Validity of microsoft kinectTM for measuring gait parameters

Kharazi, M.R.; Memari, Ashkan; Shahrokhi, Amin; Nabavi, Hoda; Khorami, Sepehr; Rasooli, Anita; Barnamei, H.R.; Jamshidian, A.R.; Mirbagheri, M.M.

doi:10.1109/icbme.2015.7404173

Cited by 24 publications

(11 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy of the Kinect sensor has been a topic of interest in several studies. For example, in [22], Kharazi et al presented a similar study to ours. The authors examined real overground walking.…”

Section: Discussionsupporting

confidence: 83%

“…This effect was produced by the progressive apparition of the subject. For this reason, some studies such as [ 1 , 22 ] kept the subject on their feet within the recording area before starting the walking test, or alternatively, preferred to use treadmills [ 23 , 24 ]. In this study, a dual camera system was chosen to extend the range of view of the camera.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Guffanti

Brunete

Hernando

et al. 2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionsupporting

confidence: 83%

Section: Methodsmentioning

confidence: 99%

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

Guffanti

Brunete

Hernando

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Although several studies evaluated the validity and reliability of the Microsoft Kinect v2 skeleton estimation using markerbased motion capture systems as a golden standard system [19]- [27], none of these studies investigated or reported any distortion in the estimated joint positions. The majority of these studies employed Vicon [19], [20], [33], [34], [21], [26]- [32], BTS SMART-DX [22], [35], or Qualisys [24], [36], [37] motion capture systems that all utilize reflective markers. Microsoft Kinect v2 estimates the depth information based on time-of-flight (ToF) principle using infrared (IR) emitters and detector [38].…”

Section: Introductionmentioning

confidence: 99%

Influence of a Marker-Based Motion Capture System on the Performance of Microsoft Kinect v2 Skeleton Algorithm

et al. 2019

View full text Add to dashboard Cite

Microsoft Kinect sensors are being widely used as low-cost marker-less motion capture systems in various kinematic studies. Previous studies investigated the reliability and validity of Microsoft Kinect sensors by employing marker-based motion capture systems. Both systems employ IR emitters and detectors to track human posture and physical activities. This paper hypothesizes that the motion capture systems may interfere with Microsoft Kinect One sensor and influence the sensor's performance in tracking the skeleton. Hence, this study investigated the impact of a motion capture system on the Microsoft Kinect v2 skeleton algorithm using a mannequin in the presence of eight Qualisys Oqus 300/310 cameras and retroreflective markers. It was found that the motion capture system introduced a destructive impact on the Microsoft Kinect v2 skeleton tracking algorithm. In addition, it was observed that retroreflective markers placed near the joints caused the Microsoft Kinect v2 to give an incorrect reading of estimate the joint position. The motion capture cameras thus caused a timevarying distortion of the Microsoft Kinect estimate of the joint position. It is believed that the inference can be reduced by decreasing the number of markers and avoiding facing the motion capture cameras in sight of Microsoft Kinect v2.

show abstract

“…Unlike other body segments, the foot is located in the anterior–posterior direction of the body. Being accompanied with ground light reflection, tracking errors might occur [53]. According to Dubois et al, a feasible solution for this is to extract gait variables from the geometric centers of depth images, which could partly avoid foot tracking visual occlusions [54,55].…”

Section: Discussionmentioning

confidence: 99%

The Validity and Reliability of a Kinect v2-Based Gait Analysis System for Children with Cerebral Palsy

Mithraratne

Wilson

et al. 2019

Sensors

View full text Add to dashboard Cite

The aim of this study is to evaluate if Kinect is a valid and reliable clinical gait analysis tool for children with cerebral palsy (CP), and whether linear regression and long short-term memory (LSTM) recurrent neural network methods can improve its performance. A gait analysis was conducted on ten children with CP, on two occasions. Lower limb joint kinematics computed from the Kinect and a traditional marker-based Motion Analysis system were investigated by calculating the root mean square errors (RMSE), the coefficients of multiple correlation (CMC), and the intra-class correlation coefficients (ICC2,k). Results showed that the Kinect-based kinematics had an overall modest to poor correlation (CMC—less than 0.001 to 0.70) and an angle pattern similarity with Motion Analysis. After the calibration, RMSE on every degree of freedom decreased. The two calibration methods indicated similar levels of improvement in hip sagittal (CMC—0.81 ± 0.10 vs. 0.75 ± 0.22)/frontal (CMC—0.41 ± 0.35 vs. 0.42 ± 0.37) and knee sagittal kinematics (CMC—0.85±0.07 vs. 0.87 ± 0.12). The hip sagittal (CMC—0.97±0.05) and knee sagittal (CMC—0.88 ± 0.12) angle patterns showed a very good agreement over two days. Modest to excellent reliability (ICC2,k—0.45 to 0.93) for most parameters renders it feasible for observing ongoing changes in gait kinematics.

show abstract

Validity of microsoft kinectTM for measuring gait parameters

Cited by 24 publications

References 13 publications

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

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

Influence of a Marker-Based Motion Capture System on the Performance of Microsoft Kinect v2 Skeleton Algorithm

The Validity and Reliability of a Kinect v2-Based Gait Analysis System for Children with Cerebral Palsy

Contact Info

Product

Resources

About