A Human-centered Augmentation Framework: HCI Methodologies
for Designing and Evaluating Bodily, Perceptual, and Socio-relational Capabilities

Name(Affiliation): Jihwan Kim (Department of Computer Science)

Date / Location: June 18, 2026, 15:00~16:00 / ITBT 501

Human augmentation has become a central research direction in human–computer interaction (HCI) as interactive systems increasingly support, extend, or mediate human capabilities through bodily interfaces, immersive environments, robotic systems, and intelligent agents. However, augmentation should not be evaluated only by whether it improves performance, efficiency, or task outcomes. A technological intervention may enhance measurable capability while weakening the experiential conditions that allow the supported capability to remain meaningful, controllable, comfortable, trustworthy, and acceptable to the user. This thesis addresses this issue by deriving a human-centered augmentation framework from a cross-case synthesis of three empirical HCI studies on bodily, perceptual, and socio-relational augmentation.

The first study examines embodied electrical muscle stimulation (EMS) as bodily augmentation. Electrical muscle stimulation can improve rapid motor response by inducing preemptive muscle activation, but direct bodily actuation may also reduce the user’s sense of agency. Through two experiments, the study identified an EMS timing sweet spot that improved reaction time while preserving agency and applied this timing to a serious-game-based pistol-shooting scenario. The results showed that averagely and individually embodied EMS reduced reaction time without compromising agency. They further indicated that individual calibration can benefit users whose agency-preserving timing differs from the group average.

The second study examines individualized foveated rendering (IFR) as perceptual augmentation. In real-time virtual reality (VR), rendering constraints can threaten the stability and quality of visual experience when systems cannot update visual information efficiently. Foveated rendering (FR) can reduce computational burden by lowering peripheral visual quality based on the characteristics of human vision, but non-personalized rendering parameters may degrade visual experience for some users. Through three experiments, the study measured users’ central visual area and peripheral resolution threshold and evaluated IFR in both controlled visual evaluation and VR first-person-shooter contexts. The results indicated that IFR maintained virtual experience while reducing rendering resources, especially for users who required a larger central high-quality area. These findings further suggest that individualization should be combined with safeguards against overly aggressive perceptual optimization.

The third study examines affective and cognitive feedback from a humanoid robot as socio-relational augmentation. In human–robot collaboration, failures attributed to the human collaborator can threaten not only task progress, but also confidence, social connectedness, and willingness to continue collaboration. The study compared success, no feedback, affective feedback, and cognitive feedback conditions in a collaborative block-stacking task with either a robot or human collaborator. The results indicated that both affective and cognitive feedback improved teamwork quality, perceived copresence, and intimacy compared with no feedback. Affective feedback produced stronger socio-relational benefits, particularly for copresence and intimacy, whereas cognitive feedback helped users understand the failure situation, adjust subsequent action, and continue the task with clearer guidance.

The cross-case synthesis derives three core elements of the human-centered augmentation framework from the specific tensions observed across the three studies. In the EMS study, rapid response became valuable only when the stimulated action remained compatible with the user’s sense of agency. In the IFR study, rendering reduction became valuable only when visual experience and task behavior were maintained. In the robot feedback study, post-failure support became valuable only when teamwork quality, copresence, and intimacy were preserved. These cases led to three analytic elements: capability gap, augmentation level, and experience boundary. The framework uses these elements to distinguish whether an intervention provides too little support, crosses the relevant experiential boundary, or achieves an appropriate balance between capability support and experience preservation.

This thesis contributes to HCI by reframing human augmentation as a question of appropriateness rather than maximization. The framework is not intended to replace domain-specific measures such as reaction time, visual quality, or teamwork quality. Instead, it clarifies how such measures can be interpreted together when an augmentation system changes both capability and experience. In this sense, user experience is treated as a validity condition for human-centered augmentation rather than as a secondary outcome after technical success. The framework can therefore serve as a methodological basis for future augmentation research that seeks to support human capability without weakening the experience through which that capability remains meaningful to the user.