Publikation
Adaptive multimodal biosignal control for exoskeleton supported stroke rehabilitation
Anett Seeland; Marc Tabie; Su-Kyoung Kim; Frank Kirchner; Elsa Andrea Kirchner
In: IEEE International Conference on Systems, Man, and Cybernetics. IEEE International Conference on Systems, Man, and Cybernetics (SMC-2017), October 5-8, Banff, Canada, IEEE, 10/2017.
Zusammenfassung
A relevant issue of neuro-interfacing wearable
robots in rehabilitation is the necessity to have training data, since
the collection of sufficient data from patients within a reasonable
recording time is not always possible. However, the use of historic
data (e.g., session-to-session transfer, subject-to-subject transfer)
can often lead to a reduction in classification performance which
is affected by the selection of the historic data (i.e., which historic
data was chosen for transfer). In this paper, we analyze two
approaches to handle this reduction. First, we used incremental
algorithms that can be adapted to the current session when
trainable components (the spatial filter and the classifier) are
transferred between different sessions. Second, we increased
the number of sessions to learn more generalized models. To
evaluate the approaches, we used electroencephalographic data
that was recorded as training data for demonstrating our neuro-
interfacing wearable robot in the application of upper-body
sensorimotor rehabilitation. The data was collected from the
same healthy subject on 14 different days (14 sessions). Our
results showed that the use of a mixture of training sessions
improved the classification performance. Further, we could show
that the adaptive approaches contributed to less variability in
performance that allows the system to be more robust. Hence,
one can efficiently use both approaches (i.e., adapting and
generalizing the models) depending on how much training data is
available. Finally, the analyzed approaches are very promising to
increase system applicability in upper-body sensorimotor robotic
rehabilitation.