  When creating multi-channel time-series datasets for Human Activity
Recognition (HAR), researchers are faced with the issue of subject selection
criteria. It is unknown what physical characteristics and/or soft-biometrics,
such as age, height, and weight, need to be taken into account to train a
classifier to achieve robustness towards heterogeneous populations in the
training and testing data. This contribution statistically curates the training
data to assess to what degree the physical characteristics of humans influence
HAR performance. We evaluate the performance of a state-of-the-art
convolutional neural network on two HAR datasets that vary in the sensors,
activities, and recording for time-series HAR. The training data is
intentionally biased with respect to human characteristics to determine the
features that impact motion behaviour. The evaluations brought forth the impact
of the subjects' characteristics on HAR. Thus, providing insights regarding the
robustness of the classifier with respect to heterogeneous populations. The
study is a step forward in the direction of fair and trustworthy artificial
intelligence by attempting to quantify representation bias in multi-channel
time series HAR data.
