In this thesis, we determined how system identification can be integrated in the clinic to assess human balance control. Adequately balance assessment is important in order to detect people with high fall risks and to optimize balance training, which would ultimately result in better rehabilitation and thus less falls. Current clinical tests suffer from ceiling effects, are subjective and do not provide insight in the underlying mechanisms. System identification techniques seem promising, but they depend on large, expensive and complex devices such as motion platforms and motion capture cameras, and are therefore less suitable for clinical use. In part 1, we first focussed on the technical and methodological characteristics of the system identification techniques. In part 2, we used the resulting system identification method, whereby the treadmill applied support surface perturbations, on a minor stroke population to evaluate subtle changes in balance control of the paretic leg.
|Award date||30 Sep 2020|
|Publication status||Published - 2020|