Meet the team

Shlomi Haar

"Our movement is the best measurable output of our brain. By linking body movement and brain activity measurements during real-world behaviour, we can better understand the neurobehavioural mechanisms of neurodegeneration and improve treatment delivery." Shlomi Haar
UK DRI Emerging Leader

With a background in biomedical engineering and experience in electrophysiology, Dr Shlomi Haar worked as an engineer in a human neuroimaging lab before pursuing a PhD studying movement encoding in the human brain at Ben-Gurion University of the Negev. In 2017, he was awarded a prestigious Royal Society – Kohn International Fellowship, joining the Brain and Behaviour Lab at Imperial College London. Here, he addressed the neurobehavioural changes during the learning process of novel real-life complex motor skills. Dr Haar became an Edmond and Lily Safra Research Fellow in 2020 at the Department of Brain Sciences, Imperial College London, before becoming a UK DRI Emerging Leader at the Care Research and Technology Centre in 2021 – sponsored by UK DRI Group Leader Dr Tim Constandinou. In his research programme, Dr Haar investigates the neurobehavioural mechanisms of movement disorders and their treatments, specifically regarding deep brain stimulation in Parkinson’s disease.

1. At a glance

Digital biomarkers based on continuous measurement of motor behaviour and brain activity can provide cost-effective, objective, and robust measures for the tracking the progression of neurodegenerative disease, changes in care needs, and the effect of interventions. Moreover, those can be used as robust outcome measurements for clinical trials, making those shorter, cheaper and conclusive.

Dr Shlomi Haar and his team are developing motor digital biomarkers which can account for daily life activities in the real-world and fluctuations in posture, mobility, frailty, and movement structure. This would enable treatment optimization, and identification of the need for greater support, and would provide robust outcome measures, based on home sensing technology, to assess the effectiveness of new disease-modifying interventions.

Dr Shlomi Haar and his team are also working to improve understanding of the neural mechanisms underlying movement and movement disorders in order to improve treatment delivery for Parkinson’s Disease and enable new therapies. The team focus is on mapping the effect of deep brain stimulation (delivered clinically via implanted electrodes or experimentally via non-invasive methods) on brain activity and motor function, to develop neural and motor digital biomarkers for personalised and precision therapy.

2. Scientific goals

Most of our brain is involved in the planning and execution of body movement, and motor function and related behaviour are informative measures in most neurological conditions. Motor decline showed to have a higher attributable risk for dementia than cognitive decline. Unlike cognitive decline, motor decline can be measured objectively, passively, and continuously through sensors. Hence, accurate tracking of motor decline in dementia can provide novel digital biomarkers for improving diagnosis, tracking disease progression, and identifying care needs, which may help improve independence and slow functional decline.

This UK DRI programme, led by Dr Shlomi Haar, studies the neurobehavioural mechanisms of human movement in health and disease: motor control, motor learning, motor decline, and their neural correlates. Dr Haar is leading interdisciplinary research between engineering and neuroscience, deploying novel digital technologies for neural and movement recordings, pervasive AI, and state-of-the-art data science and machine learning to unleash the potential of Real-World Motor Neuroscience to study real-world neurobehaviour of neurodegenerative patients and develop novel digital biomarkers that will enable improved care, robust outcome measure for clinical trials in new disease-modifying interventions and close loop personalized interventions.

The research programme focuses on improving our understanding of the neural network of human motor control and the effects of neurodegeneration on it, predominantly in Parkinson’s disease. The programme aims to improve disease progression and symptom fluctuation tracking for closed-loop personalized interventions. Of specific interest is a better understanding of the neurobehavioural mechanisms of Deep Brain Stimulation (DBS) for Parkinson's disease. This would enable better treatment delivery and leverage smart sensing and AI toward personalized medicine using adaptive closed-loop therapies.

Main objectives and research goals:

The neuroscientific aim of the programme is to improve our understanding of the neural mechanisms underlying movement and movement decline, and the translational aim is to use it to improve treatment delivery and enable new therapies. Ultimately, the goal is to close the loop, using smart sensing and AI to guide personalised and tailored therapies.

3. Team members

Nathan Steadman (PhD Student)
Federico Nardi (PhD Student)
Cosima Graef ( PhD Student)
Niro Yogendran (PhD Student)
Alena Kutuzova (PhD Student)
Assaf Touboul (PhD Student)
Nicolas Calvo-Peiro (PhD Student)
Jenna Yun (Research Technician)
Gaia Frigerio (Research Technician)

4. Collaborations

Within UK DRI:

  • Prof Timothy Constandinou, UK DRI Care Research & Technology
  • Prof Ravi Vaidyanathan, UK DRI Care Research & Technology
  • Prof Payam Barnaghi, UK DRI Care Research & Technology
  • Prof David Sharp, UK DRI Care Research & Technology
  • Dr Nir Grossman, UK DRI at Imperial

Beyond UK DRI:

  • Dr Yen Tai, Imperial College London and Imperial College Chairing Cross NHS Trust
  • Prof Aldo Faisal, Imperial College London
  • Prof Dario Farina, Imperial College London
  • Dr Chris Butler, Imperial College London
  • Prof Huiling Tan, Oxford

5. Topics

Human Movement, Motor Control, Motor Learning, Motor Decline, Movement Disorders, Parkinson’s Disease, Deep Brain Stimulation, Neurodegeneration, Dementia

6. Techniques

Depth cameras, UWB Radar, Wearable sensors, EEG, fMRI, Deep Brain Stimulation, Non-invasive brain stimulation, Artificial intelligence, Machine Learning

7. Key publications

Gonzalez-Robles, Bartlett, Burnell, Clarke, Haar, et al. (2023) Embedding patient input in outcome measures for long-term disease-modifying Parkinson disease trials. Movement Disorders [doi: 10.1002/mds.29691]

Crook-Rumsey, Daniels, Abulikemu, …, and Haar (2023). Multicohort cross-sectional study of cognitive and behavioural digital biomarkers in neurodegeneration: the Living Lab study protocol. BMJ Open 13:e072094 [doi: 10.1136/bmjopen-2023-072094]

Haugland, Borovykh, Tai, and Haar (2023). Explainable Deep Learning for Arm Classification During Deep Brain Stimulation - Towards Digital Biomarkers for Closed-Loop Stimulation. Paper 1368, The 7th Conference on Cognitive Computational Neuroscience [doi: 10.32470/CCN.2023.1368-0]

Carpio-Chicote, Jeyasingh-Jacob, Abulikemu, and Haar (2023). Computational Tracking of Parkinsonian Motor Fluctuations in a Real-World Setting: A Case Study. Paper 1420, The 7th Conference on Cognitive Computational Neuroscience [doi: 10.32470/CCN.2023.1420-0]

Abulikemu, Tai, and Haar (preprint). Modulatory Effect of Levodopa on the Basal Ganglia-Cerebellum Connectivity in Parkinson's Disease [ doi: 10.1101/2023.01.16.524229]

Jeyasingh-Jacob, Crook-Rumsey, Shah, Joseph, Abulikemu, Daniels, Sharp, and Haar (preprint). Markerless Motion Capture to Quantify Functional Performance in Neurodegeneration: A Systematic Review [doi: 10.2196/preprints.52582]

Patel+, Haar+, Handslip+ et al. (2021). Natural history, trajectory, and management of mechanically ventilated COVID-19 patients in the United Kingdom. Intensive Care Med 47, 549–565 [doi: 10.1007/s00134-021-06389-z]

Haar and Faisal (2020). Brain activity reveals multiple motor-learning mechanisms in a real-world task. Front. Hum. Neurosci. 14:354. [doi: 10.3389/fnhum.2020.00354]

Haar and Donchin (2020). A revised computational neuroanatomy for motor control. Journal of Cognitive Neuroscience 32 (10), 1823-1836. [doi: 10.1162/jocn_a_01602]

8. Lab website

Imperial Profile
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