  In previous works, a mobile application was developed using an unmodified
commercial off-the-shelf smartphone to recognize whole-body exercises. The
working principle was based on the ultrasound Doppler sensing with the device
built-in hardware. Applying such a lab-environment trained model on realistic
application variations causes a significant drop in performance, and thus
decimate its applicability. The reason of the reduced performance can be
manifold. It could be induced by the user, environment, and device variations
in realistic scenarios. Such scenarios are often more complex and diverse,
which can be challenging to anticipate in the initial training data. To study
and overcome this issue, this paper presents a database with controlled and
uncontrolled subsets of fitness exercises. We propose two concepts to utilize
small adaption data to successfully improve model generalization in an
uncontrolled environment, increasing the recognition accuracy by two to six
folds compared to the baseline for different users.
