Validity of motion analysis using the Kinect system to evaluate single leg stance in patients with hip disorders

Asaeda, Masashi; Kuwahara, Wataru; Fujita, Naoto; Yamasaki, Takuma; Adachi, Nobuo

doi:10.1016/j.gaitpost.2018.04.010

Cited by 25 publications

(14 citation statements)

References 18 publications

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“…In this longitudinal clinical assessment, a participant’s gait was assessed two times with two different conditions each. Rated as gold standard for investigating changes in gait [33–36], a computerized video-based motion analysis system with infrared cameras was used to track changes in pre-defined spatiotemporal parameters. The precision of this system has been shown in various publications [37–39].…”

Section: Methodsmentioning

confidence: 99%

The long-term effects of an implantable drop foot stimulator on gait in hemiparetic patients

et al. 2019

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Drop foot is a frequent abnormality in gait after central nervous system lesions. Different treatment strategies are available to functionally restore dorsal extension during swing phase in gait. Orthoses as well as surface and implantable devices for electrical stimulation of the peroneal nerve may be used in patients who do not regain good dorsal extension. While several studies investigated the effects of implanted systems on walking speed and gait endurance, only a few studies have focussed on the system’s impact on kinematics and long-term outcomes. Therefore, our aim was to further investigate the effects of the implanted system ActiGait on gait kinematics and spatiotemporal parameters for the first time with a 1-year follow-up period. 10 patients were implanted with an ActiGait stimulator, with 8 patients completing baseline and follow-up assessments. Assessments included a 10-m walking test, video-based gait analysis and a Visual Analogue Scale (VAS) for health status. At baseline, gait analysis was performed without any assistive device as well as with surface electrical stimulation. At follow-up patients walked with the ActiGait system switched off and on. The maximum dorsal extension of the ankle at initial contact increased significantly between baseline without stimulation and follow-up with ActiGait (p = 0.018). While the spatio-temporal parameters did not seem to change much with the use of ActiGait in convenient walking speed, patients did walk faster when using surface stimulation or ActiGait compared to no stimulation at the 10-m walking test at their fastest possible walking speed. Patients rated their health better at the 1-year follow-up. In summary, a global improvement in gait kinematics compared to no stimulation was observed and the long-term safety of the device could be confirmed.

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Section: Methodsmentioning

confidence: 99%

The long-term effects of an implantable drop foot stimulator on gait in hemiparetic patients

et al. 2019

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“…The Kinect for Windows SDK 2.0 allows for tracking the 3D positions and orientations of 25 joints of six users simultaneously [ 9 ]. However, Microsoft has stopped the production of the Kinect v2 [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard: A Pilot Study

Albert

Owolabi

Gebel

et al. 2020

Sensors

197

120

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Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking using a gold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people.

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“…Además, si se hace una comparación respecto a metodologías las cuales no incluyen el uso del marco de Frenet Serret, pero si el Kinect, se puede encontrar resultados en la clasificación que varían entre el 83% y 93% para la evaluación del movimiento de personas con trastorno de cadera [19], y del 95% en pacientes con Parkinson [2], con la diferencia que estos usan solo un marcador, lo que hace que aumente el porcentaje de clasificación.…”

Section: Discussionunclassified

Descripción del movimiento humano basado en el marco de Frenet Serret y datos tipo MOCAP

2021

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Clasificar el movimiento humano se ha convertido en una necesidad tecnológica, en donde para definir la posición de un sujeto requiere identificar el recorrido de las extremidades y el tronco del cuerpo, y tener la capacidad de diferenciar esta posición respecto a otros sujetos o movimientos, generándose la necesidad tener datos y algoritmos que faciliten su clasificación. Es así, como en este trabajo, se evalúa la capacidad discriminante de datos de captura de movimiento en rehabilitación física, donde la posición de los sujetos es adquirida con el Kinect de Microsoft y marcadores ópticos, y atributos del movimiento generados con el marco de Frenet Serret, evaluando su capacidad discriminante con los algoritmos máquinas de soporte vectorial, redes neuronales y k vecinos más cercanos. Los resultados presentan porcentajes de acierto del 93.5% en la clasificación con datos obtenidos del Kinect, y un éxito del 100% para los movimientos con marcadores ópticos. Classify human movement has become a technological necessity, where defining the position of a subject requires identifying the trajectory of the limbs and trunk of the body, having the ability to differentiate this position from other subjects or movements, which generates the need to have data and algorithms that help their classification. Therefore, the discriminant capacity of motion capture data in physical rehabilitation is evaluated, where the position of the subjects is acquired with the Microsoft Kinect and optical markers. Attributes of the movement generated with the Frenet Serret framework. Evaluating their discriminant capacity by means of support vector machines, neural networks, and k nearest neighbors algorithms. The obtained results present an accuracy of 93.5% in the classification with data obtained from the Kinect, and success of 100% for movements where the position is defined with optical markers.

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Validity of motion analysis using the Kinect system to evaluate single leg stance in patients with hip disorders

Cited by 25 publications

References 18 publications

The long-term effects of an implantable drop foot stimulator on gait in hemiparetic patients

The long-term effects of an implantable drop foot stimulator on gait in hemiparetic patients

Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard: A Pilot Study

Descripción del movimiento humano basado en el marco de Frenet Serret y datos tipo MOCAP

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