Paper Info
Title
Kinematic-Based Classification of Social Gestures and Grasping by Humans and Machine Learning Techniques
Abstract
The affective motion of humans conveys messages that other humans perceive and understand without conventional linguistic processing. This ability to classify human movement into meaningful gestures or segments plays also a critical role in creating social interaction between humans and robots. In the research presented here, grasping and social gesture recognition by humans and four machine learning techniques (k-Nearest Neighbor, Locality-Sensitive Hashing Forest, Random Forest and Support Vector Machine) is assessed by using human classification data as a reference for evaluating the classification performance of machine learning techniques for thirty hand/arm gestures. The gestures are rated according to the extent of grasping motion on one task and the extent to which the same gestures are perceived as social according to another task. The results indicate that humans clearly rate differently according to the two different tasks. The machine learning techniques provide a similar classification of the actions according to grasping kinematics and social quality. Furthermore, there is a strong association between gesture kinematics and judgments of grasping and the social quality of the hand/arm gestures. Our results support previous research on intention-from-movement understanding that demonstrates the reliance on kinematic information for perceiving the social aspects and intentions in different grasping actions as well as communicative point-light actions.
Year
DOI
Venue
2021
10.3389/frobt.2021.699505
FRONTIERS IN ROBOTICS AND AI
Keywords
DocType
Volume
gesture recognition, social gestures, machine learning, biological motion, kinematics, social signal processing, affective motion
Journal
8
ISSN
Citations 
PageRank 
2296-9144
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Paul Hemeren111.38
Peter Veto200.34
Serge Thill302.37
Cai Li400.34
Jiong Sun500.34