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Research project

Measuring cognitive load of stroke patients at home

Status: Ongoing project

To personalize rehabilitation to the patient’s capabilities, we develop new ways to measure the cognitive load of stroke patients at home.

What we do

About our project

Because of the damage caused by stroke, many stroke survivors experience difficulties in daily life. Not only are there physical impairments, but many patients also struggle with attention, memory and planning. These cognitive problems can lead to increased fatigue, increased risk of burn-outs and reduced learning capabilities.

Aim of the project
Currently, there are no validated ways to gain insight into how much cognitive demands we ask from stroke patients. We mostly rely on questionnaires and observations to guess the patient’s experience. This information is valuable but subjective and at risk for biases. Our goal for this project is to objectively assess cognitive load in real-time, allowing tailor-made rehabilitation that is optimally adjusted to the patient’s capabilities.

The approach of the project
We want to create an innovative algorithm that combines different sensors to estimate the cognitive demands of a person. To do this, we will use wearable sensors that are convenient for use at home.

Impact of the project

A cornerstone in neuro-rehabilitation is learning. Patients relearn old movements or learn new compensation strategies to achieve their old function. When a person experiences too much cognitive load, they have trouble learning and feel tired quickly. By ensuring that the patient is never overloaded, we want to make rehabilitation more effective

Our research focus

We want to be able to track the cognitive load at home. This requires us to use devices that are easy to use, robust and unobtrusive. That makes wearables, such as smartwatches and chest bands, logical choices for our measurement devices. 
In this project, we explore the validity of these wearables to obtain signals that can estimate cognitive load. We will then use these signals to create a model that gets insights into the patient’s cognitive demands. 
Sensor fusion 
As Aristotle famously said: “The whole is greater than the sum of its parts”. With sensor fusion, we combine data from multiple sensors. With this combination, we can create new signals or improve existing signals and get a better estimation of a person’s cognitive load. 
Data science 
To combine these complex signals and get meaningful information, we will require modern statistical approaches such as machine learning.

Our team

Danny Lemmers, PhD student

Dr. Hans Bussmann, Associate Professor Erasmus MC, promotor

Prof. Jane Cramm, Professor EUR, promotor

Dr. Arkady Zgonnikov, Assistant Professor TU Delft, co-promotor

Dr. Erik Grauwmeijer, Physician, Rijndam Rehabilitation


Contact us

Hans Bussmann: j.b.j.bussmann@erasmusmc.nl