Experimental evaluation of Microsoft Kinect's accuracy and capture rate for stroke rehabilitation applications

Webster, David; Çelik, Özkan

doi:10.1109/haptics.2014.6775498

Cited by 54 publications

(26 citation statements)

References 9 publications

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“…In recent years, the computer vision community has proposed an array of solutions to solve this challenge, but majority are not validated clinically. These works have predominantly focused on depth sensor technology, removing the need for intrusive sensors, which has been shown to be sufficiently accurate and responsive for tracking in both in-home and clinical settings [10], [12], [13].…”

Section: Related Workmentioning

confidence: 99%

“…Further, these systems provide a single health indicator whereas an in-depth descriptive indicator could prove more useful to a clinician. In addition, existing systems have been evaluated using game-orientated datasets, and without clinical validation [5], [6], [10]. We utilise the newly released K3Da dataset [11], which consists of clinically relevant motions captured using the Kinect One sensor to validate the proposed framework.…”

mentioning

confidence: 99%

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Automated Analysis and Quantification of Human Mobility Using a Depth Sensor

Leightley

McPhee

Yap

2017

IEEE J. Biomed. Health Inform.

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Analysis and quantification of human motion to support clinicians in the decision-making process is the desired outcome for many clinical-based approaches. However, generating statistical models that are free from human interpretation and yet representative is a difficult task. In this paper, we propose a framework that automatically recognizes and evaluates human mobility impairments using the Microsoft Kinect One depth sensor. The framework is composed of two parts. First, it recognizes motions, such as sit-to-stand or walking 4 m, using abstract feature representation techniques and machine learning. Second, evaluation of the motion sequence in the temporal domain by comparing the test participant with a statistical mobility model, generated from tracking movements of healthy people. To complement the framework, we propose an automatic method to enable a fairer, unbiased approach to label motion capture data. Finally, we demonstrate the ability of the framework to recognize and provide clinically relevant feedback to highlight mobility concerns, hence providing a route toward stratified rehabilitation pathways and clinician-led interventions.

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Automated Analysis and Quantification of Human Mobility Using a Depth Sensor

Leightley

McPhee

Yap

2017

IEEE J. Biomed. Health Inform.

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“…Thus, marker-based systems always set up multiple markers (Fig. 2.3) [42,49] for calibration by comparing the difference in the relative positions of the markers from the sensors or comparing the relative speeds of moving markers [47]. The advantage of the marker-based systems' calibration is the stable accuracy; IR sensors are robust to the influence of the environment of the measurement area.…”

Section: Calibrationmentioning

confidence: 99%

Development of a Self-Calibrated Motion Capture System by Nonlinear Trilateration of Multiple Kinects v2

2017

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In this paper, a Kinect-based distributed and real-time motion capture system is developed. A trigonometric method is applied to calculate the relative positions of Kinect v2 sensors with a calibration wand and register the sensors' positions automatically. By combining results from multiple sensors with a nonlinear least square method, the accuracy of motion capture is optimized. Moreover, to exclude inaccurate results from sensors, a computational geometry is applied in the occlusion approach to discover occluded joint data. The synchronization approach is based on the NTP protocol, which synchronizes the time between the clocks of a server and of clients dynamically, leading to the proposed system being real time. Experiments to validate the proposed system are conducted from the perspective of calibration, occlusion, and accuracy. More specifically, the mean absolute error of the calibration results is 0.73 cm, the proposed occlusion method is tested on upper and lower limbs, and the synchronization component guarantees the clock synchronization and real-time performance for more than 99% of the measurement process.Furthermore, to demonstrate the practical performance of our system, a comparison with previously developed motion capture systems (the linear trilateration approach [52] and the geometric trilateration approach [51]) with the benchmark OptiTrack system is performed for the tracked joints of the head, shoulder, elbow, and wrist, therein showing that the accuracy of our proposed system is 38.3% and 24.1% better than the aforementioned two trilateration systems. Quantitative analysis is also conducted on our proposed system with the commercial inertial motion capture system Delsys smart sensor system by comparing the measurements of lower limbs (i.e., hips, knees, and ankles), and the standard deviation of our proposed system's measurement results is 4.92 cm.

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“…Several other authors have used Kinect for the measurement of shoulder motion [30][31][32][33][34][35] . The accuracy of Kinect, which does not require the attachment of markers to determine limb position, has been validated in comparison with marker-based systems [36][37][38][39][40][41] . The present study was undertaken to explore the relationship between subjective and observer-independent objective measures of shoulder function.…”

mentioning

confidence: 99%