Estimating Physical Ability of Stroke Patients without Specific Tests

Publication/Creation Date
September 11 2015
Adrian Derungs (creator)
Julia Seiter (creator)
Corina Schuster-Amft (creator)
Oliver Amft (creator)
University Of Passau (contributor)
ETH Zrich (contributor)
Reha Rheinfelden (contributor)
Persuasive Intent
We estimate the Extended Barthel Index (EBI) in patients after stroke using inertial sensor measurements acquired during daily activity, rather than specific assessments. The EBI is a standard clinical assessment showing patient independence in handling everyday tasks. Our work aims at providing a continuous ability estimate for patients and therapists that could be used without expert supervision. We extract nine activity primitives (AP), including sitting, standing, transition, etc. from the continuous sensor data using basic rules that do not require data-based training. Using the relative duration of activity primitives, we evaluate the EBI score estimation using two regression methods: Generalised Linear Models (GLM) and Support-Vector Regression (SVR). We evaluated our approaches in full-day study recordings from 11 stroke patients with totally 102 days in ambulatory rehabilitation in a daycare centre. Our results show that EBI can be estimated from the activity primitives with approximately 12% relative error on average for all study participants using SVR. Our results indicate that EBI can be estimated in daily life activity, thus supporting patients and therapists in tracking rehab progress.
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Date archived
November 30 2015
Last edited
August 23 2016
How to cite this entry
Adrian Derungs, Julia Seiter, Corina Schuster-Amft, Oliver Amft. (September 11 2015). "Estimating Physical Ability of Stroke Patients without Specific Tests". 2015 ACM International Symposium on Wearable Computers (ISWC 2015). International Symposium On Wearable Computers. Fabric of Digital Life.