The burden of chronic disease has increased substantially over the past few years, and is expected to grow further as the global population and life expectancy continue to grow.

For example, the number of individuals with Parkinson’s disease (PD) in Western Europe’s 5 most and world’s 10 most populous nations are expected to double by 2030, and India is projected to face one of the highest growth rates. This could result in huge barriers in assessing quality healthcare services, exacerbated by factors such as wealth, gender, caste, and geography.

Researchers at the OICSD are investigating the role of technology in addressing these unprecedented healthcare challenges facing our societies. Specifically, we are developing novel remote technologies that can be deployed as reliable, non-invasive and inexpensive diagnostic support tools in home and community settings. Using these technologies, clinicians could monitor large number of patients remotely, and offer personalized guidance and services that are highly customized to ones needs. This could revolutionize health diagnosis and care in India and globally.

Recent Talks

  • “Novel Statistical Methodologies for Probabilistic Time Series Forecasting”, Manchester-Aston joint research day, University of Manchaster, U.K. (Nov. 2015).
  • “Detecting and Monitoring the Symptoms of Parkinson’s Disease using Smartphones”,

Oxford India Centre Seminar Series, University of Oxford, U.K. (Nov. 2015).

  • “Nonlinear and Nonparametric Modelling Approaches for Probabilistic Time Series

Forecasting”, Network Journal Club, Mathematical Institute, Oxford, U.K. (Oct. 2015).

  • “Time Series Forecasting: A Journey from Small to Big Datasets”, Systems and

Data Analytics Workshop, Aston University, Birmingham, U.K. (Sept. 2015).

  • “Symptom Monitoring for Parkinson’s Disease”, Parkinson’s UK West Herts Branch,

Watford, U.K. (Aug. 2015).

Ongoing Projects

Research in the OICSD engages across technology and healthcare, with projects including:

  • Detecting and monitoring the symptoms of Parkinson’s disease using smartphones
  • Investigating voice as a biomarker of LRRK2-associated Parkinson’s disease
  • Predicting the number of patient arrivals in accident and emergency (A&E) departments to inform staffing decisions
  • Developing biomarkers for Multiple Sclerosis

Journal Publications

  • J. Zablocki, S. Arora and M. Barua (2016), “Factors Affecting Media Coverage of Species Discovery”, Conservation Biology, forthcoming.
  • S. Arora and J.W. Taylor (2016), “Forecasting Electricity Smart Meter Data Using Conditional Kernel Density Estimation”, Omega, 59, 47-59. 
  • S. Arora, V. Venkataraman, A. Zhan, S. Donohue, K.M. Biglan, E.R. Dorsey and M.A. Little (2015), “Detecting and Monitoring the Symptoms of Parkinson’s Disease using Smartphones: A Pilot Study”, Parkinsonism and Related Disorders, 21, 650-653.
  • N.A. Vasistha, F. García-Moreno, S. Arora, A.F.P. Cheung, S.J. Arnold, E.J. Robertso and Z. Molnár (2014), “Cortical and Clonal Contribution of Tbr2 Expressing Progenitors in the Developing Mouse Brain”, Cerebral Cortex, 25, 3290-3302. 
  • S. Arora and M.S. Santhanam (2014), “Synchronization of Coupled Map Lattice using Delayed Variable Feedback”, Journal of Applied Nonlinear Dynamics, 3, 245-253. 
  • S. Arora and J.W. Taylor (2013), “Short-term Forecasting of Anomalous Load using Rule-based Triple Seasonal Methods”, IEEE Transactions on Power Systems, 28, 3235-3242. 
  • S. Arora, M.A. Little and P.E. McSharry (2013), “Nonlinear and Nonparametric Modeling Approaches for Probabilistic Forecasting of the US Gross National Product”, Studies in Nonlinear Dynamics and Econometrics, 17, 395-420. 
  • A.H. Gharekhan, S. Arora, A.N. Oza, M.B. Sureshkumar, A. Pradhan and P.K. Panigrahi (2011), “Distinguishing Autofluorescence of Normal, Benign, and Cancerous Breast Tissues through Wavelet Domain Correlation Studies”, Journal of Biomedical Optics, 16, 087003. 
  • A.H. Gharekhan, S. Arora, P.K. Panigrahi and A. Pradhan (2010), “Distinguishing Cancer and Normal Breast Tissue Autofluorescence Using Continuous Wavelet Transform”, IEEE Journal of Selected Topics in Quantum Electronics, 16, 893-899.
  • D.P. Ahalpara, S. Arora and M.S. Santhanam (2009), “Genetic Programming Based Approach for Synchronization with Parameter Mismatches in EEG”, Lecture Notes in Computer Science, 5481, 13-24. 
  • S. Arora, J. Acharya, A. Verma, P.K. Panigrahi (2008), “Multilevel Thresholding for Image Segmentation Through a Fast Statistical Recursive Algorithm”, Pattern Recognition Letters, 29, 119-125. 
  • A.H. Gharekhan, S. Arora, K.B.K. Mayya, P.K. Panigrahi, M.B. Sureshkumar and A. Pradhan (2008), “Characterizing Breast Cancer Tissues Through the Spectral Correlation Properties of Polarized Fluorescence”, Journal of Biomedical Optics, 13, 054063. 
  • M.S. Santhanam and S. Arora (2007), “Zero Delay Synchronization of Chaos in Coupled Map Lattices”, Physical Review E, 76, 026202. 
  • S. Hemachander, A. Verma, S. Arora, P.K. Panigrahi (2006), “Locally Adaptive Block Thresholding Method with Continuity Constraint”, Pattern Recognition Letters, 28, 119-124.