  Human activity recognition has become an attractive research area with the
development of on-body wearable sensing technology. With comfortable
electronic-textiles, sensors can be embedded into clothing so that it is
possible to record human movement outside the laboratory for long periods.
However, a long-standing issue is how to deal with motion artefacts introduced
by movement of clothing with respect to the body. Surprisingly, recent
empirical findings suggest that cloth-attached sensor can actually achieve
higher accuracy of activity recognition than rigid-attached sensor,
particularly when predicting from short time-windows. In this work, a
probabilistic model is introduced in which this improved accuracy and
resposiveness is explained by the increased statistical distance between
movements recorded via fabric sensing. The predictions of the model are
verified in simulated and real human motion capture experiments, where it is
evident that this counterintuitive effect is closely captured.
