Instead of predicting just one emotion for activity (e.g., video watching), fine-grained recognition enables more temporally precise recognition. Previous works on require segment-by-segment, labels to train the algorithm. However, experiments collect these are costly and time-consuming compared with only collecting label after user watched that stimulus (i.e., post-stimuli labels). To recogniz...

This paper proposes the design of a rehabilitation robot for upper limb based on physiological electrical signals. The system uses surface electromyography (sEMG) and electroencephalography (EEG) signals to control robot's movements. has modular structure consisting mechanical hand, system, data acquisition system. STM32 microcontroller Bluetooth module real-time transmission control. collects ...

Primary Area Surrogate measures of safety ABSTRACT This paper describes the development of an in-vehicle measurement system that monitors the physiological signals (i.e., heart rate, heart rate variation, breathing and eye brinking) of drivers. These physiological signals will be utilized to detect the onset of driver fatigue, crucial for timely applying drowsiness countermeasures. Fatigue driv...

Nonstationary and nonlinear signals generated by living complex systems defy traditional mechanistic approaches, which are based on homeostasis. Previous our studies have shown that the evaluation of the interactions of physiological signals by using special analysis methods is suitable for observation of physiological processes. It is demonstrated the possibility of using deep physiological mo...

Recognizing and accurately classifying human emotion is a complex challenging task. Recently, great attention has been paid to the recognition methods using three different approaches: based on non-physiological signals (like speech facial expression), physiological signals, or hybrid approaches. Non-physiological are easily controlled by individual, so these approaches have downsides in real w...

Physiological and affective computing propose methods to improve human–machine interactions by adapting machines to the users’ states. Recently, social signal processing (SSP) has proposed to apply similar methods to human–human interactions with the hope of better understanding and modeling social interactions. Most of the social signals employed are facial expressions, body movements and spee...

Recently, emotion recognition systems based on physiological signals have introduced in humancomputer interaction researches. The aim of this study is to classify seven emotions (happiness, sadness, anger, fear, disgust, surprise, and stress) by machine learning algorithms using physiological signals. 12 college students participated in this experiment over 10 times. Total 70 emotional stimuli ...

Tumorigenesis occurs when the mechanisms involved in the control of tissue homeostasis are disrupted and cells stop responding to physiological signals. Therefore, genes capable of desensitizing tumoral cells from physiological signals may provide a selective advantage within the tumoral mass and influence the outcome of the disease. We undertook a large scale genetic screen to identify genes a...

In analyzing physiological signals related to speech, it is neces sary to average several repetitions in order to improve the Signal to Noise Ratio. However, in a recent experiment, considerable differen ces were found in the articulation rate of repeated realizations of a medium length utterance, especially for untrained subjects. This makes averaging of related physiological signals a non-tri...