Lean to Fly: Leaning-Based Embodied Flying can Improve Performance and User Experience in 3D Navigation

When users in virtual reality cannot physically walk and self-motions are instead only visually simulated, spatial updating is often impaired. In this paper, we report on a study that investigated if HeadJoystick, an embodied leaning-based flying interface, could improve performance in a 3D navigational search task that relies on maintaining situational awareness and spatial updating in VR. We compared it to Gamepad, a standard flying interface. For both interfaces, participants were seated on a swivel chair and controlled simulated rotations by physically rotating. They either leaned (forward/backward, right/left, up/down) or used the Gamepad thumbsticks for simulated translation. In a gamified 3D navigational search task, participants had to find eight balls within 5 min. Those balls were hidden amongst 16 randomly positioned boxes in a dark environment devoid of any landmarks. Compared to the Gamepad, participants collected more balls using the HeadJoystick. It also minimized the distance travelled, motion sickness, and mental task demand. Moreover, the HeadJoystick was rated better in terms of ease of use, controllability, learnability, overall usability, and self-motion perception. However, participants rated HeadJoystick could be more physically fatiguing after a long use. Overall, participants felt more engaged with HeadJoystick, enjoyed it more, and preferred it. Together, this provides evidence that leaning-based interfaces like HeadJoystick can provide an affordable and effective alternative for flying in VR and potentially telepresence drones.

Instrumental music versus Quran recitation: Which has the most influence on the accuracy and speed of work?

Many studies have proved that instrumental music, as well as Quran recitation, affects human factors such as mood, performance, and intelligence. However, to the best of our knowledge, there is no research on the effect of listening to Quran recitation before doing mental task on work productivity. This study examined the impact of instrumental music and Quran recitation on work productivity, especially on the accuracy and the speed of work. The study involved 20 people of productive age (15-64 years) as respondents. The true experimental one group pre-test and post-test design were used in analyzing the data. The data collected are the speed and the number of errors when conducting the Stroop task. The study found that instrumental music, as well as Quran recitation, has a significant contribution to the increase of the accuracy and the speed of work (p=0.00). The study also proved that Quran recitation has a significantly higher effect on work speed and accuracy compared to instrumental music treatment (p=0.00). The average speed of conducting the Stroop task with Quran recitation treatment is 7.67% higher than with the instrumental music. Furthermore, the average number of errors with Quran recitation treatment is significantly lower (e=2.93) than with the instrumental music treatment (e=4.68).

Empirical analysis for non-stationary signal de-noising, de-trending and discrimination applications

This dissertation focuses on the study and development of methods for empirical analysis of non-stationary signals in the context of de-noising, de-trending and discrimination applications. For this purpose, Empirical Mode Decomposition (EMD), which is a relatively new signal decomposition technique, is chosen as the starting point. EMD does not rely on any fixed basis, but instead defines a signal adaptive decomposition methodology. The use of EMD for signal de-noising and de-trending is demonstrated through formulation of a methodology for mental task classification using EEG signals. Furthermore, a methodology for analysis and classification of pathological speech signals is developed, whereby a high classification accuracy through use of meaningful instantaneous features is demonstrated. Following this, a novel modification of EMD, named Empirical Mode Decomposition-Modified Peak Selection (EMD-MPS), is proposed. EMD-MPS allows a time-scale based decomposition of signals, which is not possible using the original EMD algorithm. The EMD-MPS algorithm is defined, and its properties empirically established, thereby validating the expected behaviour of EMD-MPS. Importantly, EMD-MPS is shown to provide new insight into the decomposition behaviour of the original EMD algorithm. Also, a novel hierarchical decomposition methodology, which uses the time-scale based decomposition of EMD-MPS to divide a signal into selected frequency bands, is developed and illustrated using synthetic and real world signals. EMD-MPS is also used for time-scale based de-noising and de-trending of signals, first demonstrated using synthetic and real signals, and then validated by practical applications such as mental task classification and seizure detection. An empirical sparse dictionary learning framework based on EMD with application to signal classification is then proposed and developed in the dissertation. As part of this framework, a discriminative dictionary learning algorithm is developed, and characteristics of the empirical dictionary established. The utility of the proposed framework for signal classification is demonstrated using EEG signals. The proposed framework is then applied for automated seizure detection using long-term EEG recordings, and the results are used to discuss the potential and implications for patient specific dictionaries, as well as the associated advantages of the framework when using long-term data.

Empirical analysis for non-stationary signal de-noising, de-trending and discrimination applications

This dissertation focuses on the study and development of methods for empirical analysis of non-stationary signals in the context of de-noising, de-trending and discrimination applications. For this purpose, Empirical Mode Decomposition (EMD), which is a relatively new signal decomposition technique, is chosen as the starting point. EMD does not rely on any fixed basis, but instead defines a signal adaptive decomposition methodology. The use of EMD for signal de-noising and de-trending is demonstrated through formulation of a methodology for mental task classification using EEG signals. Furthermore, a methodology for analysis and classification of pathological speech signals is developed, whereby a high classification accuracy through use of meaningful instantaneous features is demonstrated. Following this, a novel modification of EMD, named Empirical Mode Decomposition-Modified Peak Selection (EMD-MPS), is proposed. EMD-MPS allows a time-scale based decomposition of signals, which is not possible using the original EMD algorithm. The EMD-MPS algorithm is defined, and its properties empirically established, thereby validating the expected behaviour of EMD-MPS. Importantly, EMD-MPS is shown to provide new insight into the decomposition behaviour of the original EMD algorithm. Also, a novel hierarchical decomposition methodology, which uses the time-scale based decomposition of EMD-MPS to divide a signal into selected frequency bands, is developed and illustrated using synthetic and real world signals. EMD-MPS is also used for time-scale based de-noising and de-trending of signals, first demonstrated using synthetic and real signals, and then validated by practical applications such as mental task classification and seizure detection. An empirical sparse dictionary learning framework based on EMD with application to signal classification is then proposed and developed in the dissertation. As part of this framework, a discriminative dictionary learning algorithm is developed, and characteristics of the empirical dictionary established. The utility of the proposed framework for signal classification is demonstrated using EEG signals. The proposed framework is then applied for automated seizure detection using long-term EEG recordings, and the results are used to discuss the potential and implications for patient specific dictionaries, as well as the associated advantages of the framework when using long-term data.

Development of a Novel Web Camera-Based Contact-Free Major Depressive Disorder Screening System Using Autonomic Nervous Responses Induced by a Mental Task and Its Clinical Application

BackgroundTo increase the consultation rate of potential major depressive disorder (MDD) patients, we developed a contact-type fingertip photoplethysmography-based MDD screening system. With the outbreak of SARS-CoV-2, we developed an alternative to contact-type fingertip photoplethysmography: a novel web camera-based contact-free MDD screening system (WCF-MSS) for non-contact measurement of autonomic transient responses induced by a mental task.MethodsThe WCF-MSS measures time-series interbeat intervals (IBI) by monitoring color tone changes in the facial region of interest induced by arterial pulsation using a web camera (1920 × 1080 pixels, 30 frames/s). Artifacts caused by body movements and head shakes are reduced. The WCF-MSS evaluates autonomic nervous activation from time-series IBI by calculating LF (0.04–0.15 Hz) components of heart rate variability (HRV) corresponding to sympathetic and parasympathetic nervous activity and HF (0.15–0.4 Hz) components equivalent to parasympathetic activities. The clinical test procedure comprises a pre-rest period (Pre-R; 140 s), mental task period (MT; 100 s), and post-rest period (Post-R; 120 s). The WCF-MSS uses logistic regression analysis to discriminate MDD patients from healthy volunteers via an optimal combination of four explanatory variables determined by a minimum redundancy maximum relevance algorithm: HF during MT (HFMT), the percentage change of LF from pre-rest to MT (%ΔLF(Pre–R⇒MT)), the percentage change of HF from pre-rest to MT (%ΔHF(Pre–R⇒MT)), and the percentage change of HF from MT to post-rest (%ΔHF(MT⇒Post–R)). To clinically test the WCF-MSS, 26 MDD patients (16 males and 10 females, 20–58 years) were recruited from BESLI Clinic in Tokyo, and 27 healthy volunteers (15 males and 12 females, 18–60 years) were recruited from Tokyo Metropolitan University and RICOH Company, Ltd. Electrocardiography was used to calculate HRV variables as references.ResultThe WCF-MSS achieved 73% sensitivity and 85% specificity on 5-fold cross-validation. IBI correlated significantly with IBI from reference electrocardiography (r = 0.97, p < 0.0001). Logit scores and subjective self-rating depression scale scores correlated significantly (r = 0.43, p < 0.05).ConclusionThe WCF-MSS seems a promising contact-free MDD screening apparatus. This method enables web camera built-in smartphones to be used as MDD screening systems.

Combined mental task and metaboreflex impair cerebral oxygenation in patients with type 2 diabetes mellitus

Cardiovascular regulation is altered by type 2 diabetes mellitus (DM2), producing an abnormal response to muscle metaboreflex. During physical exercise, cerebral blood flow is impaired in patients with DM2, and this phenomenon may reduce cerebral oxygenation (COX). We hypothesized that the simultaneous execution of a mental task (MT) and metaboreflex activation would reduce COX in patients with DM2. Thirteen individuals suffering from DM2 (6 women) and 13 normal age-matched controls (CTL, 6 women) participated in this study. They underwent five different tests, each lasting 12 min: postexercise muscle ischemia (PEMI) to activate the metaboreflex, control exercise recovery (CER), PEMI + MT, CER + MT, and MT alone. COX was evaluated using near-infrared spectroscopy with sensors applied to the forehead. Central hemodynamics was assessed using impedance cardiography. We found that when MT was superimposed on the PEMI-induced metaboreflex, patients with DM2 could not increase COX to the same extent reached by the CTL group (101.13% ± 1.08% vs. 104.23% ± 2.51%, P < 0.05). Moreover, patients with DM2 had higher mean blood pressure and systemic vascular resistance as well as lower stroke volume and cardiac output levels compared with the CTL group, throughout our experiments. It was concluded that patients with DM2 had reduced capacity to enhance COX when undertaking an MT during metaboreflex. Results also confirm that patients with DM2 had dysregulated hemodynamics during metaboreflex, with exaggerated blood pressure response and vasoconstriction. This may have implications for these patients’ lack of inclination to exercise.

Spectral Analysis of Electroencephalographic Data in Serious Games

In this paper, we describe an investigation of brain activity while playing a serious game (SG). A SG is focused on improving logical thinking, specifically on cognitive training of students in the field of basic logic gates, and we summarize SG description, design, and development. A method based on various signal processing techniques for evaluating electroencephalographic (EEG) data was implemented in the MATLAB. This assessment was based on the analysis of the spectrogram of particular brain activity. Changes in brain activity power at a characteristic frequency band during the gameplay were calculated from the spectrogram. The EEG of 21 respondents was measured. Based on the results, the respondents can be divided into three groups according to specific EEG activity changes during the gameplay compared to a relaxed state. The beta/alpha ratio, an indicator of brain employment to a mental task, was increased during gameplay in 18 of the 21 subjects. Our results reflected the sex of respondents, time of the game and the indicator, and whether the game was successfully completed.