Aims: Smartphone Augmented Reality (AR) enables social applications, but single-point Inverse Kinematics (IK) avatar tracking limits pose expressiveness and introduces positional errors. We introduce DPIK, a dual-point tracking system combining hand IK with face tracking, and evaluated whether it improves tracking accuracy, embodiment, and user experience over single-point IK.

Methods: DPIK uses the front and back cameras of ARKit to track the face of the user and hand/device pose, driving avatar hand IK and hip/head positioning. In a within-subjects study (N=20), we compared DPIK against our prior single-point IK system (ARIKA) and full-body motion capture (ground truth) across positional/rotational accuracy, embodiment, and user experience. We also examined temporal drift in tracking error and Spearman correlations between error and embodiment/user experience scores.

Results: DPIK significantly outperformed ARIKA in positional accuracy for 18 of 19 joints and reduced head rotational error by over . A temporal drift analysis found that neither ARIKA nor DPIK exhibited statistically significant growth in positional error over time. DPIK received significantly higher overall user experience ratings than ARIKA. Embodiment differences were not statistically significant, though effect sizes were moderate-to-large. Exploratory correlations suggested lower DPIK embodiment scores for Acceptance and Control with higher positional error, but these did not survive multiple-comparison correction; positional error did not predict user experience ratings for either technique.

Conclusion: DPIK improves tracking accuracy and user experience over ARIKA, with tracking fidelity selectively shaping embodiment rather than overall user experience, highlighting a distinction between perceptual ownership and holistic evaluation of smartphone AR avatar systems.

Population ageing is increasing the demand for technologies that support independent living while addressing the growing complexity of cognitive decline, caregiving, and long-term home-based care. Despite the fact that ambient assisted living (AAL) and extended reality (XR) technologies have independently demonstrated considerable potential, both approaches remain fragmented in their technological paradigm: AAL offers constant environmental support but is lacking in terms of cognitive and social stimulation, and XR provides immersive therapeutic experiences that are not integrated into everyday routine. This paper introduces the Ambient XR Framework, a conceptual architecture for the relational orchestration that integrates ambient intelligence and immersive technologies centred on the resident-caregiver dyad. The concept does not intend to replace current assistive technologies but rather introduces immersive interaction as a context-aware feature of the ambient care environment. It describes an operational workflow, three design principles, a multidimensional design space, and an evaluation roadmap and incorporates governance, consent, and ethical considerations in the architectural framework. Three exemplar research directions, such as ambient memory support, memory co-creation, and skill rehearsal for older adults with mild cognitive impairment, showcase how the framework can support continuity of daily life and immersive experiences. The Ambient XR Framework can serve as a design ground for creating next-generation, human-centered Ambient XR ecosystems for the ageing population.

Aims: Multimodal emotion recognition (MER) can outperform unimodal approaches by integrating complementary information from multiple sources. However, real-world applications often involve incomplete or missing modalities, reducing the reliability of existing MER models. This study proposes a framework that remains robust under missing-modality conditions while preserving the advantages of multimodal integration.

Methods: To address this challenge, we propose a cross-modal latent regeneration and attention long short-term memory (CMLR-ALSTM) framework. The framework combines pretrained variational autoencoder encoders with residual projection networks, optimized with L2 loss, to achieve stable latent-space alignment across modalities. Regenerated and available latent representations are integrated using a cross-modal attention mechanism and processed by an LSTM to model temporal dependencies and improve multimodal fusion under incomplete data.

Results: The proposed framework was evaluated on three benchmark datasets under complete, partially missing, and completely missing modality scenarios. Experimental results demonstrate that CMLR-ALSTM achieves up to 17.22% improvement under missing-modality conditions while maintaining competitive performance under complete-modality settings. The results also show that the proposed latent regeneration strategy effectively preserves cross-modal relationships and robust latent representations.

Conclusion: The experimental results confirm the effectiveness of the proposed framework, particularly in realistic environments where data availability is inconsistent. By leveraging CMLR to reconstruct missing representations and modelling temporal dependencies through LSTM, the proposed approach provides a more robust and reliable MER framework for practical deployment. The results across different modality combinations demonstrate its ability to generalize across heterogeneous multimodal settings.

Aims: Mental illnesses affect millions worldwide. Despite greater public awareness, significant stigma remains. Anti-stigmatization interventions can help reduce prejudice by promoting education and meaningful contact with people with mental illness. Technology-based interventions may further support these efforts by simulating such contact through approaches such as storytelling. Social robots may offer additional advantages for storytelling-based interventions, especially through the integration of music and sound effects.

Methods: In multimodal robotic storytelling, non-speech sounds remain largely overlooked, despite their importance in related media. To address this gap, we compared a robotic storyteller narrating a story about a person experiencing a panic attack using only voice and bodily expression with versions integrating sound effects, background music, or both.

Results: While the addition of sounds did not affect recipients’ prejudice differently than storytelling without them, adding both sound effects and music increased transportation and improved associative empathy. In contrast, adding only music decreased associative empathy. Although mediation was not indicated, the results revealed transportation as a predictor for empathy and prejudice, while being only minimally influenced by sound integration itself.

Conclusion: The effects of sound integration in a robotic storytelling intervention on recipients’ prejudice were mixed, recommending either the combination of both sound types or complete omission. Transportation was indicated as a key lever for increasing empathy and decreasing prejudice that warrants further investigation. Future work is needed to gain deeper insights into robotic storytelling as an intervention tool for reducing prejudice, including work on transportation as a modifiable factor and the integration of pre-post-measurements.