Dipanjan Ghosh, Andrew Olewnik, Kemper Lewis, Junghan Kim and Arun Lakshmanan
J. Mech. Des 139(9), 091401 (Jul 12, 2017); doi: 10.1115/1.4036780
Understanding consumer perceptions of products and the potential impact of those perceptions on purchase decisions is critical information that should influence product development decisions. Though firms often seek consumer feedback on products, such feedback often occurs long after product use and lacks specific details about the interaction, usage context, etc. This work introduces a novel framework – Cyber-Empathic Design – that integrates sensor data and real-time user feedback to develop a more accurate model of user perceptions. The framework is applied to a case study focused on user perceptions of shoes. The results of this work demonstrate the potential for product developers to leverage the IoT (internet-of-things) movement, real-time user feedback, and advances in machine learning to connect user perceptions to specific engineered product features.
Figure: Data collection method (left) and resulting perceptual model (right).
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