Meet the team

Timothy Constandinou

"By developing new devices enabling passive and minimally obtrusive sensing, as well as bioelectronic intervention we aim to deploy technology that provides new insights to ultimately improve the lives of people living with dementia." Timothy Constandinou
UK DRI Group Leader

Timothy Constandinou is Professor of Bioelectronics at Imperial College London, Director of the Next Generation Neural Interfaces (NGNI) Lab and Head of the Circuits & Systems (CAS) Research Group at Imperial College London. Obtaining his PhD in Electronic Engineering in 2005, he went on to specialise in medical device technology for brain disorders. As a Group Leader at the UK DRI Care Research & Technology Centre based at Imperial, Timothy leads an exciting programme of developing unobtrusive radar sensors, in-ear hearables, and implantable devices to empower next generation dementia care and research.

1. At a glance

Developing new devices for home monitoring and intervention aimed at helping people living with dementia

Professor Timothy Constandinou and his team are developing novel bioelectronic systems that will enable continuous, minimally obtrusive monitoring and new interventions to improve outcomes in people living with dementia (PLWD). The scope of this programme encompasses the full breadth of biomedical device technologies that sense human physiology and behaviour and provide the opportunity to intervene. These include wearable, implantable and remote sensing technologies capturing physiological, behavioural and environmental parameters. Devices can intervene using a direct interface to the human body, or remotely through an external device, carer, or clinician.

The UK DRI Care Research and Technology (CR&T) Centre’s core mission is improving dementia care by using new technologies. This is uniquely challenging in dementia, particularly relating to PLWD using and engaging with technology. It is therefore essential to co-create with key stakeholders (PLWD, carers, clinicians, scientists) and adopt a user-centred design strategy. This has helped define our focus to create technologies that encourage deployability (low cost, scalability) and good compliance (uptake and adherence). Specifically, we are developing unobtrusive, remote sensing using wall-mounted radar, ultra- wearable platforms using in-ear sensing (‘hearables’), and brain interfacing using implantable devices.

2. Scientific goals

This UK DRI programme, led by Professor Timothy Constandinou, focuses on integrating radar within the smart home infrastructure and deploy in PLWD homes to assess their health and wellbeing longitudinally, while leveraging hearables for monitoring and diagnosis of sleep disorders. Our objective is to enable the observation of disease progression by providing new physiological and behavioural measurables unobtrusively at home, improving the assessment of therapeutic interventions such as pharmacological efficacy, behavioural adjustments for sleep, and facilitating further research in dementia.

A second focus will extend our technology platform to include deep brain stimulation (DBS) devices that are routinely used in the treatment of motor symptoms in Parkinson’s disease (PD). By leveraging a strategic UK partnership, we will create the capability to observe brain physiology 24/7 through a next generation DBS device integrated in the home. We will use this to explore how stimulation can be optimised across the circadian cycle to reduce daytime sleepiness and enhance vigilance whilst also maintaining the baseline therapy for motor symptoms. We expect that sensing brain activity using a networked implant will provide new insights into disease progression. Establishing this testbed in PD paves the way to then explore new bioelectronic interventions in neurodegeneration and dementia.

Main objectives and research goals:

1. To develop passive low-cost ways of sensing movement, vital signs and monitoring behaviour in the home, including the application of ultra-wide band (UWB) radar.

2. To develop unobtrusive miniature devices that allow low-cost, continuous home monitoring of brain activity using sparsely sampled electroencephalography (EEG).

3. To integrate the new technology into the Healthy Home system, providing synchronised multi-modal data including EEG, movement, location, respiration, and heart rate.

4. To develop new research platforms (wearable and/or implantable) as investigational tools and to accelerate translational studies in dementia.

3. Team members

Dr Shlomi Haar (Emerging Leader)
Prof Danilo Mandic (Academic)
Prof Tor Sverre Lande (Principal Scientist)
Dr Alan Bannon (Postdoctoral Researcher)
Dr Ian Williams (Postdoctoral Researcher)
Dorian Haci (Postdoctoral Researcher)
Andrea Mifsud (Postdoctoral Researcher)
Peilong Feng (Postdoctoral Researcher)
Dr Harry Davies (Postdoctoral Researcher)
Adrien Rapeaux (Research Assistant)
Ghena Hammour (Research Assistant)
Ziwei Chen (Research Assistant)
Cosima Graef (PhD Student)
Charalambos Hadjipanayi (PhD Student)
Alena Kutuzova (PhD Student)
Federico Nardi (PhD Student)
Assaf Touboul (PhD Student)
Maowen Yin (PhD Student)
Niro Yogendran (PhD Student)
Zheng Zheng (PhD Student)
Zachary Nairac (PhD Student)
Vichaya Manatchinapisit (PhD Student)
Dimitris Antoniades (PhD Student)
Berkay Ozbek (PhD Student)
Steven Wong (PhD Student)
Natalia Martinez (PhD Student)
Mei Miyazaki Kirby (PhD Student)
Nicolas Calvo Peiro (PhD Student)
Gaia Frigerio (MSc Student)

4. Collaborations

Within UK DRI:

  • Professor David Sharp, UK DRI Care Research & Technology – Behaviour and cognition
  • Professor Derk-Jan Dijk, UK DRI Care Research & Technology – Improving sleep and circadian disruption
  • Professor William Wisden and Professor Nick Franks FRS, UK DRI at Imperial – Research tools for studying sleep

Beyond UK DRI:

  • Professor Tim Denison, University of Oxford – Implantable research platforms for translational studies
  • Dag Wisland, Novelda AS – Ultra-wideband radar-on-chip
  • Ivor Gillbe, Bioinduction Ltd – Active implantable medical devices

5. Topics

Remote sensing, hearables, assistive technology, implantable devices, research tools

6. Techniques

Ultra-wideband radar sensing, behavioural and motion analysis, physiological monitoring, electrophysiology, neuromodulation, deep brain stimulation

7. Key publications

Zamora, M., Toth, R., Morgante, F., Ottaway, J., Gillbe, T., Martin, S., Lamb, G., Noone, T., Benjaber, M., Nairac, Z., Sehgal, D., Constandinou, T.G., Herron, J., Aziz, T.Z., Gillbe, I., Green, A.L., Pereira, E.C. and Denison, T., 2022. DyNeuMo Mk-1: Design and pilot validation of an investigational motion-adaptive neurostimulator with integrated chronotherapy. Experimental Neurology, 351, p.113977.

Davies, H.J., Williams, I., Hammour, G., Yarici, M., Stacey, M.J., Seemungal, B.M. and Mandic, D.P., 2022. In-Ear SpO2 for Classification of Cognitive Workload. IEEE Transactions on Cognitive and Developmental Systems.

Bannon, A., Rapeaux, A. and Constandinou, T.G., 2021. Tiresias: A low-cost networked UWB radar system for in-home monitoring of dementia patients. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 7068-7072).

Chen, Z., Bannon, A., Rapeaux, A. and Constandinou, T.G., 2021. Towards Robust, Unobtrusive Sensing of Respiration Using UWB Impulse Radar for the Care of People Living with Dementia. In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 866-871).

Hammour, G.M. and Mandic, D.P., 2021. Hearables: Making Sense from Motion Artefacts in Ear-EEG for Real-Life Human Activity Classification. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 6889-6893).

Hsieh, B., Harding, E.C., Wisden, W., Franks, N.P. and Constandinou, T.G., 2019. A Miniature Neural Recording Device to Investigate Sleep and Temperature Regulation in Mice. In 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) (pp. 1-4).

8. Lab website

UK DRI at Imperial Profile

UK DRI at Imperial Research Programme (Next generation neural interfaces)

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