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The aim of the present paper was to study heart rate changes during a video stimulation depicting two actors (male and female) producing dynamic facial expressions of happiness, sadness, and a neutral expression. We measured ballistocardiographic emotion-related heart rate responses with an unobtrusive measurement device called the EMFi chair. Ratings of subjective responses to the video stimuli were also collected. The results showed that the video stimuli evoked significantly different ratings of emotional valence and arousal. Heart rate decelerated in response to all stimuli and the deceleration was the strongest during negative stimulation. Furthermore, stimuli from the male actor evoked significantly larger arousal ratings and heart rate responses than the stimuli from the female actor. The results also showed differential responding between female and male participants. The present results support the hypothesis that heart rate decelerates in response to films depicting dynamic negative facial expressions. The present results also support the idea that the EMFi chair can be used to perceive emotional responses from people while they are interacting with technology.

As we know, singular value decomposition (SVD) is designed for computing singular values (SVs) of a matrix. Then, if it is used for finding SVs of an -by-1 or 1-by- array with elements representing samples of a signal, it will return only one singular value that is not enough to express the whole signal. To overcome this problem, we designed a new kind of the feature extraction method which we call ''time-frequency moments singular value decomposition (TFM-SVD).'' In this new method, we use statistical features of time series as well as frequency series (Fourier transform of the signal). This information is then extracted into a certain matrix with a fixed structure and the SVs of that matrix are sought. This transform can be used as a preprocessing stage in pattern clustering methods. The results in using it indicate that the performance of a combined system including this transform and classifiers is comparable with the performance of using other feature extraction methods such as wavelet transforms. To evaluate TFM-SVD, we applied this new method and artificial neural networks (ANNs) for ballistocardiogram (BCG) data clustering to look for probable heart disease of six test subjects. BCG from the test subjects was recorded using a chair-like ballistocardiograph, developed in our project. This kind of device combined with automated recording and analysis would be suitable for use in many places, such as home, office, and so forth. The results show that the method has high performance and it is almost insensitive to BCG waveform latency or nonlinear disturbance.