Soft Robotic Technological Probe for Speculative Fashion Futures

Emerging wearable robotics demand design approaches that address not only function, but also social meaning. In response, we present Sumbrella, a soft robotic garment developed as a speculative fashion probe. We first detail the design and fabrication of the Sumbrella, including sequenced origami-inspired bistable units, fabric pneumatic actuation chambers, cable driven shape morphing mechanisms, computer vision components, and an integrated wearable system comprising a hat and bolero jacket housing power and control electronics. Through a focus group with twelve creative technologists, we then used Sumbrella as a technological probe to explore how people interpreted, interacted, and imagined future relationships with soft robotic wearables. While Sumbrella allowed our participants to engage in rich discussion around speculative futures and expressive potential, it also surfaced concerns about exploitation, surveillance, and the personal risks and societal ethics of embedding biosensing technologies in public life. We contribute to the Human-Robot Interaction (HRI) field key considerations and recommendations for designing soft robotic garments, including the potential for kinesic communication, the impact of such technologies on social dynamics, and the importance of ethical guidelines. Finally, we provide a reflection on our application of speculative design; proposing that it allows HRI researchers to not only consider functionality, but also how wearable robots influence definitions of what is considered acceptable or desirable in public settings.

Towards Personalised Patient Risk Prediction Using Temporal Hospital Data Trajectories

Quantifying a patient's health status provides clinicians with insight into patient risk, and the ability to better triage and manage resources. Early Warning Scores (EWS) are widely deployed to measure overall health status, and risk of adverse outcomes, in hospital patients. However, current EWS are limited both by their lack of personalisation and use of static observations. We propose a pipeline that groups intensive care unit patients by the trajectories of observations data throughout their stay as a basis for the development of personalised risk predictions. Feature importance is considered to provide model explainability. Using the MIMIC-IV dataset, six clusters were identified, capturing differences in disease codes, observations, lengths of admissions and outcomes. Applying the pipeline to data from just the first four hours of each ICU stay assigns the majority of patients to the same cluster as when the entire stay duration is considered. In-hospital mortality prediction models trained on individual clusters had higher F1 score performance in five of the six clusters when compared against the unclustered patient cohort. The pipeline could form the basis of a clinical decision support tool, working to improve the clinical characterisation of risk groups and the early detection of patient deterioration.

TraCE: Trajectory Counterfactual Explanation Scores

Counterfactual explanations, and their associated algorithmic recourse, are typically leveraged to understand, explain, and potentially alter a prediction coming from a black-box classifier. In this paper, we propose to extend the use of counterfactuals to evaluate progress in sequential decision making tasks. To this end, we introduce a model-agnostic modular framework, TraCE (Trajectory Counterfactual Explanation) scores, which is able to distill and condense progress in highly complex scenarios into a single value. We demonstrate TraCE's utility across domains by showcasing its main properties in two case studies spanning healthcare and climate change.

Identification, explanation and clinical evaluation of hospital patient subtypes

We present a pipeline in which unsupervised machine learning techniques are used to automatically identify subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning. In parallel, clinicians assessed intra-cluster similarities and inter-cluster differences of the identified patient subtypes within the context of their clinical knowledge. By confronting the outputs of both automatic and clinician-based explanations, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.