by the University’s Human Subjects Review Board, and all participants provided written consent.
Subject preparation
Upon arrival at the laboratory, subjects were familiarized with the experimental setup and outfitted
Results
Participant characteristics are in Table 2.
Discussion
The study goal was to examine the capacity of the Kinect v2 to measure lower extremity sagittal plane kinematics and spatiotemporal events during treadmill walking. Our results indicate that the Kinect is an acceptable tool for the assessment of sagittal plane knee and hip ROM and joint angles across the gait cycle; however, significant limitations existed when assessing ankle joint kinematics. Additionally, the capacity of the Kinect to assess spatiotemporal variables was excellent for step
Conclusions
In conclusion, the Kinect v2 sensor can potentially to be an effective clinical tool for evaluating sagittal plane knee and hip joint kinematics and some spatiotemporal variables during walking gait. Given advances in sensor technology and ease of data acquisition and processing, the Kinect v2 may be a realistic and cost effective alternative to expensive traditional motion analysis systems for clinical applications. Additional investigations should examine the capacity of the Kinect v2 to
Conflicts of interest statement
None.
References (34)
- et al.
Gait variability and fall risk in community-living older adults: a 1-year prospective study
Arch. Phys. Med. Rehabil.
(2001) - et al.
Gait analysis of patients following total knee replacement: a systematic review
Knee
(2007) - et al.
Gait analysis for poststroke rehabilitation: the relevance of biomechanical analysis and the impact of gait speed
Phys. Med. Rehabil. Clin. North Am.
(2013) - et al.
Validity of the Microsoft Kinect for providing lateral trunk lean feedback during gait retraining
Gait Posture
(2013) - et al.
Validity of the Microsoft Kinect for assessment of postural control
Gait Posture
(2012) - et al.
Validity and reliability of the Kinect within functional assessment activities: comparison with standard stereophotogrammetry
Gait Posture
(2014) - et al.
Accuracy of the Microsoft Kinect for measuring gait parameters during treadmill walking
Gait Posture
(2015) - et al.
Gait assessment using the Microsoft Xbox One Kinect: concurrent validity and inter-day reliability of spatiotemporal and kinematic variables
J. Biomech
(2015) - et al.
Two simple methods for determining gait events during treadmill and overground walking using kinematic data
Gait Posture
(2008) - et al.
ISB recommendations for standardization in the reporting of kinematic data
J. Biomech.
(28 1995)
Correlation, agreement, and Bland-Altman analysis: statistical analysis of method comparison studies
Am. J. Ophthalmol.
Comparison of ICC and CCC for assessing agreement for data without and with replications
Comput. Stat. Data Anal.
Concurrent validity of the Microsoft Kinect for assessment of spatiotemporal gait variables
J. Biomech.
Ambulatory motor assessment in Parkinson's disease
Mov. Disord.
Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis
J. Appl. Physiol. (1985)
Using the functional movement screen™ to evaluate the effectiveness of training
J. Strength Cond. Res.
Clinimetric properties of the performance-oriented mobility assessment
Phys. Ther.
Cited by (116)
Examination of the prediction of the planar piecewise continuous lumped muscle parameter model for walking gait with ankle-foot orthosis
2023, Medical Engineering and PhysicsA comparison of three-dimensional kinematics between markerless and marker-based motion capture in overground gait
2023, Journal of BiomechanicsPrediction of gait kinetics using Markerless-driven musculoskeletal modeling
2023, Journal of BiomechanicsA hand motion capture method based on infrared thermography for measuring fine motor skills in biomedicine
2023, Artificial Intelligence in Medicine