Emotion Tracking for Health, Memory, and Well-Being

In this talk I will describe novel systems that allow users to reflect upon their moods and learn positive coping strategies for dealing with stress and depression. I will also describe systems and applications that perform mood detection in real time using mobile and wearable technology. We are exploring novel user interface applications to help users reflect upon and manage their affective experiences. Many questions remain from our work, in terms of how useful a system like this would be over the long term and how valuable a personalized, mobile, awareness system is. Finally, we feel that there is a huge opportunity in the remote familial space, or in a close social network, where knowing about the emotional health of separated loved ones or close friends comes in to play. These new research areas are also tightly coupled with privacy issues. A few examples of applications in these new areas will be presented

Human factors involvement in bringing the power of AI to a heterogeneous user population

The Human Factors involvement in developing COMPAQ QuickSolve, an electronic problem-solving and information system for Compaq's line of networked printers, is described. Empowering customers with expert system technology so they could solve advanced networked printer problems on their own was a major goal in designing this system. This process would minimize customer down-time, reduce the number of phone calls to the Compaq Customer Support Center, improve customer satisfaction, and, most importantly, differentiate Compaq printers in the marketplace by providing the best, and most technologically advanced, customer support. This represents a re-engineering of Compaq's customer support strategy and implementation. In its first generation system, SMART, the objective was to provide expert knowledge to Compaq's help desk operation to more quickly and correctly answer customer questions and problems. QuickSolve is a second generation system in that customer support is put directly in the hands of the consumers. As a result, the design of QuickSolve presented a number of challenging issues. Because the produce would be used by a diverse and heterogeneous set of users, a significant amount of human factors research and analysis was required while designing and implementing the system. Research that shaped the organization and design of the expert system component as well

HCI Research Transfer to Practice: Better Together

Currently, HCI researchers and HCI practitioners work in relatively separate spheres of influence. Practitioners often question the value of academic HCI research and desire more practical directions. HCI researchers often wonder if their research findings are communicated via the optimal channels for influencing practitioners’ process and direction, or whether their results generalize to the real workaday world of HCI. This panel attempts to outline what practitioners need from their academic partners, and how they think these needs can be addressed by academic research. Academics on the panel will state what they see as interesting future research challenges, and whether or how they think they can address the practitioner community’s interests. The practitioners on the panel will then state their opinions about the opportunities for technology transfer from academia to practice

DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization

Facial action unit recognition has many applications from market research to psychotherapy and from image captioning to entertainment. Despite its recent progress, deployment of these models has been impeded due to their limited generalization to unseen people and demographics. This work conducts an in-depth analysis of performance across several dimensions: individuals(40 subjects), genders (male and female), skin types (darker and lighter), and databases (BP4D and DISFA). To help suppress the variance in data, we use the notion of self-supervised denoising autoencoders to design a method for deep face normalization(DeepFN) that transfers facial expressions of different people onto a common facial template which is then used to train and evaluate facial action recognition models. We show that person-independent models yield significantly lower performance (55% average F1 and accuracy across 40 subjects) than person-dependent models (60.3%), leading to a generalization gap of 5.3%. However, normalizing the data with the newly introduced DeepFN significantly increased the performance of person-independent models (59.6%), effectively reducing the gap. Similarly, we observed generalization gaps when considering gender (2.4%), skin type (5.3%), and dataset (9.4%), which were significantly reduced with the use of DeepFN. These findings represent an important step towards the creation of more generalizable facial action unit recognition systems

Understanding Perceptions of Problematic Facebook Use: When People Experience Negative Life Impact and a Lack of Control

While many people use social network sites to connect with friends and family, some feel that their use is problematic, seriously affecting their sleep, work, or life. Pairing a survey of 20,000 Facebook users measuring perceptions of problematic use with behavioral and demographic data, we examined Facebook activities associated with problematic use as well as the kinds of people most likely to experience it. People who feel their use is problematic are more likely to be younger, male, and going through a major life event such as a breakup. They spend more time on the platform, particularly at night, and spend proportionally more time looking at profiles and less time browsing their News Feeds. They also message their friends more frequently. While they are more likely to respond to notifications, they are also more likely to deactivate their accounts, perhaps in an effort to better manage their time. Further, they are more likely to have seen content about social media or phone addiction. Notably, people reporting problematic use rate the site as more valuable to them, highlighting the complex relationship between technology use and well-being. A better understanding of problematic Facebook use can inform the design of context-appropriate and supportive tools to help people become more in control.Comment: CHI 201

Affective Conversational Agents: Understanding Expectations and Personal Influences

The rise of AI conversational agents has broadened opportunities to enhance human capabilities across various domains. As these agents become more prevalent, it is crucial to investigate the impact of different affective abilities on their performance and user experience. In this study, we surveyed 745 respondents to understand the expectations and preferences regarding affective skills in various applications. Specifically, we assessed preferences concerning AI agents that can perceive, respond to, and simulate emotions across 32 distinct scenarios. Our results indicate a preference for scenarios that involve human interaction, emotional support, and creative tasks, with influences from factors such as emotional reappraisal and personality traits. Overall, the desired affective skills in AI agents depend largely on the application's context and nature, emphasizing the need for adaptability and context-awareness in the design of affective AI conversational agents