Adaptive Systems

Systems that model their users and adapt to them in real time need an estimate of user
state that holds up outside the laboratory, and adaptation policies that support a
person rather than destabilise them.

Estimation in use is a different regime, not a harder version of the same one. In the
laboratory we average over hundreds of repetitions, we know when each stimulus
occurred, and we analyse the recording afterwards; in use there is a single pass, no
trigger, drifting electrodes and a subject who moves. We are building estimation that
works under those constraints — causal, without averaging, inside a latency budget —
at present on the real-time analysis of somatosensory evoked potentials along the
nervous system. Real-time estimation of external state such as head and eye movement
has been part of our work for years and provides the engineering ground for it.

Adaptation raises a problem that measurement does not have. A system that responds to
rising cognitive load reduces that load, and with it the signal from which the
estimate was drawn. We also hold that users should be able to recognise how a system
has understood them and to correct it, which is a requirement on the interface and not
only on the estimator.

 I. Ignatieva, B. Nierula, K. Tomotaki-Dawoud, and S. Bosse. ‘From Microsociology to VR Design: A Framework for Integrating Non-Verbal Behavior in Adaptive Systems’. In: Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems. CHI EA ’26: Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems. Barcelona , Spain: ACM, Apr. 13, 2026, pp. 1–5

S. Gül, S. Bosse, D. Podborski, T. Schierl, C. Hellge, M. A. Kastner, and J. Zahálka. ‘Reproducibility Companion Paper: Kalman Filter-Based Head Motion Prediction for Cloud-Based Mixed Reality’. In: Proceedings of the 29th International Conference on Multimedia. ACM. Ottawa, ON, Canada: ACM, 2021, pp. 3619–3621