150 research outputs found
Search and Delivery of Standardized Learning Resources Based on SOAP Messaging and Native XML Databases
With the progress of Web-based learning technologies, standardized digital repositories and learning management systems are becoming prevalent over time. The heterogeneity in underlying databases and access methods, however, makes it difficult to share and exchange the learning resources between them. In this paper, we propose an architecture for the search and delivery of learning resources. Based on the SOAP transmission protocol, the architecture seeks to improve interoperability between heterogeneous E-Learning implementations. We also present a general-purpose query language as a building block of the architecture. The language provides a unified query interface for resource repositories, thereby shielding the users from the differences in underlying databases and metadata schemas. To highlight our design, an implementation using LOM, native XML database and XPath is presented. The last part of this paper discusses technical and pedagogical issues of concern regarding the launching of contents from within standardized LMSs
User experience for multi-device ecosystems: challenges and opportunities
Smart devices have pervaded every aspect of humans' daily lives. Though single device UX products are relatively successful, the experience of cross-device interaction is still far from satisfactory and can be a source of frustration. Inconsistent UI styles, unclear coordination, varying fidelity, pairwise interactions, lack of understanding intent, limited data sharing and security, and other problems typically degrade the experience in a multi-device ecosystem. Redesigning the UX, tailored to multi-device ecosystems to enhance the user experience, turns out to be challenging but at the same time affording many new opportunities. This workshop brings together researchers, practitioners and developers with different backgrounds, including from fields such as computationally design, affective computing, and multimodal interaction to exchange views, share ideas, and explore future directions on UX for distributed scenarios, especially for those heterogeneous cross-device ecosystems. The topics cover but are not limited to distributed UX design, accessibility, cross-device HCI, human factors in distributed scenarios, user-centric interfaces, and multi-device ecosystems
Facilitating Self-monitored Physical Rehabilitation with Virtual Reality and Haptic feedback
Physical rehabilitation is essential to recovery from joint replacement
operations. As a representation, total knee arthroplasty (TKA) requires
patients to conduct intensive physical exercises to regain the knee's range of
motion and muscle strength. However, current joint replacement physical
rehabilitation methods rely highly on therapists for supervision, and existing
computer-assisted systems lack consideration for enabling self-monitoring,
making at-home physical rehabilitation difficult. In this paper, we
investigated design recommendations that would enable self-monitored
rehabilitation through clinical observations and focus group interviews with
doctors and therapists. With this knowledge, we further explored Virtual
Reality(VR)-based visual presentation and supplemental haptic motion guidance
features in our implementation VReHab, a self-monitored and multimodal physical
rehabilitation system with VR and vibrotactile and pneumatic feedback in a TKA
rehabilitation context. We found that the third point of view real-time
reconstructed motion on a virtual avatar overlaid with the target pose
effectively provides motion awareness and guidance while haptic feedback helps
enhance users' motion accuracy and stability. Finally, we implemented
\systemname to facilitate self-monitored post-operative exercises and validated
its effectiveness through a clinical study with 10 patients
UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language
We introduce UbiPhysio, a milestone framework that delivers fine-grained
action description and feedback in natural language to support people's daily
functioning, fitness, and rehabilitation activities. This expert-like
capability assists users in properly executing actions and maintaining
engagement in remote fitness and rehabilitation programs. Specifically, the
proposed UbiPhysio framework comprises a fine-grained action descriptor and a
knowledge retrieval-enhanced feedback module. The action descriptor translates
action data, represented by a set of biomechanical movement features we
designed based on clinical priors, into textual descriptions of action types
and potential movement patterns. Building on physiotherapeutic domain
knowledge, the feedback module provides clear and engaging expert feedback. We
evaluated UbiPhysio's performance through extensive experiments with data from
104 diverse participants, collected in a home-like setting during 25 types of
everyday activities and exercises. We assessed the quality of the language
output under different tuning strategies using standard benchmarks. We
conducted a user study to gather insights from clinical experts and potential
users on our framework. Our initial tests show promise for deploying UbiPhysio
in real-life settings without specialized devices.Comment: 27 pages, 14 figures, 5 table
MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention
Problematic smartphone use negatively affects physical and mental health.
Despite the wide range of prior research, existing persuasive techniques are
not flexible enough to provide dynamic persuasion content based on users'
physical contexts and mental states. We first conduct a Wizard-of-Oz study
(N=12) and an interview study (N=10) to summarize the mental states behind
problematic smartphone use: boredom, stress, and inertia. This informs our
design of four persuasion strategies: understanding, comforting, evoking, and
scaffolding habits. We leverage large language models (LLMs) to enable the
automatic and dynamic generation of effective persuasion content. We develop
MindShift, a novel LLM-powered problematic smartphone use intervention
technique. MindShift takes users' in-the-moment physical contexts, mental
states, app usage behaviors, users' goals & habits as input, and generates
high-quality and flexible persuasive content with appropriate persuasion
strategies. We conduct a 5-week field experiment (N=25) to compare MindShift
with baseline techniques. The results show that MindShift significantly
improves intervention acceptance rates by 17.8-22.5% and reduces smartphone use
frequency by 12.1-14.4%. Moreover, users have a significant drop in smartphone
addiction scale scores and a rise in self-efficacy. Our study sheds light on
the potential of leveraging LLMs for context-aware persuasion in other behavior
change domains
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