Ankle ReLoad

Annually, approximately 105,000 patients are
treated for an ankle fracture in the Netherlands and Germany, with a large
proportion of patients aged between 35 and 65 years. Besides impacting the
personal lives of these patients, this also leads to additional societal costs
due to absenteeism.
The recovery of these fractures has been shown to be more effective when the
joint is actively loaded. However, it is still unclear what the optimal ankle
load is to promote recovery, as too much load can even be harmful and prolong
the recovery process.
Determining ankle load has so far only been possible in laboratory conditions
due to the required complex measuring equipment. This means that individual
patients and their specialists do not have insight into how the joint is loaded
during the recovery period at home.
The goal of this project is to develop a demonstrator that is able to measure
ankle load at home through a ambulatory measurement system with a minimal
amount of sensors. This demonstrator will provide feedback to the patient and
the healthcare professionals via a digital application.
First, the ankle load is determined very accurately through lab based
measurements for a large number of patients. In the same measurements, data
will be gathered with the ambulatory measurement system with a minimal number
of sensors. By applying machine learning methods to the combined data from the
lab system and the minimal sensing system,  the sensing system can be
trained to determine ankle load from its limited sensors set. Continuous
monitoring and interpretation of the individual load pattern at home can then
be performed to study and guide optimization of the recovery process.
In addition, innovative next-generation methods for measuring body movement and
external load will be developed required for a consistently high data quality
in future large-scale applications.