Prof Dr Andrea Pfingsten from OTH Regensburg was recently invited to present her latest research findings at the World Physiotherapy Congress (WPC) in Tokyo (Japan). In collaboration with the physiotherapy and biomechanics laboratories of the Regensburg Center of Health Sciences and Technology, Prof. Dr Pfingsten was involved in research into data-based exercise selection in knee rehabilitation.
In view of the increasing shortage of specialists and an ageing population, data-based decision-making processes are becoming increasingly important to support the efficient selection of exercises. Joint reaction force (JRF) is likely to be a key parameter in this context.
For the study, the kinematic and kinetic data of 20 knee exercises were recorded from 30 healthy test subjects. The technical basis was a markerless motion capture system with eight cameras, chairs and stairs equipped with force plates and load cells. The collected data was fed into a whole-body model, which was used to calculate joint angles, angular velocities, muscle activity and, in particular, joint reaction forces. In her presentation, Prof Pfingsten compared the resulting peak loads using the example of three exercise groups (single-leg stand, sit-to-stand, stair climbing): Depending on the progression, single-leg stance tasks generate 2.5 to 2.9 times the body weight, sit-to-stand movements 2.8 to 4.2 times the body weight and stair activities 4.8 times the body weight. A statistical analysis using Welch's ANOVA confirmed the increasing load with increasing difficulty, with the exception of the stair activities. It is likely that the healthy test subjects transferred hardly any weight to the railing during the assisted version.
As part of the overarching ‘MyReha-digital’ project, a patient-centred rehabilitation system for patients with knee endoprostheses is to be developed on a data-based basis. A sensor system records kinematic parameters during exercises, a smartphone app transmits the information to a backend server and algorithms determine optimised exercise sequences from the data over the long term. The aim is to provide biofeedback-supported, location-independent training in which the individualisation of therapy will be based on sound data in the future.
The project is funded by the Federal Ministry of Education and Research and is being carried out in cooperation with the medical device manufacturers Oped, Linova and Interactive Wear as well as the Barmherzige Brüder clinic in Regensburg, which specialises in knee surgery.
