David J. Brown

Daniel J. Buxton, Mufti Mahmud, Jordan J. Bird et al.

Digital Human Modelling (DHM) is increasingly shaped by advances in AI, wearable biosensing, and interactive digital environments, particularly in research addressing accessibility and inclusion. However, many AI-enabled DHM approaches remain tightly coupled to specific platforms, tasks, or interpretative pipelines, limiting reproducibility, scalability, and ethical reuse. This paper presents a platform-agnostic DHM framework designed to support AI-ready multimodal interaction research by explicitly separating sensing, interaction modelling, and inference readiness. The framework integrates the OpenBCI Galea headset as a unified multimodal sensing layer, providing concurrent EEG, EMG, EOG, PPG, and inertial data streams, alongside a reproducible, game-based interaction environment implemented using SuperTux. Rather than embedding AI models or behavioural inference, physiological signals are represented as structured, temporally aligned observables, enabling downstream AI methods to be applied under appropriate ethical approval. Interaction is modelled using computational task primitives and timestamped event markers, supporting consistent alignment across heterogeneous sensors and platforms. Technical verification via author self-instrumentation confirms data integrity, stream continuity, and synchronisation; no human-subjects evaluation or AI inference is reported. Scalability considerations are discussed with respect to data throughput, latency, and extension to additional sensors or interaction modalities. Illustrative use cases demonstrate how the framework can support AI-enabled DHM and HCI studies, including accessibility-oriented interaction design and adaptive systems research, without requiring architectural modifications. The proposed framework provides an emerging-technology-focused infrastructure for future ethics-approved, inclusive DHM research.

Kieran Woodward, Eiman Kanjo, David J. Brown et al.

Stress is a growing concern in modern society adversely impacting the wider population more than ever before. The accurate inference of stress may result in the possibility for personalised interventions. However, individual differences between people limits the generalisability of machine learning models to infer emotions as people's physiology when experiencing the same emotions widely varies. In addition, it is time consuming and extremely challenging to collect large datasets of individuals' emotions as it relies on users labelling sensor data in real-time for extended periods. We propose the development of a personalised, cross-domain 1D CNN by utilising transfer learning from an initial base model trained using data from 20 participants completing a controlled stressor experiment. By utilising physiological sensors (HR, HRV EDA) embedded within edge computing interfaces that additionally contain a labelling technique, it is possible to collect a small real-world personal dataset that can be used for on-device transfer learning to improve model personalisation and cross-domain performance.

Misbahu S. Zubair, David J. Brown, Matthew Bates et al.

Visual Programming Tools (VPTs) allow users to create interactive media projects such as games and animations using visual representations of programming concepts. Although VPTs have been shown to have huge potential for teaching children with cognitive impairments including those with Autism Spectrum Condition (ASC), research has shown that existing VPTs may not be accessible to them. Therefore, this study proposes a set of recommendations for the design of accessible VPTs for children with ASC. Recommendations were initially gathered and validated by interviewing experts (n=7). The interviews were thematically analysed to identify recommendations. A second set of interviews with a subset of the initial experts (n=3) was then conducted to validate the gathered recommendations. An examination of the available literature was then conducted to identify additional recommendations for the design of VPTs. These recommendations arose from those used for the design of other interactive applications for children with ASC (e.g. virtual environments, serious games) and not identified as part of those the initially gathered from interviews. A novel set of recommendations for the design of VPTs for children with ASC and additional cognitive impairments has been defined as the result of this study.