An Unsupervised Machine Learning Approach To Assessing Designer Performance During Physical Prototyping

[+] Author and Article Information
Matthew Dering

Computer Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16801

Conrad Tucker

Industrial Engineering, Pennsylvania State University, University Park, Pennsylvania 16801

Soundar Kumara

Industrial Engineering, Pennsylvania State University, University Park, Pennsylvania 16801

1Corresponding author.

ASME doi:10.1115/1.4037434 History: Received September 01, 2016; Revised July 12, 2017


An important part of the Engineering Design process is prototyping, where designers build and test their designs. Current methods for reducing time spent during the prototyping process have focused primarily on optimizing designer to designer interactions, as opposed to designer to tool interactions. Advancements in commercially-available sensing systems (e.g., the Kinect) and machine learning algorithms have opened the pathway towards real-time observation of designers behavior in engineering workspaces during prototype construction. Towards this end, this work hypothesizes that an object O being used for task i is distinguishable from object O being used for task j, where i is the correct task and j is the incorrect task. The contributions of this work are i) the ability to recognize these objects in a free roaming engineering workshop environment and ii) the ability to distinguish between the correct and incorrect use of objects used during a prototyping task. By distinguishing the difference between correct and incorrect uses, incorrect behavior can be detected and quickly corrected. A case study is presented involving participants in an engineering design workshop to demonstrate the effectiveness of the proposed methodology, by asking participants to perform correct and incorrect tasks with a tool. The participants' movements are analyzed by an unsupervised clustering algorithm to determine if there is a statistical difference between tasks being performed correctly and incorrectly. Clusters which are a plurality incorrect are found to be significantly distinct for each node considered by the methodology, each with p << 0.001.

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