Abstract
AbstractWe describe an approach to human posture classification using RGBD camera (Kinect V2 sensor) data. We compared deep learning methods for human posture classification versus classical data classification methods. We conducted a user study where participants assumed various postures, including whole body, upper and lower body, as well as body transition motion. Several classical data classification methods, such as support vector machine, random forest, neural network, and Adaboost, were used for posture classification. Results show that the posture classification accuracy for the classical data classification methods is between 75% an 99%. The accuracy of the classical data classification methods is comparable to the accuracy of the long-short-term-memory (LSTM) deep learning method which is between 86% and 99%. Our findings suggest that the use of the classical data classification methods on the RGBD camera data is likely sufficient for posture classification, at least for certain task scenarios without incurring the overhead of deep learning.KeywordsComputer visionHuman postureDeep learningMachine learning
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.