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Online Predictions of Human Motion
KTH, School of Engineering Sciences (SCI).
KTH, School of Engineering Sciences (SCI).
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Collaboration between humans and robots is becoming an increasingly commonoccurrence in both industry and homes, more so with every forthcomingtechnological advance. This paper examines the possibilities of performinghuman hand movement predictions on the fly, e.g. by only using informationup to the specific moment in time of which the prediction is carried out.Specifically, data will be collected using a Kinect (v.1).The model used for the predictor developed is the Minimum Jerk model,which states that certain multi-joint reaching movements are planned in sucha way that the hand is to follow a straight path while maximizing smoothness.Extent, direction and duration of the motion are main objectives for thepredictor to determine, with a Kalman filter and curve fitting as the mainconstituents. Another assumption in this work is that a reliable start detectoris available. An experiment where five volunteers were to perform differentreaching movements was conducted.This study shows that the approach is feasible in some cases, namelyusable predictions is acquired for long movements. In the case of shortmovements the alternative of not doing any prediction was by all meansbetter.

Place, publisher, year, edition, pages
2017. , 27 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-210845OAI: oai:DiVA.org:kth-210845DiVA: diva2:1120459
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Examiners
Available from: 2017-07-06 Created: 2017-07-06 Last updated: 2017-07-06Bibliographically approved

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CiteExportLink to record
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  • apa
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Output format
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