Using the Microsoft Kinect to assess human bimanual coordination

Optical marker-based systems are the gold-standard for capturing three-dimensional (3D) human kinematics. However, these systems have various drawbacks including time consuming marker placement, soft tissue movement artifact, and are prohibitively expensive and non-portable. The Microsoft Kinect is an inexpensive, portable, depth camera that can be used to capture 3D human movement kinematics. Numerous investigations have assessed the Kinect\u27s ability to capture postural control and gait, but to date, no study has evaluated it\u27s capabilities for measuring spatiotemporal coordination. In order to investigate human coordination and coordination stability with the Kinect, a well-studied bimanual coordination paradigm (Kelso, 1984, Kelso; Scholz, & Schöner, 1986) was adapted. ^ Nineteen participants performed ten trials of coordinated hand movements in either in-phase or anti-phase patterns of coordination to the beat of a metronome which was incrementally sped up and slowed down. Continuous relative phase (CRP) and the standard deviation of CRP were used to assess coordination and coordination stability, respectively.^ Data from the Kinect were compared to a Vicon motion capture system using a mixed-model, repeated measures analysis of variance and intraclass correlation coefficients (2,1) (ICC(2,1)).^ Kinect significantly underestimated CRP for the the anti-phase coordination pattern (p \u3c.0001) and overestimated the in-phase pattern (p\u3c.0001). However, a high ICC value (r=.097) was found between the systems. For the standard deviation of CRP, the Kinect exhibited significantly higher variability than the Vicon (p \u3c .0001) but was able to distinguish significant differences between patterns of coordination with anti-phase variability being higher than in-phase (p \u3c .0001). Additionally, the Kinect was unable to accurately capture the structure of coordination stability for the anti-phase pattern. Finally, agreement was found between systems using the ICC (r=.37).^ In conclusion, the Kinect was unable to accurately capture mean CRP. However, the high ICC between the two systems is promising and the Kinect was able to distinguish between the coordination stability of in-phase and anti-phase coordination. However, the structure of variability as movement speed increased was dissimilar to the Vicon, particularly for the anti-phase pattern. Some aspects of coordination are nicely captured by the Kinect while others are not. Detecting differences between bimanual coordination patterns and the stability of those patterns can be achieved using the Kinect. However, researchers interested in the structure of coordination stability should exercise caution since poor agreement was found between systems

Reliability and comparison of Kinect-based methods for estimating spatiotemporal gait parameters of healthy and post-stroke individuals

[EN] Different studies have analyzed the potential of the off-the-shelf Microsoft Kinect, in its different versions, to estimate spatiotemporal gait parameters as a portable markerless low-cost alternative to laboratory grade systems. However, variability in populations, measures, and methodologies prevents accurate comparison of the results. The objective of this study was to determine and compare the reliability of the existing Kinect-based methods to estimate spatiotemporal gait parameters in healthy and post-stroke adults. Forty-five healthy individuals and thirty-eight stroke survivors participated in this study. Participants walked five meters at a comfortable speed and their spatiotemporal gait parameters were estimated from the data retrieved by a Kinect v2, using the most common methods in the literature, and by visual inspection of the videotaped performance. Errors between both estimations were computed. For both healthy and post-stroke participants, highest accuracy was obtained when using the speed of the ankles to estimate gait speed (3.6¿5.5 cm/s), stride length (2.5¿5.5 cm), and stride time (about 45 ms), and when using the distance between the sacrum and the ankles and toes to estimate double support time (about 65 ms) and swing time (60¿90 ms). Although the accuracy of these methods is limited, these measures could occasionally complement traditional tools.This work was supported by Universitat Politecnica de Valencia (Grant PAID-10-16) and Fundacio La Marato de la TV3 (Project VALORA).Latorre, J.; Llorens Rodríguez, R.; Colomer, C.; Alcañiz Raya, ML. (2018). Reliability and comparison of Kinect-based methods for estimating spatiotemporal gait parameters of healthy and post-stroke individuals. Journal of Biomechanics. 72:268-273. https://doi.org/10.1016/j.jbiomech.2018.03.008S2682737

Comparing Microsoft Kinect and Observational Gait Analysis in Assessing Gait Parameters of Apparently Healthy Adults

Objectives: Although the Microsoft Kinect has compelling potential for gait analysis in medicine, data available to compare it with observational gait analysis (OGA) is scarce. This study compared the Microsoft Kinect and the OGA in assessing the gait parameters of apparently healthy adults. Methods: Ninety-seven apparently healthy young male adults participated in this comparative study. First, the participant’s age, height, weight, and body mass index were obtained. Afterward, gait parameters involving the number of steps, cadence, stride length, and step length were assessed concurrently following OGA standard procedures and the Microsoft Kinect during a 6-m walk down the hallway. The obtained data were analyzed using descriptive and inferential statistics. The significance level was set at P<0.05. Results: The Mean±SD walk time, steps, cadence, velocity, and stride length were 8.07±1.39 s, 14.0±2.96 counts, 72.9±11.9 steps/min, 0.8±0.13 m/s, and 0.77±0.13m, respectively. Step length was significantly higher (P<0.05) with Microsoft Kinect than OGA, whereas stride length and walk speed values were significantly (P<0.05) lower with Microsoft Kinect. A moderate but significant (P=0.001) positive correlation existed between Microsoft Kinect and OGA regarding walk speed. In contrast, regarding the step length, a weak but significant (P<0.05) positive correlation was found between Microsoft Kinect and OGA. Discussion: Step length values of Microsoft Kinect were significantly higher than OGA values, whereas stride length and walk speed values of Microsoft Kinect were significantly lower than OGA values. Walk speed and step length measured by Microsoft Kinect and OGA were positively correlated

Doppler Radar for the Extraction of Biomechanical Parameters in Gait Analysis

The applicability of Doppler radar for gait analysis is investigated by quantitatively comparing the measured biomechanical parameters to those obtained using motion capturing and ground reaction forces. Nineteen individuals walked on a treadmill at two different speeds, where a radar system was positioned in front of or behind the subject. The right knee angle was confined by an adjustable orthosis in five different degrees. Eleven gait parameters are extracted from radar micro-Doppler signatures. Here, new methods for obtaining the velocities of individual lower limb joints are proposed. Further, a new method to extract individual leg flight times from radar data is introduced. Based on radar data, five spatiotemporal parameters related to rhythm and pace could reliably be extracted. Further, for most of the considered conditions, three kinematic parameters could accurately be measured. The radar-based stance and flight time measurements rely on the correct detection of the time instant of maximal knee velocity during the gait cycle. This time instant is reliably detected when the radar has a back view, but is underestimated when the radar is positioned in front of the subject. The results validate the applicability of Doppler radar to accurately measure a variety of medically relevant gait parameters. Radar has the potential to unobtrusively diagnose changes in gait, e.g., to design training in prevention and rehabilitation. As contact-less and privacy-preserving sensor, radar presents a viable technology to supplement existing gait analysis tools for long-term in-home examinations.Comment: 13 pages, 9 figures, 2 tables, accepted for publication in the IEEE Journal of Biomedical and Health Informatics (J-BHI

Human Gait Analysis in Neurodegenerative Diseases: a Review

This paper reviews the recent literature on technologies and methodologies for quantitative human gait analysis in the context of neurodegnerative diseases. The use of technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. In this paper, sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to gait analysis. First, the phases of the human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. The work continues with a survey on the publicly available datasets principally used for comparing results. Then the paper reports the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problems and future directions is outlined

Clinical 3-D Gait Assessment of Patients with Polyneuropathy Associated with Hereditary Transthyretin Amyloidosis

Hereditary amyloidosis associated with transthyretin V30M (ATTRv V30M) is a rare and inherited multisystemic disease, with a variable presentation and a challenging diagnosis, follow-up and treatment. This condition entails a definitive and progressive motor impairment that compromises walking ability from near onset. The detection of the latter is key for the disease's diagnosis. The aim of this work is to perform quantitative 3-D gait analysis in ATTRv V30M patients, at different disease stages, and explore the potential of the obtained gait information for supporting early diagnosis and/or stage distinction during follow-up. Sixty-six subjects (25 healthy controls, 14 asymptomatic ATTRv V30M carriers, and 27 symptomatic patients) were included in this case-control study. All subjects were asked to walk back and forth for 2 min, in front of a Kinect v2 camera prepared for body motion tracking. We then used our own software to extract gait-related parameters from the camera's 3-D body data. For each parameter, the main subject groups and symptomatic patient subgroups were statistically compared. Most of the explored gait parameters can potentially be used to distinguish between the considered group pairs. Despite of statistically significant differences being found, most of them were undetected to the naked eye. Our Kinect camera-based system is easy to use in clinical settings and provides quantitative gait information that can be useful for supporting clinical assessment during ATTRv V30M onset detection and follow-up, as well as developing more objective and fine-grained rating scales to further support the clinical decisions.This work was supported by the National funding agency, FCT—Fundação para a Ciência e a Tecnologia, in the context of the projects (UIDB/50014/2020; UIDB/00127/2020) and scholarship (SFRH/DB/110438/2015). This work was also supported by the Porto University Hospital Center (CHUP) in the context of the scholarship (BI.02/2018/UCA/CHP) as part of the research project [2014/167(119-DEFI/149-CES)]info:eu-repo/semantics/publishedVersio