Designing for Affective Augmentation: Assistive, harmful, or unfamiliar?

In what capacity are affective augmentations helpful to humans, and what risks (if any) do they pose? In this position paper, we outline three works on affective augmentation systems, where our studies suggest these systems have the ability to influence our cognitive, affective, and (social) bodily perceptions in perhaps unusual ways. We provide considerations on whether these systems, outside clinical settings, are assistive, harmful, or as of now largely unfamiliar to users

DeepSleep: A ballistocardiographic deep learning approach for classifying sleep stages

Current techniques for tracking sleep are either obtrusive (Polysomnography) or low in accuracy (wearables). In this early work, we model a sleep classification system using an unobtrusive Ballistocardiographic (BCG)-based heart sensor signal collected from a commercially available pressure-sensitive sensor sheet. We present DeepSleep, a hybrid deep neural network architecture comprising of CNN and LSTM layers. We further employed a 2-phase training strategy to build a pre-trained model and to tackle the limited dataset size. Our model results in a classification accuracy of 74%, 82%, 77% and 63% using Dozee BCG, MIT-BIH’s ECG, Dozee’s ECG and Fitbit’s PPG datasets, respectively. Furthermore, our model shows a positive correlation (r = 0.43) with the SATED perceived sleep quality scores. We show that BCG signals are effective for long-term sleep monitoring, but currently not suitable for medical diagnostic purposes

AN IMPROVEMENT OF CROSS ENTROPY THRESHOLDING FOR SKIN CANCER

Image processing procedures in medical diagnosis are used to improve diagnosis accuracy. An example of this is skin cancer detection using the thresholding approach. Thus, research studies involved in identification of inherited mutations predisposing family members to malignant melanoma have been performed in the Cancer Genetics field. Melanoma is one of the deadliest cancers, but could be cured when diagnosed early. A fundamental step in image processing is segmentation that includes thresholding, among others. Thresholding is based on finding the optimal thresholds value that partitions the image into multiple classes to be able to distinguish the objects from the background. The algorithm developed in this work is based on Minimum Cross Entropy Thresholding (MCET) method, using statistical distributions. We improved the previous work of Pal by using separately different statistical distributions (Gaussian, Lognormal and Gamma) instead of Poisson distribution. We applied our improved methods on bimodal skin cancer images and obtained promising experimental results. The resulting segmented skin cancer images, using Gamma distribution yielded better estimation of the optimal threshold than does the same MCET method with Lognormal, Gaussian and Poisson distribution

PHYTOCHEMICAL STUDY OF BIOACTIVE CONSTITUENTS FROM SATUREJA MONTANA L. GROWING IN EGYPT AND THEIR ANTIMICROBIAL AND ANTIOXIDANT ACTIVITIES

 Objective: This work aimed to investigate the lipid constituents and flavonoidal compounds of Satureja montana, in addition to evaluation of different extracts and/or isolated compounds as antimicrobials and antioxidants.Methods: The volatile and lipid constituents were extracted with n-hexane by partition from hydroalcoholic extract of S. montana L. aerial parts, after then were fractionated to unsaponifiable matters and fatty acid methyl esters which were identified by gas–liquid chromatography and/or gas chromatography–mass spectrometry. The phenolic constituents were isolated from the ethyl acetate fraction of the aqueous methanolic extract of the aerial parts of the plant. The antimicrobial activity of different extracts and the isolated compounds was evaluated against Gram-positive, Gram-negative bacteria, yeast, and fungus using a modified Kirby-Bauer disc diffusion method.Results: The identified compounds are luteolin-7-rhamnoside-4'-O-β-glucopyranoside (1), quercetin-3-O-α-L-rhamnopyranoside (2), quercetin- 7-O-glucopyranoside (3), luteolin-7-O-glucopyranoside (4), 5-hydroxy-6,7,8,4'-tetramethoxy flavone (5), gallic acid (6), 2,3-hexahydroxydiphenoyl 1-galloyl glucopyranoside (7), and quercetin (8). The structure of all isolated compounds was established using different chromatographic and spectroscopic measurements (PC, thin-layer chromatography, ultraviolet [UV], 1D, 2D-nuclear magnetic resonance, and MS). Compound-2 showed the highest antibacterial activity against all the tested microorganisms. Hydroalcoholic extract exhibited high antioxidant activity (87.7%). On the other hand, hexane fraction showed a low antioxidant activity (46.4%), in addition to the compound-8 showed the highest antioxidant activity (96.27%) in 2,2-diphenyl-1-picrylhydrazyl assay.Conclusion: It can be concluded that the hydroalcoholic extract of S. montana showed significant antimicrobial and antioxidant activity

Beyond Halo and Wedge: Visualizing out-of-view objects on head-mounted virtual and augmented reality devices

Head-mounted devices (HMDs) for Virtual and Augmented Reality (VR/AR) enable us to alter our visual perception of the world. However, current devices suffer from a limited field of view (FOV), which becomes problematic when users need to locate out of view objects (e.g., locating points-of-interest during sightseeing). To address this, we developed and evaluated in two studies HaloVR, WedgeVR, HaloAR and WedgeAR, which are inspired by usable 2D off-screen object visualization techniques (Halo, Wedge). While our techniques resulted in overall high usability, we found the choice of AR or VR impacts mean search time (VR: 2.25s, AR: 3.92s) and mean direction estimation error (VR: 21.85°, AR: 32.91°). Moreover, while adding more out-of-view objects significantly affects search time across VR and AR, direction estimation performance remains unaffected. We provide implications and discuss the challenges of designing for VR and AR HMDs

RadialLight: Exploring radial peripheral LEDs for directional cues in head-mounted displays

Current head-mounted displays (HMDs) for Virtual Reality (VR) and Augmented Reality (AR) have a limited field-of-view (FOV). This limited FOV further decreases the already restricted human visual range and amplifies the problem of objects going out of view. Therefore, we explore the utility of augmenting HMDs with RadialLight, a peripheral light display implemented as 18 radially positioned LEDs around each eye to cue direction towards out-of-view objects. We first investigated direction estimation accuracy of multi-colored cues presented on one versus two eyes. We then evaluated direction estimation accuracy and search time performance for locating out-of-view objects in two representative 360° video VR scenarios. Key findings show that participants could not distinguish between LED cues presented to one or both eyes simultaneously, participants estimated LED cue direction within a maximum 11.8° average deviation, and out-of-view objects in less distracting scenarios were selected faster. Furthermore, we provide implications for building peripheral HMDs

CorrFeat: Correlation-based feature extraction algorithm using skin conductance and pupil diameter for emotion recognition

To recognize emotions using less obtrusive wearable sensors, we present a novel emotion recognition method that uses only pupil diameter (PD) and skin conductance (SC). Psychological studies show that these two signals are related to the attention level of humans exposed to visual stimuli. Based on this, we propose a feature extraction algorithm that extract correlation-based features for participants watching the same video clip. To boost performance given limited data, we implement a learning system without a deep architecture to classify arousal and valence. Our method outperforms not only state-of-art approaches, but also widely-used traditional and deep learning methods