Can a Chair Detect Nodding?: An Exploratory Study for Privacy-aware Meeting Analysis

In this paper, we explore a novel non-intrusive nodding recognition method for privacy-aware meeting analysis. We focus on the subtle sway through the chair on which a participant is seated, employing an accelerometer mounted to the chair's backrest to recognize nodding. By capturing subtle sway of the chair rather than using cameras or wearable devices, our method preserves participants' privacy while enabling naturalistic nodding measurement in real meeting settings. In experimental evaluations conducted under controlled conditions, a machine learning model based on Random Forest (RF) achieved nodding recognition accuracy exceeding 98%. Nod count accuracy was also favorable, with a mean absolute error (MAE) of 5.40 and a weighted absolute percentage error (WAPE) of 7.11%. In a subsequent evaluation in an actual meeting environment, recognition accuracy decreased due to body movements and posture changes; nevertheless, the results suggest the feasibility of nodding recognition via chair sway sensing. Our results suggest a practical, privacy-aware route to unobtrusive meeting analysis via chair-sway sensing.