Ankle ReLoad

Basic data for this project

Description

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.