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  • Journal article
    Lima MR, Wairagkar M, Gupta M, Baena FRY, Barnaghi P, Sharp DJ, Vaidyanathan Ret al., 2022,

    Conversational affective social robots for ageing and dementia support

    , IEEE Transactions on Cognitive and Developmental Systems, Vol: 14, Pages: 1378-1397, ISSN: 2379-8920

    Socially assistive robots (SAR) hold significant potential to assist older adults and people with dementia in human engagement and clinical contexts by supporting mental health and independence at home. While SAR research has recently experienced prolific growth, long-term trust, clinical translation and patient benefit remain immature. Affective human-robot interactions are unresolved and the deployment of robots with conversational abilities is fundamental for robustness and humanrobot engagement. In this paper, we review the state of the art within the past two decades, design trends, and current applications of conversational affective SAR for ageing and dementia support. A horizon scanning of AI voice technology for healthcare, including ubiquitous smart speakers, is further introduced to address current gaps inhibiting home use. We discuss the role of user-centred approaches in the design of voice systems, including the capacity to handle communication breakdowns for effective use by target populations. We summarise the state of development in interactions using speech and natural language processing, which forms a baseline for longitudinal health monitoring and cognitive assessment. Drawing from this foundation, we identify open challenges and propose future directions to advance conversational affective social robots for: 1) user engagement, 2) deployment in real-world settings, and 3) clinical translation.

  • Conference paper
    Zhao Y, Barnaghi P, Haddadi H, 2022,

    Multimodal federated learning on IoT data

    , 2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI), Publisher: IEEE

    Federated learning is proposed as an alternative to centralized machine learning since its client-server structure provides better privacy protection and scalability in real-world applications. In many applications, such as smart homes with Internet-of-Things (IoT) devices, local data on clients are generated from different modalities such as sensory, visual, and audio data. Existing federated learning systems only work on local data from a single modality, which limits the scalability of the systems. In this paper, we propose a multimodal and semi-supervised federated learning framework that trains autoencoders to extract shared or correlated representations from different local data modalities on clients. In addition, we propose a multimodal FedAvg algorithm to aggregate local autoencoders trained on different data modalities. We use the learned global autoencoder for a downstream classification task with the help of auxiliary labelled data on the server. We empirically evaluate our framework on different modalities including sensory data, depth camera videos, and RGB camera videos. Our experimental results demonstrate that introducing data from multiple modalities into federated learning can improve its classification performance. In addition, we can use labelled data from only one modality for supervised learning on the server and apply the learned model to testing data from other modalities to achieve decent F1 scores (e.g., with the best performance being higher than 60%), especially when combining contributions from both unimodal clients and multimodal clients.

  • Journal article
    Wairagkar M, Lima MR, Bazo D, Craig R, Weissbart H, Etoundi AC, Reichenbach T, Iyenger P, Vaswani S, James C, Barnaghi P, Melhuish C, Vaidyanathan Ret al., 2022,

    Emotive response to a hybrid-face robot and translation to consumer social robots

    , IEEE Internet of Things Journal, Vol: 9, Pages: 3174-3188, ISSN: 2327-4662

    We present the conceptual formulation, design, fabrication, control and commercial translation of an IoT enabled social robot as mapped through validation of human emotional response to its affective interactions. The robot design centres on a humanoid hybrid-face that integrates a rigid faceplate with a digital display to simplify conveyance of complex facial movements while providing the impression of three-dimensional depth. We map the emotions of the robot to specific facial feature parameters, characterise recognisability of archetypical facial expressions, and introduce pupil dilation as an additional degree of freedom for emotion conveyance. Human interaction experiments demonstrate the ability to effectively convey emotion from the hybrid-robot face to humans. Conveyance is quantified by studying neurophysiological electroencephalography (EEG) response to perceived emotional information as well as through qualitative interviews. Results demonstrate core hybrid-face robotic expressions can be discriminated by humans (80%+ recognition) and invoke face-sensitive neurophysiological event-related potentials such as N170 and Vertex Positive Potentials in EEG. The hybrid-face robot concept has been modified, implemented, and released by Emotix Inc in the commercial IoT robotic platform Miko (‘My Companion’), an affective robot currently in use for human-robot interaction with children. We demonstrate that human EEG responses to Miko emotions are comparative to that of the hybrid-face robot validating design modifications implemented for large scale distribution. Finally, interviews show above 90% expression recognition rates in our commercial robot. We conclude that simplified hybrid-face abstraction conveys emotions effectively and enhances human-robot interaction.

  • Journal article
    Natarajan N, Vaitheswaran S, Raposo de Lima M, Wairagkar M, Vaidyanathan Ret al., 2022,

    Acceptability of social robots and adaptation of hybrid-face robot for dementia care in India: a qualitative study

    , American Journal of Geriatric Psychiatry, Vol: 30, Pages: 240-245, ISSN: 1064-7481

    ObjectivesThis study aims to understand the acceptability of social robots and the adaptation of the Hybrid-Face Robot for dementia care in India.MethodsWe conducted a focus group discussion and in-depth interviews with persons with dementia (PwD), their caregivers, professionals in the field of dementia, and technical experts in robotics to collect qualitative data.ResultsThis study explored the following themes: Acceptability of Robots in Dementia Care in India, Adaptation of Hybrid-Face Robot and Future of Robots in Dementia Care. Caregivers and PwD were open to the idea of social robot use in dementia care; caregivers perceived it to help with the challenges of caregiving and positively viewed a future with robots.DiscussionThis study is the first of its kind to explore the use of social robots in dementia care in India by highlighting user needs and requirements that determine acceptability and guiding adaptation.

  • Journal article
    Palermo F, Li H, Capstick A, Fletcher-Lloyd N, Zhao Y, Kouchaki S, Nilforooshan R, Sharp D, Barnaghi Pet al., 2021,

    Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data

    Agitation is one of the neuropsychiatric symptoms with high prevalence indementia which can negatively impact the Activities of Daily Living (ADL) andthe independence of individuals. Detecting agitation episodes can assist inproviding People Living with Dementia (PLWD) with early and timelyinterventions. Analysing agitation episodes will also help identify modifiablefactors such as ambient temperature and sleep as possible components causingagitation in an individual. This preliminary study presents a supervisedlearning model to analyse the risk of agitation in PLWD using in-homemonitoring data. The in-home monitoring data includes motion sensors,physiological measurements, and the use of kitchen appliances from 46 homes ofPLWD between April 2019-June 2021. We apply a recurrent deep learning model toidentify agitation episodes validated and recorded by a clinical monitoringteam. We present the experiments to assess the efficacy of the proposed model.The proposed model achieves an average of 79.78% recall, 27.66% precision and37.64% F1 scores when employing the optimal parameters, suggesting a goodability to recognise agitation events. We also discuss using machine learningmodels for analysing the behavioural patterns using continuous monitoring dataand explore clinical applicability and the choices between sensitivity andspecificity in-home monitoring applications.

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  • Finalist: Best Paper - IEEE Transactions on Mechatronics (awarded June 2021)

  • Finalist: IEEE Transactions on Mechatronics; 1 of 5 finalists for Best Paper in Journal

  • Winner: UK Institute of Mechanical Engineers (IMECHE) Healthcare Technologies Early Career Award (awarded June 2021): Awarded to Maria Lima (UKDRI CR&T PhD candidate)

  • Winner: Sony Start-up Acceleration Program (awarded May 2021): Spinout company Serg Tech awarded (1 of 4 companies in all of Europe) a place in Sony corporation start-up boot camp

  • “An Extended Complementary Filter for Full-Body MARG Orientation Estimation” (CR&T authors: S Wilson, R Vaidyanathan)