scholarly journals RGB-D Sensors as Marker-Less MOCAP Systems: A Comparison Between Microsoft Kinect V2 and the New Microsoft Kinect Azure

2021 ◽  
pp. 359-367
Author(s):  
Benedetta Rosa ◽  
Filippo Colombo Zefinetti ◽  
Andrea Vitali ◽  
Daniele Regazzoni
Keyword(s):  
Microsoft Kinect ◽  
Kinect V2

scholarly journals The Accuracy of the Microsoft Kinect V2 Sensor for Human Gait Analysis. A Different Approach for Comparison with the Ground Truth

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4405
Author(s):  
Diego Guffanti ◽  
Alberto Brunete ◽  
Miguel Hernando ◽  
Javier Rueda ◽  
Enrique Navarro Cabello
Keyword(s):  
Gait Analysis ◽  
Ground Truth ◽  
Potential Method ◽  
Error Rates ◽  
Microsoft Kinect ◽  
Human Gait ◽  
Kinect V2 ◽  
Dynamic Time ◽  
Human Gait Analysis ◽  
Sensor Accuracy

Several studies have examined the accuracy of the Kinect V2 sensor during gait analysis. Usually the data retrieved by the Kinect V2 sensor are compared with the ground truth of certified systems using a Euclidean comparison. Due to the Kinect V2 sensor latency, the application of a uniform temporal alignment is not adequate to compare the signals. On that basis, the purpose of this study was to explore the abilities of the dynamic time warping (DTW) algorithm to compensate for sensor latency (3 samples or 90 ms) and develop a proper accuracy estimation. During the experimental stage, six iterations were performed using the a dual Kinect V2 system. The walking tests were developed at a self-selected speed. The sensor accuracy for Euclidean matching was consistent with that reported in previous studies. After latency compensation, the sensor accuracy demonstrated considerably lower error rates for all joints. This demonstrated that the accuracy was underestimated due to the use of inappropriate comparison techniques. On the contrary, DTW is a potential method that compensates for the sensor latency, and works sufficiently in comparison with certified systems.

scholarly journals Validity and Reliability of Upper Limb Functional Assessment Using the Microsoft Kinect V2 Sensor

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Laisi Cai ◽  
Ye Ma ◽  
Shuping Xiong ◽  
Yanxin Zhang
Keyword(s):  
Upper Limb ◽  
Functional Assessment ◽  
Kinematic Model ◽  
Assessment System ◽  
Microsoft Kinect ◽  
Validity And Reliability ◽  
Retest Reliability ◽  
Kinect V2 ◽  
Flexion Extension ◽  
Test Retest Reliability

Objective. To quantify the concurrent accuracy and the test-retest reliability of a Kinect V2-based upper limb functional assessment system. Approach. Ten healthy males performed a series of upper limb movements, which were measured concurrently with Kinect V2 and the Vicon motion capture system (gold standard). Each participant attended two testing sessions, seven days apart. Four tasks were performed including hand to contralateral shoulder, hand to mouth, combing hair, and hand to back pocket. Upper limb kinematics were calculated using our developed kinematic model and the UWA model for Kinect V2 and Vicon. The interdevice coefficient of multiple correlation (CMC) and the root mean squared error (RMSE) were used to evaluate the validity of the kinematic waveforms. Mean absolute bias and Pearson’s r correlation were used to evaluate the validity of the angles at the points of target achieved (PTA) and the range of motion (ROM). The intersession CMC and RMSE and the intraclass correlation coefficient (ICC) were used to assess the test-retest reliability of Kinect V2. Main Results. Both validity and reliability are found to be task-dependent and plane-dependent. Kinect V2 had good accuracy in measuring shoulder and elbow flexion/extension angular waveforms (CMC>0.87), moderate accuracy of measuring shoulder adduction/abduction angular waveforms (CMC=0.69-0.82), and poor accuracy of measuring shoulder internal/external angles (CMC<0.6). We also found high test-retest reliability of Kinect V2 in most of the upper limb angular waveforms (CMC=0.75-0.99), angles at the PTA (ICC=0.65-0.91), and the ROM (ICC=0.68-0.96). Significance. Kinect V2 has great potential as a low-cost, easy implemented device for assessing upper limb angular waveforms when performing functional tasks. The system is suitable for assessing relative within-person change in upper limb motions over time, such as disease progression or improvement due to intervention.

scholarly journals Development and Validation of a Portable and Inexpensive Tool to Measure the Drop Vertical Jump Using the Microsoft Kinect V2

2017 ◽  
Vol 9 (6) ◽  
pp. 537-544 ◽  
Author(s):  
Aaron D. Gray ◽  
Brad W. Willis ◽  
Marjorie Skubic ◽  
Zhiyu Huo ◽  
Swithin Razu ◽  
...  
Keyword(s):  
Motion Analysis ◽  
Female Athletes ◽  
Vertical Jump ◽  
Intraclass Correlation ◽  
Microsoft Kinect ◽  
Screening Tools ◽  
Initial Contact ◽  
Adolescent Female ◽  
Kinect V2 ◽  
Drop Vertical Jump

Background: Noncontact anterior cruciate ligament (ACL) injury in adolescent female athletes is an increasing problem. The knee-ankle separation ratio (KASR), calculated at initial contact (IC) and peak flexion (PF) during the drop vertical jump (DVJ), is a measure of dynamic knee valgus. The Microsoft Kinect V2 has shown promise as a reliable and valid marker-less motion capture device. Hypothesis: The Kinect V2 will demonstrate good to excellent correlation between KASR results at IC and PF during the DVJ, as compared with a “gold standard” Vicon motion analysis system. Study Design: Descriptive laboratory study. Level of Evidence: Level 2. Methods: Thirty-eight healthy volunteer subjects (20 male, 18 female) performed 5 DVJ trials, simultaneously measured by a Vicon MX-T40S system, 2 AMTI force platforms, and a Kinect V2 with customized software. A total of 190 jumps were completed. The KASR was calculated at IC and PF during the DVJ. The intraclass correlation coefficient (ICC) assessed the degree of KASR agreement between the Kinect and Vicon systems. Results: The ICCs of the Kinect V2 and Vicon KASR at IC and PF were 0.84 and 0.95, respectively, showing excellent agreement between the 2 measures. The Kinect V2 successfully identified the KASR at PF and IC frames in 182 of 190 trials, demonstrating 95.8% reliability. Conclusion: The Kinect V2 demonstrated excellent ICC of the KASR at IC and PF during the DVJ when compared with the Vicon system. A customized Kinect V2 software program demonstrated good reliability in identifying the KASR at IC and PF during the DVJ. Clinical Relevance: Reliable, valid, inexpensive, and efficient screening tools may improve the accessibility of motion analysis assessment of adolescent female athletes.

scholarly journals The feasibility of using Microsoft Kinect v2 sensors during radiotherapy delivery

2016 ◽  
Vol 17 (6) ◽  
pp. 446-453 ◽  
Author(s):  
David M. Edmunds ◽  
Sophie E. Bashforth ◽  
Fatemeh Tahavori ◽  
Kevin Wells ◽  
Ellen M. Donovan
Keyword(s):  
Microsoft Kinect ◽  
Kinect V2

scholarly journals Exploiting Performance of Different Low-Cost Sensors for Small Amplitude Oscillatory Motion Monitoring: Preliminary Comparisons in View of Possible Integration

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Elisa Benedetti ◽  
Roberta Ravanelli ◽  
Monica Moroni ◽  
Andrea Nascetti ◽  
Mattia Crespi
Keyword(s):  
Low Cost ◽  
Software Tool ◽  
Oscillatory Motion ◽  
Microsoft Kinect ◽  
Time Interval ◽  
Gps Receiver ◽  
Mems Accelerometer ◽  
Kinect V2 ◽  
Vibrating Table ◽  
Oscillation Frequencies

We address the problem of low amplitude oscillatory motion detection through different low-cost sensors: a LIS3LV02DQ MEMS accelerometer, a Microsoft Kinect v2 range camera, and a uBlox 6 GPS receiver. Several tests were performed using a one-direction vibrating table with different oscillation frequencies (in the range 1.5–3 Hz) and small challenging amplitudes (0.02 m and 0.03 m). A Mikrotron EoSens high-resolution camera was used to give reference data. A dedicated software tool was developed to retrieve Kinect v2 results. The capabilities of the VADASE algorithm were employed to process uBlox 6 GPS receiver observations. In the investigated time interval (in the order of tens of seconds) the results obtained indicate that displacements were detected with the resolution of fractions of millimeters with MEMS accelerometer and Kinect v2 and few millimeters with uBlox 6. MEMS accelerometer displays the lowest noise but a significant bias, whereas Kinect v2 and uBlox 6 appear more stable. The results suggest the possibility of sensor integration both for indoor (MEMS accelerometer + Kinect v2) and for outdoor (MEMS accelerometer + uBlox 6) applications and seem promising for structural monitoring applications.

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