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research-article

APPLICATION OF FEATURE-LEARNING METHODS TOWARDS PRODUCT USAGE CONTEXT IDENTIFICATION AND COMFORT PREDICTION

[+] Author and Article Information
Dipanjan Ghosh

Graduate Research Assistant, Department of Mechanical and Aerospace Engineering, 805 Furnas Hall, University at Buffalo - Suny, Buffalo, New York - 14260
dipanjan@buffalo.edu

Andrew Olewnik

ASME Member Research Assistant Professor, Department of Mechanical and Aerospace Engineering, 412 Bonner Hall, University at Buffalo - Suny, Buffalo, New York - 14260
olewnik@buffalo.edu

Kemper Lewis

ASME Fellow, Professor, Department of Mechanical and Aerospace Engineering, 208 Bonner Hall, University at Buffalo - Suny, Buffalo, New York - 14260
kelewis@buffalo.edu

1Corresponding author.

ASME doi:10.1115/1.4037435 History: Received October 28, 2016; Revised July 11, 2017

Abstract

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 the data and understanding the usage context along with comfort of the user adds a new dimension to the product design field. 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 Convolution Neural Network has been developed and applied for a walking activity classification using smartphone accelerometer data. Results are compared with feature-based machine learning algorithms (neural network and support vector machines), 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 towards comfort level prediction is presented using force sensor data of a sensor-integrated shoe.

Copyright (c) 2017 by ASME
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