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Prediction of human action segmentation based on end-effector kinematics using linear models
Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
2011 (Engelska)Ingår i: European Perspectives on Cognitive Science: Proceedings of the European Conference on Cognitive Science / [ed] Kokinov, B. et al., Sofia: New Bulgarian University Press , 2011, s. 6 sidor-Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The work presented in this paper builds on previous research which analysed human action segmentation in the case of simple object manipulations with the hand (rather than larger-scale actions). When designing algorithms to segment observed actions, for instance to train robots by imitation, the typical approach involves non-linear models but it is less clear whether human action segmentation is also based on such analyses. In the present paper, we therefore explore (1) whether linear models built from observed kinematic variables of a human hand can accurately predict human action segmentation and (2) what kinematic variables are the most important in such a task. In previous work, we recorded speed, acceleration and change in direction for the wrist and the tip of each of the five fingers during the execution of actions as well as the segmentation of these actions into individual components by humans. Here, we use this data to train a large number of models based on every possible training set available and find that, amongst others, the speed of the wrist as well as the change in direction of the index finger were preferred in models with good performance. Overall, the best models achieved R2 values over 0.5 on novel test data but the average performance of trained models was modest. We suggest that this is due to a suboptimal training set (which was not specifically designed for the present task) and that further work be carried out to identify better training sets as our initial results indicate that linear models may indeed be a viable approach to predicting human action segmentation.

Ort, förlag, år, upplaga, sidor
Sofia: New Bulgarian University Press , 2011. s. 6 sidor-
Nyckelord [en]
Action segmentation; Motion primitives; Linear model; Stepwise regression
Nationell ämneskategori
Data- och informationsvetenskap
Forskningsämne
Teknik
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URN: urn:nbn:se:his:diva-5196ISBN: 978-954-535-660-5 OAI: oai:DiVA.org:his-5196DiVA, id: diva2:429230
Tillgänglig från: 2011-07-04 Skapad: 2011-07-04 Senast uppdaterad: 2018-01-12

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http://nbu.bg/cogs/eurocogsci2011/proceedings/pdfs/EuroCogSci-paper195.pdf

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Thill, SergeHemeren, Paul E.Durán, Boris

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