Research Papers

Application of Feature-Learning Methods Toward Product Usage Context Identification and Comfort Prediction

[+] Author and Article Information
Dipanjan Ghosh

Department of Mechanical and
Aerospace Engineering,
805 Furnas Hall,
University at Buffalo—SUNY,
Buffalo, NY 14260
e-mail: dipanjan@buffalo.edu

Andrew Olewnik

Department of Mechanical and
Aerospace Engineering,
412 Bonner Hall,
University at Buffalo—SUNY,
Buffalo, NY 14260
e-mail: olewnik@buffalo.edu

Kemper Lewis

Fellow ASME
Department of Mechanical and
Aerospace Engineering,
208 Bell Hall,
University at Buffalo—SUNY,
Buffalo, NY 14260
e-mail: kelewis@buffalo.edu

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received October 28, 2016; final manuscript received July 11, 2017; published online November 28, 2017. Assoc. Editor: Monica Bordegoni.

J. Comput. Inf. Sci. Eng 18(1), 011004 (Nov 28, 2017) (10 pages) Paper No: JCISE-16-2118; doi: 10.1115/1.4037435 History: Received October 28, 2016; Revised July 11, 2017

Usage context is considered a critical driving factor for customers' product choices. In addition, physical use of a product (i.e., user-product interaction) dictates a number of customer perceptions (e.g., level of comfort). In the emerging internet of things (IoT), this work hypothesizes that it is possible to understand product usage and level of comfort while it is “in-use” by capturing the user-product interaction data. Mining this data to understand both the usage context and the comfort of the user adds new capabilities to product design. There has been tremendous progress in the field of data analytics, but the application in product design is still nascent. In this work, application of feature-learning methods for the identification of product usage context and level of comfort is demonstrated, where usage context is limited to the activity of the user. A novel generic architecture using foundations in convolutional neural network (CNN) is developed and applied to a walking activity classification using smartphone accelerometer data. Results are compared with feature-based machine learning algorithms (neural network and support vector machines (SVM)) and demonstrate the benefits of using the feature-learning methods over the feature-based machine-learning algorithms. To demonstrate the generic nature of the architecture, an application toward comfort level prediction is presented using force sensor data from a sensor-integrated shoe.

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Fig. 1

Relationship of usage context with product preference

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Fig. 2

Traditional and proposed machine learning methodology

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Fig. 3

Machine learning algorithms learning style classification

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Fig. 4

SVM illustrations [24]

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Fig. 5

Simple neuron model

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Fig. 6

Simple neuron model (with input vector)

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Fig. 7

Multilayer neural network

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Fig. 8

CNN illustrations (for images)

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Fig. 9

Proposed CNN architecture for multisource signals

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Fig. 10

Model training and selection procedure

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Fig. 11

Test dataset accuracy comparison—activity recognition

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Fig. 12

Confusion matrices: (a) NN, (b) MC-SVM, and (c) CNN

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Fig. 13

Foot area2, sensor integrated shoe prototype and sensor layout

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Fig. 14

Test dataset accuracy comparison—comfort rating estimation

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Fig. 15

Conceptual integration of usage context with CED framework




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