مورد إلكتروني
Kinematic-based classification of social gestures and grasping by humans and machine learning techniques
| العنوان: | Kinematic-based classification of social gestures and grasping by humans and machine learning techniques |
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| المؤلفون: | Hemeren, Paul, Veto, Peter, Thill, Serge, Cai, Li, Sun, Jiong |
| بيانات النشر: | Högskolan i Skövde, Institutionen för informationsteknologi Högskolan i Skövde, Forskningsmiljön Informationsteknologi Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Netherlands Pin An Technology Co. Ltd., Shenzhen, China Volvo Cars, Göteborg, Sweden 2021 |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | 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. CC BY 4.0Correspondence: Dr. Paul Hemeren, University of Skövde, Skövde, Sweden, paul.hemeren@his.seThis article is part of the Research Topic Affective Shared Perceptionpublished: 15 October 2021 |
| مصطلحات الفهرس: | gesture recognition, social gestures, machine learning, Biological motion, kinematics, social signal processing, Human Computer Interaction, Människa-datorinteraktion (interaktionsdesign), Robotics, Robotteknik och automation, Article in journal, info:eu-repo/semantics/article, text |
| DOI: | 10.3389.frobt.2021.699505 |
| URL: | Frontiers in Robotics and AI, 2021, 8:308, s. 1-17 |
| الإتاحة: | Open access content. Open access content info:eu-repo/semantics/openAccess |
| ملاحظة: | application/pdf English |
| أرقام أخرى: | UPE oai:DiVA.org:his-20560 0000-0002-1227-6843 0000-0003-1177-4119 doi:10.3389/frobt.2021.699505 PMID 34746242 ISI:000716638700001 Scopus 2-s2.0-85118674941 1280479516 |
| المصدر المساهم: | UPPSALA UNIV LIBR From OAIster®, provided by the OCLC Cooperative. |
| رقم الأكسشن: | edsoai.on1280479516 |
| قاعدة البيانات: | OAIster |
| DOI: | 10.3389.frobt.2021.699505 |
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