Learn More
To provide a means for recognition of affect from a distance, this paper analyzes the capability of gait to reveal a person's affective state. We address interindividual versus person-dependent recognition, recognition based on discrete affective states versus recognition based on affective dimensions, and efficient feature extraction with respect to(More)
—Body movements communicate affective expressions and, in recent years, computational models have been developed to recognize affective expressions from body movements or to generate movements for virtual agents or robots which convey affective expressions. This survey summarizes the state of the art on automatic recognition and generation of such(More)
Most of the systems for recognition of activities aim to identify a set of normal human activities. Data is either recorded by computer vision or sensor based networks. These systems may not work properly if an unusual event or abnormal activity occurs, especially ones that have not been encountered in the past. By definition, unusual events are mostly rare(More)
This paper describes recognition of emotions of an unkown person during natural walking. As gait data is redundant, high dimensional and variable, effective feature extraction is essential. A combination of two consecutive Principal Component Analyses (PCA) and Fourier Transformation is used for data reduction. Naive Bayes, 1-Nearest Neighbor and Support(More)
This study investigates recognition of affect in human walking as daily motion, in order to provide a means for affect recognition at distance. For this purpose, a data base of affective gait patterns from non-professional actors has been recorded with optical motion tracking. Principal Component Analysis (PCA), Kernel PCA (KPCA) and Linear Discriminant(More)
Detection of falls is very important from a health and safety perspective. However, falls occur rarely and infrequently, which leads to either limited or no training data and thus can severely impair the performance of supervised activity recognition algorithms. In this paper, we address the problem of identification of falls in the absence of training data(More)