+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Evaluation of the microsoft kinect skeletal versus depth data analysis for timed-up and go and figure of 8 walk tests

Evaluation of the microsoft kinect skeletal versus depth data analysis for timed-up and go and figure of 8 walk tests

Conference Proceedings 2016: 2274-2277

We compared the performance of the Kinect skeletal data with the Kinect depth data in capturing different gait parameters during the Timed-up and Go Test (TUG) and Figure of 8 Walk Test (F8W). The gait parameters considered were stride length, stride time, and walking speed for the TUG, and number of steps and completion time for the F8W. A marker-based Vicon motion capture system was used for the ground-truth measurements. Five healthy participants were recruited for the experiment and were asked to perform three trials of each task. Results show that depth data analysis yields stride length and stride time measures with significantly low percentile errors as compared to the skeletal data analysis. However, the skeletal and depth data performed similar with less than 3% of absolute mean percentile error in determining the walking speed for the TUG and both parameters of F8W. The results show potential capabilities of Kinect depth data analysis in computing many gait parameters, whereas, the Kinect skeletal data can also be used for walking speed in TUG and F8W gait parameters.

Please choose payment method:

(PDF emailed within 0-6 h: $19.90)

Accession: 059708750

Download citation: RISBibTeXText

PMID: 28268781

DOI: 10.1109/embc.2016.7591183

Related references

SU-E-I-92: Accuracy Evaluation of Depth Data in Microsoft Kinect. Medical Physics 39(6part5): 3646, 2012

Automated analysis of gait and modified timed up and go using the Microsoft Kinect in people with Parkinson's disease: associations with physical outcome measures. Medical and Biological Engineering and Computing 57(2): 369-377, 2019

Statistical analysis-based error models for the Microsoft Kinect(TM) depth sensor. Sensors 14(9): 17430-17450, 2014

Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis. Sensors 16(7):, 2016

Patient walk detection in hospital room using Microsoft Kinect V2. Conference Proceedings 2016: 4395-4398, 2016

Clinical relevance using timed walk tests and 'timed up and go' testing in persons with multiple sclerosis. PhysioTherapy Research International 12(2): 105-114, 2007

Evaluation of the Microsoft Kinect for screening ACL injury. Conference Proceedings 2013: 4152-4155, 2013

Evaluation of foot posture using the Microsoft Kinect. Journal of Science and Medicine in Sport 16: E24-E25, 2013

An evaluation of 3D head pose estimation using the Microsoft Kinect v2. Gait and Posture 48: 83-88, 2016

Using Data From the Microsoft Kinect 2 to Quantify Upper Limb Behavior: A Feasibility Study. IEEE Journal of Biomedical and Health Informatics 21(5): 1386-1392, 2017

Using data from the Microsoft Kinect 2 to determine postural stability in healthy subjects: A feasibility trial. Plos one 12(2): E0170890, 2017

A technological evaluation of the Microsoft Kinect for automated behavioural mapping at bed rest. Studies in Health Technology and Informatics 188: 39-45, 2013

Evaluation of the Microsoft Kinect as a clinical assessment tool of body sway. Gait and Posture 40(4): 532-538, 2014

Comparative analysis of respiratory motion tracking using Microsoft Kinect v2 sensor. Journal of Applied Clinical Medical Physics 19(3): 193-204, 2018

Performance analysis of a generalized motion capture system using microsoft kinect 2.0. Biomedical Signal Processing and Control 38: 265-280, 2017