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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.

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• Journal article
  Kolanko MA, Soreq E, Lai H, Barnaghi P, Dijk D-J, Sharp DJet al., 2022,

  Clinically relevant monitoring of long-term night-time behaviour and physiology from the homes of people living with dementia.

  , Alzheimers Dement, Vol: 18 Suppl 2

  BACKGROUND: Disturbances of sleep and night-time behaviours are amongst the most disabling symptoms of dementia. They often increase carers' burden and the risk of institutionalization. The causes are complex and are difficult to investigate because of a lack of acceptable methods for monitoring behaviours in the home. Here we show that a passive under-mattress can be used to track changes in night-time behaviour and physiology, and that a range of digital biomarkers produced are informative in understanding the effects of medication changes, disease progression and intercurrent illness in patients living with dementia (PLWD). METHOD: We used contactless Withings Sleep Mattress (WSM) to monitor bed-occupancy in 4 PLWD (age 74-93, 3males) enrolled into the CR&T MINDER cohort study. Each participant was tracked over 1000 nights between 2019 and 2021. Minute-to-minute timeseries were extracted from WSM to calculate bed occupancy metrics and nocturnal physiology measures (heart and breathing rates (HR/BR)). Raw measures were standardized within subjects by comparing each time point to the mean of the time points that preceded it. We then investigated the relationship between these metrics and clinical events such as infections and medication. RESULT: The 4 case studies illustrate the potential of this technology for passive health monitoring in PLWD. High levels of intraindividual variability in behavioural and physiological metrics were observed. Progressive changes in bed occupancy were observed in two patients with frontotemporal dementia and Alzheimer's disease (Cases 1&2). Intercurrent illness and medications changes influenced the measures. For example, Patient 1 showed progressive night-time wandering with increasing time spent out of bed, which improved following the initiation of risperidone. Case 2 showed recurrent episodes of heart failure accompanied by increased nocturnal HR. Cases 3 and 4 showed urinary tract infections, which were accompanied by t

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• Journal article
  Soreq E, Kolanko MA, Monica CD, Ravindran KKG, Revell VL, de Villèle P, Barnaghi P, Dijk D-J, Sharp DJ, CRT groupet al., 2022,

  Monitoring abnormal nocturnal behaviour in the homes of patients living with dementia.

  , Alzheimers Dement, Vol: 18 Suppl 2

  BACKGROUND: People living with dementia (PLWD) often exhibit marked sleep disturbances. These cause substantial care challenges and may be causally related to dementia progression. Collecting ecologically valid data on sleep disturbance in naturalistic settings has been difficult. As a result, sleep assessments in PLWD are generally limited to short studies in sleep laboratories or data collection from wearables, where compliance is problematic. Here, we demonstrate how passive internet of things (IoT) sensors can be used to monitor the effects of dementia on nocturnal behaviour and physiology. METHOD: Using the Withings under-mattress pressure sensor, we validated bed occupancy and physiological measures in 35 older adults tested both at home and in the laboratory. We then examined data collected between 2019 and 2021 from the general population (N=13,663) and from a cohort of PLWD taking part in the UK DRI study of home monitoring for PLWD (N=46). More than 4 million unique bed mat observations were analysed. RESULT: Arise time across all subjects was negatively correlated with time to bed (Fig.1a, r(13,617) =-0.5, p<.0001). Bed occupancy increased with age, but PLWD spent more time in bed than age-matched controls (Fig.1b) and had more nocturnal awakenings (Fig.1c). Explainable gradient boosting machine learning was successfully used to classify data from individual nights (PLWD vs. general population). PLWD probability was related to specific changes, such as increased awakenings, high or low time spent in bed, high heart rate and low breathing rate (Fig.2). We also explored variations in night-time behaviour and physiology over time for individual PLWD (Fig.3a). High within-subject variability was present, which related to disease progression, intercurrent illness and changes in medication. Data from each night was transformed into a risk score for each metric (Fig.3b) and a compound risk score. These quantify the risk of abnormal night-time behaviour and ph

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• Journal article
  Maas AIR, Menon DK, Manley GT, Abrams M, Akerlund C, Andelic N, Aries M, Bashford T, Bell MJ, Bodien YG, Brett BL, Buki A, Chesnut RM, Citerio G, Clark D, Clasby B, Cooper DJ, Czeiter E, Czosnyka M, Dams-O'Connor K, De Keyser V, Diaz-Arrastia R, Ercole A, van Essen TA, Falvey E, Ferguson AR, Figaji A, Fitzgerald M, Foreman B, Gantner D, Gao G, Giacino J, Gravesteijn B, Guiza F, Gupta D, Gurnell M, Haagsma JA, Hammond FM, Hawryluk G, Hutchinson P, van der Jagt M, Jain S, Jain S, Jiang J-Y, Kent H, Kolias A, Kompanje EJO, Lecky F, Lingsma HF, Maegele M, Majdan M, Markowitz A, McCrea M, Meyfroidt G, Mikoli A, Mondello S, Mukherjee P, Nelson D, Nelson LD, Newcombe V, Okonkwo D, Oresic M, Peul W, Pisica D, Polinder S, Ponsford J, Puybasset L, Raj R, Robba C, Roe C, Rosand J, Schueler P, Sharp DJ, Smielewski P, Stein MB, von Steinbuchel N, Stewart W, Steyerberg EW, Stocchetti N, Temkin N, Tenovuo O, Theadom A, Thomas I, Espin AT, Turgeon AF, Unterberg A, Van Praag D, van Veen E, Verheyden J, Vande Vyvere T, Wang KKW, Wiegers EJA, Williams WH, Wilson L, Wisniewski SR, Younsi A, Yue JK, Yuh EL, Zeiler FA, Zeldovich M, Zemek Ret al., 2022,

  Traumatic brain injury: progress and challenges in prevention, clinical care, and research

  , LANCET NEUROLOGY, Vol: 21, Pages: 1004-1060, ISSN: 1474-4422
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• Journal article
  Serban A-I, Soreq E, Barnaghi P, Daniels S, Calvo R, Sharp Det al., 2022,

  The effect of COVID-19 on the home behaviours of people affected by dementia

  , npj Digital Medicine, Vol: 5, ISSN: 2398-6352

  The COVID-19 pandemic has dramatically altered the behaviour of most of the world’s population, particularly affecting the elderly, including people living with dementia (PLwD). Here we use remote home monitoring technology deployed into 31 homes of PLwD living in the UK to investigate the effects of COVID-19 on behaviour within the home, including social isolation. The home activity was monitored continuously using unobtrusive sensors for 498 days from 1 December 2019 to 12 April 2021. This period included six distinct pandemic phases with differing public health measures, including three periods of home ‘lockdown’. Linear mixed-effects modelling is used to examine changes in the home activity of PLwD who lived alone or with others. An algorithm is developed to quantify time spent outside the home. Increased home activity is observed from very early in the pandemic, with a significant decrease in the time spent outside produced by the first lockdown. The study demonstrates the effects of COVID-19 lockdown on home behaviours in PLwD and shows how unobtrusive home monitoring can be used to track behaviours relevant to social isolation.

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