Artificial Intelligence (AI) and digital devices are becoming more pervasive in the monitoring of individuals’ health as device functionalities increase and their presence in daily life continues to grow. Subsequently, these tools can provide transformative healthcare practice by enabling more personalized, proactive, and engaging interventions. This work presents the design and development of an interactive physical therapy system for adolescents that integrates a wearable EMG leg sensor array with a computer-based video game. The system captures EMG signals during therapeutic exercises and uses them to drive in-game mechanics, providing real-time feedback while also aiming to improve therapeutic engagement. A key feature of the system is its ability to adapt based on individual performance and clinician input, allowing personalized therapy progression and reporting. Using this approach, the system supports continuous personalized assessment and customized intervention for children while promoting motivation through dynamic gameplay. The developed system illustrates a promising direction for designing interactive, AI-augmented health technologies that can enhance pediatric rehabilitation outcomes through accessible, game-based experiences. Further, this work contributes to ongoing research in human-computer interaction, AI, and behavioral health by exploring multisensory interactions, social AI elements, and methods for evaluating user engagement.
Decentralized study settings have been proven to increase efficiency and diversity in clinical trials and research studies. However, one of the biggest challenges of decentralized settings is high dropout rates and low adherence to the study protocol. Gamification has shown to be a promising tool to increase motivation and engagement in different settings, but very few tools exist implementing personalization, which is crucial to address the needs of diverse user contexts. In this paper, we present our vision of using gamification to improve adherence and retention in digital, decentralized research studies and clinical trials. We aim to design, implement and evaluate a modular, AI-based gamification engine designed to personalize motivational strategies based on user profiles and context. Its modular, test-agnostic design approach enables the integration across platforms and study types, offering the potential of a reusable infrastructure for adherence support in digital health research.
The growing global burden of chronic conditions like diabetes and osteoarthritis necessitates innovative, scalable rehabilitation strategies. This paper proposes a conversational AI system designed to deliver dynamic, personalized physical rehabilitation plans. The system leverages large language models (LLMs) and physiotherapist supervision to adapt recommendations based on user feedback and physiological data. A key novelty of this work is its avatar-based AI agent, designed with the potential for integration into extended reality (XR) environments, thus aligning with emerging AR glass technologies. We present an early-stage prototype supported by a modular architecture and propose a pathway toward implementation and evaluation to enhance patient adherence, safety, and engagement in long-term rehabilitation.
Wayfinding is an important skill in the context of everyday life. Especially in unknown environments, spatial orientation in real life relies heavily on landmarks. The present study investigated the impact of outdoor and indoor landmarks in a wayfinding task performed in unknown complex office buildings in Virtual Reality. In order to investigate the performance measures of orientation, twenty-two participants had to find the conference room in the office buildings. The office buildings were constructed in the manner of mazes, incorporating dead ends and loops, and were constructed as two-story buildings. These buildings had no landmarks, indoor landmarks, outdoor landmarks or both types of landmarks. At the end, participants had to draw a digital sketch map to show the way to the conference room. The presence of landmarks led to more accurate sketch maps, in comparison to conditions without landmarks. While a discrepancy in accuracy was observed between the outdoor and the indoor landmark conditions in the data, this discrepancy did not attain statistical significance.
Recent technological advances enable seamlessly changing between different degrees of reality and virtuality using consumer hardware. This so-called cross-reality brings new chances to solve tasks that augmented and virtual reality alone were not fit for, but it also brings new challenges, for example when users need a mental representation of the content that is currently not visible and when choosing and executing transitions between realities. I propose to use context awareness to lower the mental load in such situations and to improve overall user-friendliness. My thesis begins with a literature research to gain an overview of the specific challenges and existing approaches in cross-reality context awareness. From the results, first, a taxonomy will be developed. There will also be a solution in the form of a framework supporting the inclusion of context awareness into cross-reality experiences. The taxonomy will additionally be the base for cross-reality building blocks that can be used in the framework. The results will be evaluated in developer and end-user studies to ensure that the framework is usable and solves the found challenges.
