Heart rate variability (HRV) is a well-accepted indicator for neural regulatory mechanisms in cardiovascular circulation. Its spectrum analysis provides the powerful means of observing the modulation between sympathetic and parasympathetic nervous system. The timescale of HRV is limited by discrete beat-to-beat time intervals; therefore, the exploration region of frequency band of HRV spectrum is relatively narrow. It had been proved that pulse rate variability (PRV) is a surrogate measurement of HRV in most of the circumstances. Moreover, arterial pulse wave contains small oscillations resulting from complex regulation of cardiac pumping function and vascular tone at higher frequency range. This study proposed a novel instantaneous PRV (iPRV) measurement based on Hilbert-Huang transform. Fifteen healthy subjects participated in this study and received continuous blood pressure wave recording in supine and passive head-up tilt. The result showed that the very-high-frequency band (0.4-0.9 Hz) varied during head-up tilt and had strong correlation (r = 0.77) with high-frequency band and medium correlation (r = 0.643) with baroreflex sensitivity. The very-high-frequency band of iPRV helps for the exploration of non-stationary autoregulation and provides the non-stationary spectral evaluation of HRV without distortion or information loss.

Based on the flipped‐classroom model and the potential motivational and instructional benefits of digital games, we describe a flipped game‐based learning (FGBL) strategy focused on preclass and overall learning outcomes. A secondary goal is to determine the effects, if any, of the classroom aspects of the FGBL strategy on learning efficiency. Our experiments involved 2 commercial games featuring physical motion concepts: Ballance (Newton's law of motion) and Angry Birds (mechanical energy conservation). We randomly assigned 87 8th‐grade students to game instruction (digital game before class and lecture‐based instruction in class), FGBL strategy (digital game before class and cooperative learning in the form of group discussion and practice in class), or lecture‐based instruction groups (no gameplay). Results indicate that the digital games exerted a positive effect on preclass learning outcomes and that FGBL‐strategy students achieved better overall learning outcomes than their lecture‐based peers. Our observation of similar overall outcomes between the cooperative learning and lecture‐based groups suggests a need to provide additional teaching materials or technical support when introducing video games to cooperative classroom learning activities.

BackgroundEmotion recognition technology plays the essential role of enhancement in Human-Computer Interaction (HCI). In recent years, a novel approach for emotion recognition has been reported, which is by keystroke dynamics. This approach can be considered to be rather desirable in HCI because the data used is rather non-intrusive and easy to obtain. However, there were only limited investigations about the phenomenon itself in previous studies. This study aims to examine the source of variance in keystroke typing patterns caused by emotions.MethodsA controlled experiment to collect subjects’ keystroke data in different emotional states induced by International Affective Picture System (IAPS) was conducted. Two-way Valence (3) × Arousal (3) ANOVAs were used to examine the collected dataset.ResultsThe results of the experiment indicate that the effect of emotion is significant (p < .001) in the keystroke duration, keystroke latency, and accuracy rate of the keyboard typing. However, the size of the emotional effect is small, compare to the individual variability.ConclusionsOur findings support the conclusion that the keystroke duration, keystroke latency, and also the accuracy rate of typing, are influenced by emotional states. Notably, the finding about the size of effect suggests that the accuracy rate of the emotion recognition could be further improved if personalized models are utilized. On the other hand, the finding also provides an explanation of why real-world applications which authenticate the identity of users by monitoring keystrokes may not be interfered by the emotional states of users. The experiment was conducted using standard instruments and hence is expected to be highly reproducible.

BackgroundPulse rate (PR) indicates heart beat rhythm and contains various intrinsic characteristics of peripheral regulation. Pulse rate variability (PRV) is a reliable method to assess autonomic nervous system function quantitatively as an effective alternative to heart rate variability. However, the frequency range of PRV is limited by the temporal resolution of PR based on heart rate and it is further restricted the exploration of optimal autoregulation frequency based on spectral analysis.MethodsRecently, a new novel method, called instantaneous PRV (iPRV), was proposed. iPRV breaks the limitation of temporal resolution and extends the frequency band. Moreover, iPRV provides a new frequency band, called very high frequency band (VHF; 0.4-0.9 Hz).ResultsThe results showed that the VHF indicated the influences of respiratory maneuvers (paced respiration at 6-cycle and 30-cycle) and the nonstationary condition (head-up tilt; HUT).ConclusionsVHF is as a potential indication of autoregulation in higher frequency range and with peripheral regulation. It helps for the frequency exploration of cardiovascular autoregulation.

In recent years, a novel approach for emotion recognition has been reported, which is by keystroke dynamics. The advantages of using this approach are that the data used is rather non-intrusive and easy to obtain. However, there were only limited investigations about the phenomenon itself in previous studies. Hence, this study aimed to examine the source of variance in keyboard typing patterns caused by emotions. A controlled experiment to collect subjects’ keystroke data in different emotional states induced by International Affective Digitized Sounds (IADS) was conducted. Two-way Valence (3) x Arousal (3) ANOVAs was used to examine the collected dataset. The results of the experiment indicate that the effect of arousal is significant in keystroke duration (p < .05), keystroke latency (p < .01), but not in the accuracy rate of keyboard typing. The size of the emotional effect is small, compared to the individual variability. Our findings support the conclusion that the keystroke duration and latency are influenced by arousal. The finding about the size of the effect suggests that the accuracy rate of emotion recognition technology could be further improved if personalized models are utilized. Notably, the experiment was conducted using standard instruments and hence is expected to be highly reproducible.

Dynamic regulation of cerebral circulation involves complex interaction between cardiovascular, respiratory, and autonomic nervous systems. Evaluating cerebral hemodynamics by using traditional statistic- and linear-based methods would underestimate or miss important information. Complementary ensemble empirical mode decomposition (CEEMD) has great capability of adaptive feature extraction from non-linear and non-stationary data without distortion. This study applied CEEMD for assessment of cerebral hemodynamics in response to physiologic challenges including paced 6-cycle breathing, hyperventilation, 7% CO2 breathing and head-up tilting test in twelve healthy subjects. Intrinsic mode functions (IMFs) were extracted from arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) signals, and was quantified by logarithmic averaged period and logarithmic energy density. The IMFs were able to show characteristics of ABP and CBFV waveform morphology in beat-to-beat timescale and in long-term trend scale. The changes in averaged period and energy density derived from IMFs were helpful for qualitative and quantitative assessment of ABP and CBFV responses to physiologic challenges. CEEMD is a promising method for assessing non-stationary components of systemic and cerebral hemodynamics.

No abstract

No abstract