SUMMARYMore and more recent studies of emotion estimation have taken the approach of ambient sensing, where users are monitored without constraining their bodies and without being aware of the sensing system. Few of them, however, focus on audience emotions. We attempted to examine audience emotions, which are difficult to estimate when using only individual data, and which come to be estimated more easily when using ambient sensing of synchrony among multiple users. We developed an ambient sensing chair, which employs wireless sensors to determine the center of force of the seated user, and designed an experiment for an audience watching a soccer game on TV or a technical presentation in order to examine the relationship between audience body sway and emotional states in a natural situation. We found that synchrony could be well correlated with audience excitement and boredom, something not observable in the individual data. These results indicate the possibility of development of service marketing systems that evaluate TV content, and feedback systems that give notice of the emotional state of an audience to a presenter. C⃝ 2015 Wiley Periodicals, Inc. Electron Comm Jpn, 98(4): 11-19, 2015; Published online in Wiley Online Library (wileyonlinelibrary.com).
SUMMARYThis paper presents a technique for writing-state classification by sedentary behavior sensing with a sensorequipped chair, which wirelessly monitors the user's states such as behaviors or physiological and psychological conditions, for estimation of the user's subjective difficulty with a studying problem on a desktop. Avoiding user discomfort, four load cells are mounted behind the seat plate of a regular office chair. The system simply measures the weight and the center of pressure of the seat. These data are divided into time segments, which are labeled into four sedentary-body sway primitives by the decision tree algorithm, and then the behaviors are derived from the number and pitch of those primitives in a certain interval. In systemevaluation experiments, it achieved more than 80% labeling accuracy for untrained users. We conducted a user experiment showing its potential for desk work applications. The results of the experiment showed that our proposed system could classify user states into the writing state and other states such as reading a document and watching a movie at a classification rate of 87%. C⃝ 2014 Wiley Periodicals, Inc. Electron Comm Jpn, 98(1): 15-22, 2015; Published online in Wiley Online Library (wileyonlinelibrary.com).
The authors aim to develop a community detection system without constraining users. This paper introduces a method to detect communities in which users are engaging in a conversation with each other, by an ambient sensing of body sways and their synchrony between users. We apply an improved ambient sensing chair, which had been developed in our previous study. The system employs wireless force sensors to measure the center of pressure. By using measured speaker/listener information and synchrony between any users' body sways, it is confirmed to detect the community information in experiment.
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