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• Journal article
  Fletcher-Lloyd N, Soreq E, Wilson D, Nilforooshan R, Sharp DJ, Barnaghi Pet al., 2021,

  Home monitoring of daily living activities and prediction of agitation risk in a cohort of people living with dementia.

  , Alzheimers & Dementia, Vol: 17, Pages: 1-1, ISSN: 1552-5260

  BACKGROUND: People living with dementia (PLWD) have an increased susceptibility to developing adverse physical and psychological events. Internet of Things (IoT) technologies provides new ways to remotely monitor patients within the comfort of their homes, particularly important for the timely delivery of appropriate healthcare. Presented here is data collated as part of the on-going UK Dementia Research Institute's Care Research and Technology Centre cohort and Technology Integrated Health Management (TIHM) study. There are two main aims to this work: first, to investigate the effect of the COVID-19 quarantine on the performance of daily living activities of PLWD, on which there is currently little research; and second, to create a simple classification model capable of effectively predicting agitation risk in PLWD, allowing for the generation of alerts with actionable information by which to prevent such outcomes. METHOD: A within-subject, date-matched study was conducted on daily living activity data using the first COVID-19 quarantine as a natural experiment. Supervised machine learning approaches were then applied to combined physiological and environmental data to create two simple classification models: a single marker model trained using ambient temperature as a feature, and a multi-marker model using ambient temperature, body temperature, movement, and entropy as features. RESULT: There are 102 PLWD total included in the dataset, with all patients having an established diagnosis of dementia, but with ranging types and severity. The COVID-19 study was carried out on a sub-group of 21 patient households. In 2020, PLWD had a significant increase in daily household activity (p = 1.40e-08), one-way repeated measures ANOVA). Moreover, there was a significant interaction between the pandemic quarantine and patient gender on night-time bed-occupancy duration (p = 3.00e-02, two-way mixed-effect ANOVA). On evaluating the models using 10-fold cross validation, both th

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• Journal article
  Rezvani R, Kouchaki S, Nilforooshan R, Sharp DJ, Barnaghi Pet al., 2021,

  Analysing behavioural changes in people with dementia using in-home monitoring technologies.

  , Alzheimers & Dementia, Vol: 17 Suppl 11, Pages: e052181-e052181, ISSN: 1552-5260

  BACKGROUND: Behavioural changes and neuropsychiatric symptoms such as agitation are common in people with dementia. These symptoms impact the quality of life of people with dementia and can increase the stress on caregivers. This study aims to identify the likelihood of having agitation in people affected by dementia (i.e., patients and carers) using routinely collected data from in-home monitoring technologies. We have used a digital platform and analytical methods, developed in our previous study, to generate alerts when changes occur in the digital markers collected using in-home sensing technologies (i.e., vital signs, environmental and activity data). A care monitoring team use the platform and interact with participants and caregivers when an alert is generated. METHOD: We have used connected sensory devices to collect environmental markers, including Passive Infra-Red (PIR), smart power plugs for monitoring home appliance use, motion and door sensors. The environmental marker data have been aggregated within each hour and used to train an agitation risk analysis model. We have trained a model using data collected from 88 homes (∼6 months of data from each home). The proposed model has two components: a self-supervised transformation learning and an ensemble classification model for agitation likelihood. Ten different neural network encoders are learned to create pseudo-labels using the samples from the unlabelled data. We use these pseudo-labels to train a classification model with a convolutional block and a decision layer. The trained convolutional block is then used to learn a latent representation of the data for an ensemble classification block. RESULTS: Comparing with baseline models such as LSTM network, Bidirectional LSTM (BiLSTM) network, VGG, ResNet, Inception, Random Forest (RF), Support Vector Machine (SVM) and Gaussian Process (GP) classifiers, the proposed model performs better in sensitivity (recall) and area under the precision-recall curv

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• Journal article
  Liu KY, Howard R, Banerjee S, Comas-Herrera A, Goddard J, Knapp M, Livingston G, Manthorpe J, O'Brien JT, Paterson RW, Robinson L, Rossor M, Rowe JB, Sharp DJ, Sommerlad A, Suarez-Gonzalez A, Burns Aet al., 2021,

  Dementia wellbeing and COVID-19: Review and expert consensus on current research and knowledge gaps

  , INTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY, Vol: 36, Pages: 1597-1639, ISSN: 0885-6230
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• Journal article
  Ahmadi N, Constandinou T, Bouganis C, 2021,

  Inferring entire spiking activity from local field potentials

  , Scientific Reports, Vol: 11, Pages: 1-13, ISSN: 2045-2322

  Extracellular recordings are typically analysed by separating them into two distinct signals: local field potentials (LFPs) andspikes. Previous studies have shown that spikes, in the form of single-unit activity (SUA) or multiunit activity (MUA), can beinferred solely from LFPs with moderately good accuracy. SUA and MUA are typically extracted via threshold-based techniquewhich may not be reliable when the recordings exhibit a low signal-to-noise ratio (SNR). Another type of spiking activity, referredto as entire spiking activity (ESA), can be extracted by a threshold-less, fast, and automated technique and has led to betterperformance in several tasks. However, its relationship with the LFPs has not been investigated. In this study, we aim toaddress this issue by inferring ESA from LFPs intracortically recorded from the motor cortex area of three monkeys performingdifferent tasks. Results from long-term recording sessions and across subjects revealed that ESA can be inferred from LFPswith good accuracy. On average, the inference performance of ESA was consistently and significantly higher than those of SUAand MUA. In addition, local motor potential (LMP) was found to be the most predictive feature. The overall results indicate thatLFPs contain substantial information about spiking activity, particularly ESA. This could be useful for understanding LFP-spikerelationship and for the development of LFP-based BMIs.

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• Journal article
  Huo W, Moon H, Alouane MA, Bonnet V, Huang J, Amirat Y, Vaidyanathan R, Mohammed Set al., 2022,

  Impedance modulation control of a lower limb exoskeleton to assist sit-to-stand movements

  , IEEE Transactions on Robotics, Vol: 38, Pages: 1230-1249, ISSN: 1552-3098

  As an important movement of the daily living activities, sit-to-stand (STS) movement is usually a difficult task facingelderly and dependent people. In this article, a novel impedancemodulation strategy of a lower limb exoskeleton is proposed toprovide appropriate power and balance assistance during STSmovements while preserving the wearer’s control priority. Theimpedance modulation control strategy ensures adaptation of themechanical impedance of the human-exoskeleton system towardsa desired one requiring less wearer’s effect while reinforcing thewearer’s balance control ability during STS movements. A humanjoint torque observer is designed to estimate the joint torquesdeveloped by the wearer using joint position kinematics instead ofelectromyography (EMG) or force sensors; a time-varying desiredimpedance model is proposed according to the wearer’s lowerlimb motion ability. A virtual environmental force is designedfor the balance reinforcement control. Stability and robustness ofthe proposed method are theoretically analyzed. Simulations wereimplemented to illustrate the characteristics and performance ofthe proposed approach. Experiments with four healthy subjectswere carried out to evaluate the effectiveness of the proposedmethod and show satisfactory results in terms of appropriatepower assist and balance reinforcement.

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