Stress Recognition Using Photoplethysmogram Signal
This study proposed a novel method of stress recognition using photopletysmogram (PPG) signal. PPG devices are now widely used because it is convenient, low powered, cheap and also easy to handle due to its small size. A total of 5 subjects were involved in this study. The PPG signals were taken in resting condition using pulse oximetry. The subject then goes through a stressor test in order to record the physiological changes. The data were collected before and after the test was conducted and later extracted. These samples were then categorised using classification techniques to differentiate between normal and stress condition. Based on the experimentation results, the systolic peak value differences of normal and stress conditions are evident.Therefore, the outcome of this study suggest the reliability of implementing PPG signal for stress recognition.
Driver stress level detection using HRV analysis
This paper intends to investigate stress level detection of a driver during real world driving experiment. This detection is based on heart rate variability (HRV) analysis which is derived from ECG signal and reflects autonomic nervous system state of the human body. The alteration of autonomic nervous system predicts the stress level of drivers during driving operation and permits a safe driving by the possibility of an early warning. This stress, taking place during driving, is caused by diverse factors such as changing mood, bio rhythm, fatigue, boredom or disease which can prevent the driver from reaching inappropriate state for driving. In our study, the ECG signal of the driver is extracted and preprocessed in order to perform the HRV analysis. This analysis is accomplished using one of the domain analysis approach such as time, frequency, time-frequency or non-linear methods including Wavelet and STFT. After HRV analysis, several parameters are extracted to build a vector of features for the classification phase. Our experimentation is performed with data issued from 16 different subjects from the Stress Recognition in Automobile Driver database (DRIVEDB). Several classification techniques were investigated including support vector machine with radial basis function (SVM-RBF) kernel, K nearest neighbor (KNN), and radial basis function (RBF) classifiers. Our results indicate that stress detection could be predicted with an accuracy of 83% using SVM-RBF classifier. This also points out the robustness of ECG biometric as an accurate physiological indicator of a driver state.
The Intuitive Physics of the Equilibrium of the Lever and of the Hydraulic Pressures: Implications for the Teaching of Elementary Physics.
The research field of intuitive physics focuses on discrepancies between 
theoretical and intuitive physical knowledge. Consideration of these 
discrepancies can help in the teaching of elementary physics. However, 
evidence shows that theoretical and intuitive physical knowledge may also 
be congruent. Physics teaching could further benefit from understanding the 
reasons for this congruence. The present study explored these reasons by 
investigating the intuitive physics of the equilibrium of the lever and of the 
hydraulic pressures. It was found that the intuitive-physics law of the lever 
was multiplicative for all participants while the intuitive-physics law of the 
hydraulic pressures differed among participants. Since these laws are 
equally simple and the layman probably has had extensive experience with 
the lever and scarce or no experience with the hydraulic lift, these findings 
support the general hypothesis that physical laws and corresponding 
intuitive-physics laws are congruent when people have had experience with 
the respective phenomena. The results and theoretical considerations suggest 
two strategic principles for teaching elementary physics.