Remote Continuous Cardiovascular Function Monitoring for Ambulatory SCI

Spinal Cord Injury (SCI) is a lifelong lasting condition that can affect anyone. Although much-advanced care has provided improved prognosis post-SCI, we still see a remarkable difference in the mortality rate of persons with SCI compared to the general population from 2 times in paraplegics to over 8 times in tetraplegic persons. Where the main cause of reduced life expectancy is cardiovascular disease (CVD).

Continuous monitoring of the physical activity levels, social behaviours, and environmental factors together with cardiovascular function in a continuous and unobtrusive manner will enable us to develop methods and models to quantify CVD risk models and compare them with the general population models.

Enlarged view:  wheelchair with function analysis device
Continuous sensing of wheelchair users and their environment for cardiovascular function analysis

Some CVD risk factors could be measured at annual check-ups through lipid spectrum, blood pressure, heart rate or electrocardiograms (ECG). Relevant information during daily life might portrays a different picture of the health state and cardiovascular function of the individual, e.g. blood pressure curves, long-term heart rate variability, and continuous ECG during ADLs. Implementation in clinical practice is not regularly done.

We aim to achieve a measurement systems and continuous metrics for ambulatory usage that could provide risk biomarkers of CVD by quantifying physical activities while relating them to known cardiovascular function metrics. Herewith, developing measurement protocols that could be exported from inpatient evaluations towards outpatient monitoring and prevention in telemedicine.
We make use of continuous measurements of cardiovascular activity through heart rate, blood pressure, and ECG data combined with environmental measurement and activity tracking. Multimodal data fusion is then used for building anomaly detections and a feedback loop for the medical doctors to assess the onset of multiple related conditions which would enable the development of detection algorithms and risk models for outpatient care.

Founded by the ETH-SPS Digital Transformation in Personalized Health Care for SCI Project

Working Group

  • Ana Cisnal
  • Itilekha Podder
  • Mehdi Etjehadi

External Collaborators

  • KD Dr. med. PhD Inge Eriks-Hoogland (SPZ - Outpatient Unit)
  • KD Dr. med. PhD Anke Scheel-Sailer (SPZ - Inpatient Unit)
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