Reward Learning with Trees:Methods and Evaluation

Recent efforts to learn reward functions from human feedback have tended to use deep neural networks, whose lack of transparency hampers our ability to explain agent behaviour or verify alignment. We explore the merits of learning intrinsically interpretable tree models instead. We develop a recently proposed method for learning reward trees from preference labels, and show it to be broadly competitive with neural networks on challenging high-dimensional tasks, with good robustness to limited or corrupted data. Having found that reward tree learning can be done effectively in complex settings, we then consider why it should be used, demonstrating that the interpretable reward structure gives significant scope for traceability, verification and explanation

Digital Mental Health and Social Connectedness

A detailed understanding of the mental health needs of people from refugee backgrounds is crucial for the design of inclusive mental health technologies. We present a qualitative account of the digital mental health experiences of women from refugee backgrounds. Working with community members and community workers of a charitable organisation for refugee women in the UK, we identify social and structural challenges, including loneliness and access to mental health technologies. Participants' accounts document their collective agency in addressing these challenges and supporting social connectedness and personal wellbeing in daily life: participants reported taking part in community activities as volunteers, sharing technological expertise, and using a wide range of non-mental health-focused technologies to support their mental health, from playing games to supporting religious practices. Our findings suggest that, rather than focusing only on individual self-care, research also needs to leverage community-driven approaches to foster social mental health experiences, from altruism to connectedness and belonging

Explanation before Adoption: Supporting Informed Consent for Complex Machine Learning and IoT Health Platforms

Explaining health technology platforms to non-technical members of the public is an important part of the process of informed consent. Complex technology platforms that deal with safety-critical areas are particularly challenging, often operating within private domains (e.g. health services within the home) and used by individuals with various understandings of hardware, software, and algorithmic design. Through two studies, the first an interview and the second an observational study, we questioned how experts (e.g. those who designed, built, and installed a technology platform) supported provision of informed consent by participants. We identify a wide range of tools, techniques, and adaptations used by experts to explain the complex SPHERE sensor-based home health platform, provide implications for the design of tools to aid explanations, suggest opportunities for interactive explanations, present the range of information needed, and indicate future research possibilities in communicating technology platforms

Early reconstitution of effector memory CD4+ CMV-specific T cells protects against CMV reactivation following allogeneic SCT.

Reactivation of CMV is a common complication following allogeneic haematopoietic SCT and is associated with significant morbidity and mortality. The relative importance of the CD4+ and CD8+ components of the CMV-specific immune response in protection from reactivation is unclear. The CMV-specific CD4+ and CD8+ immune response was measured at serial time points in 32 patients following allogeneic HSCT. Intracellular cytokine staining following CMV lysate stimulation and HLA-peptide tetramers were used to determine CMV-specific CD4+ and CD8+ responses, respectively. A deficient CMV-specific CD4+ T-cell immune response within the first 30-50 days post transplant was associated with high risk of viral reactivation. Patients with combined impairment of the CD4+ and CD8+ immune response within the first 100 days were susceptible to late viral reactivation. The frequency of CMV-specific CD4+ T cells correlated with CMV-specific CD8+ T cells, comprising 10% of the whole T-cell repertoire. Early CMV-specific CD4+ T-cell reconstitution was dominated by effector memory cells with normal levels of IL-2 resuming 6 months following transplantation. In summary, both CD4 and CD8 CMV-specific immune reconstitution is required for protection from recurrent activation. Measurement of the magnitude of the CMV-specific CD4+ immune response is useful in managing viral reactivation following HSCT

An Initial Usability Evaluation of the Secure Situation Awareness System

The importance of situation awareness systems in crisis-management scenarios cannot be emphasised enough. These systems enable entire disaster situations to be mapped out in a real-time fashion thereby aiding significantly in human decision-making and the necessary positioning, management and deployment of resources. As a result of the core role these systems play in responding to crises, it is vital that they are highly usable and optimized for human cognition and experience. In this paper we consider this reality in the context of an initial evaluation of the visualisation interface of a situation-awareness tool called Secure Situation Awareness (SSA). Our evaluation seeks to gather useful feedback from potential end-users on the usability of the tool’s interface to feed into the design and development of interfaces for similar systems

Generalized and efficient skill assessment from IMU data with applications in gymnastics and medical training

Human activity recognition is progressing from automatically determining what a person is doing and when, to additionally analyzing the quality of these activities—typically referred to as skill assessment. In this chapter, we propose a new framework for skill assessment that generalizes across application domains and can be deployed for near-real-time applications. It is based on the notion of repeatability of activities defining skill. The analysis is based on two subsequent classification steps that analyze (1) movements or activities and (2) their qualities, that is, the actual skills of a human performing them. The first classifier is trained in either a supervised or unsupervised manner and provides confidence scores, which are then used for assessing skills. We evaluate the proposed method in two scenarios: gymnastics and surgical skill training of medical students. We demonstrate both the overall effectiveness and efficiency of the generalized assessment method, especially compared to previous work

Protocol for PD SENSORS:Parkinson’s Disease Symptom Evaluation in a Naturalistic Setting producing Outcomes measuRes using SPHERE technology. An observational feasibility study of multi-modal multi-sensor technology to measure symptoms and activities of daily living in Parkinson’s disease

Introduction The impact of disease-modifying agents on disease progression in Parkinson’s disease is largely assessed in clinical trials using clinical rating scales. These scales have drawbacks in terms of their ability to capture the fluctuating nature of symptoms while living in a naturalistic environment. The SPHERE (Sensor Platform for HEalthcare in a Residential Environment) project has designed a multi-sensor platform with multimodal devices designed to allow continuous, relatively inexpensive, unobtrusive sensing of motor, non-motor and activities of daily living metrics in a home or a home-like environment. The aim of this study is to evaluate how the SPHERE technology can measure aspects of Parkinson’s disease.Methods and analysis This is a small-scale feasibility and acceptability study during which 12 pairs of participants (comprising a person with Parkinson’s and a healthy control participant) will stay and live freely for 5 days in a home-like environment embedded with SPHERE technology including environmental, appliance monitoring, wrist-worn accelerometry and camera sensors. These data will be collected alongside clinical rating scales, participant diary entries and expert clinician annotations of colour video images. Machine learning will be used to look for a signal to discriminate between Parkinson’s disease and control, and between Parkinson’s disease symptoms ‘on’ and ‘off’ medications. Additional outcome measures including bradykinesia, activity level, sleep parameters and some activities of daily living will be explored. Acceptability of the technology will be evaluated qualitatively using semi-structured interviews.Ethics and dissemination Ethical approval has been given to commence this study; the results will be disseminated as widely as appropriate