Empowerment is crucial for eliciting designer empathy. This research explores a distinctive integration of empathic modelling and role-play, termed Empathic Empowerment. Through a qualitative study, this research introduces a novel evaluation system, entitled the Empathic Empowerment Scale, designed to support the optimal level of designer empathy in a situated interaction drawn from habitual user experiences. The goal of this research is to empower designers to create the next generation of human-oriented solutions with enhanced inclusivity and social value, through practical experiences.

Empathic design highlights the relevance of understanding users and their circumstances in order to obtain good design outcomes. However, theory-based quantitative methods, which can be used to test user understanding, are hard to find in the design science literature. Here, we introduce a validated method used in social psychological research – the empathic accuracy method – into design to explore how well two designers perform in a design task and whether the designers’ empathic accuracy performance and the physiological synchrony between the two designers and a group of users can predict the designers’ success in two design tasks. The designers could correctly identify approximately 50% of the users’ reported mental content. We did not find a significant correlation between the designers’ empathic accuracy and their (1) performance in design tasks and (2) physiological synchrony with users. Nevertheless, the empathic accuracy method is promising in its attempts to quantify the effect of empathy in design.

A critical task in product design is mapping information from the consumer space to the design space. This process is largely dependent on the designer to identify and relate psychological and consumer level factors to engineered product attributes. In this way, current methodologies lack provision to test a designer’s cognitive reasoning and may introduce bias through the mapping process. Prior work on Cyber-Empathic Design (CED) supports this mapping by relating user–product interaction data from embedded sensors to psychological constructs. To understand consumer perceptions, a network of psychological constructs is developed using Structural Equation Modeling for parameter estimation and hypothesis testing, making the framework falsifiable in nature. The focus of this technical brief is toward automating CED through unsupervised deep learning to extract features from raw data. Additionally, Partial Least Square Structural Equation Modeling is used with extracted sensor features as inputs. To demonstrate the effectiveness of the approach a case study involving sensor-integrated shoes compares three models – a survey-only model (no sensor data), the existing CED approach with manually extracted sensor features, and the proposed deep learning based CED approach. The deep learning based approach results in improved model fit.