Humanoid robots have - by definition - some level of human-likeness in body form. According to previous research in HRI, this leads to a higher expectation of human-like behavior. Nevertheless, human-likeness is not an easy notion to define for motion even in a task as straight forward as real-time motion copying (the shadowing task) as this paper will try to argue. The main hypothesis of this paper is that subjective evaluation of robot's motion's human-likeness depends not only on the objective similarity between robot's motion and human motion but also on the interaction context (e.g, whether or not the human have previously engaged in mutual or back imitation with the robot). Moreover, the paper proposes two features of motion similarity that affect subjective evaluation of human-likeness and accuracy in the shadowing task and shows that human-likeness is a different attribution dimension form both accuracy and humanness (measured using human-nature traits). The paper reports a controlled user study involving 36 participants and 108 HRI sessions to support these claims.

Our goal is to make a system to detect the times at which one almost laughed but he or she did not show their laughter on his/her face. We define this kind of laughter as hidden laughter. To accomplish this goal, we first tried making decision trees to detect one's amusement, the input data of which were physiological indices. We used 10-fold cross validation to evaluate the trees, and their accuracy was more than 70%. In addition, we investigated the effect of cultural background on the accuracy.

In this paper, a new technique for human identification task based on heart sound signals has been proposed. It utilizes a feature level fusion technique based on canonical correlation analysis. For this purpose a robust pre-processing scheme based on the wavelet analysis of the heart sounds is introduced. Then, three feature vectors are extracted depending on the cepstral coefficients of different frequency scale representation of the heart sound namely; the mel, bark, and linear scales. Among the investigated feature extraction methods, experimental results show that the mel-scale is the best with 94.4% correct identification rate. Using a hybrid technique combining MFCC and DWT, a new feature vector is extracted improving the system's performance up to 95.12%. Finally, canonical correlation analysis is applied for feature fusion. This improves the performance of the proposed system up to 99.5%. The experimental results show significant improvements in the performance of the proposed system over methods adopting single feature extraction.

Due to the great advances in biomedical digital signal processing, new biometric traits have showed noticeable improvements in authentication systems. Recently, the ElectroCardioGram (ECG) and the PhonoCardioGraph (PCG) have been proposed as novel biometrics. This paper aims to review the previous studies related to the usage of the ECG and PCG signals in human recognition. In addition, we discuss briefly the most important techniques and methodologies used by researchers in the preprocessing, feature extraction and classification of the ECG and PCG signals. At the end, we introduce some future considerations that can be applied in this topic such as: the fusion between different techniques previously used, use both ECG and PCG signals in a multimodal biometric authentication system and building a prototype system for real-time authentication.